diff --git a/DAP-main-2/LICENSE b/DAP-main-2/LICENSE deleted file mode 100644 index 330c80c4dc8a58fb0ddb3a07b60d7c2ec43d7c9f..0000000000000000000000000000000000000000 --- a/DAP-main-2/LICENSE +++ /dev/null @@ -1,21 +0,0 @@ -MIT License - -Copyright (c) 2025 Insta360 Research Team - -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: - -The above copyright notice and this permission notice shall be included in all -copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -SOFTWARE. \ No newline at end of file diff --git a/DAP-main-2/README.md b/DAP-main-2/README.md deleted file mode 100644 index 967c085833f033e81b926a962e50db8e5fd7e17d..0000000000000000000000000000000000000000 --- a/DAP-main-2/README.md +++ /dev/null @@ -1,103 +0,0 @@ -

-Depth Any Panoramas:
-A Foundation Model for Panoramic Depth Estimation -

- - -

- Xin Lin · - Meixi Song · - Dizhe Zhang · - Wenxuan Lu · - Haodong Li -
- Bo Du · - Ming-Hsuan Yang · - Truong Nguyen · - Lu Qi -

- - - -

- arXiv - Project Page - - -

- -![teaser](assets/depth_teaser2_00.png) - - - -## 🔨 Installation - -Clone the repo first: - -```Bash -git clone https://github.com/Insta360-Research-Team/DAP -cd DAP -``` - -(Optional) Create a fresh conda env: - -```Bash -conda create -n dap python=3.12 -conda activate dap -``` - -Install necessary packages (torch > 2): - -```Bash -# pytorch (select correct CUDA version, we test our code on torch==2.7.1 and torchvision==0.22.1) -pip install torch==2.7.1 torchvision==0.22.1 - -# other dependencies -pip install -r requirements.txt -``` - -## 🖼️ Dataset - -The training dataset will be open soon. - - -## 🤝 Pre-trained model - -Please download the pretrained model: https://huggingface.co/Insta360-Research/DAP-weights - - -## 📒 Inference - -```Bash -python test/infer.py -``` - - -## 🚀 Evaluation - - -```Bash -python test/eval.py -``` - - - - -## 🤝 Acknowledgement - -We appreciate the open source of the following projects: - -* [PanDA](https://caozidong.github.io/PanDA_Depth/) -* [Depth-Anything-V2](https://github.com/DepthAnything/Depth-Anything-V2) - - -## Citation -``` -@article{lin2025dap, - title={Depth Any Panoramas: A Foundation Model for Panoramic Depth Estimation}, - author={Lin, Xin and Song, Meixi and Zhang, Dizhe and Lu, Wenxuan and Li, Haodong and Du, Bo and Yang, Ming-Hsuan and Nguyen, Truong and Qi, Lu}, - journal={arXiv}, - year={2025} - } -``` - diff --git a/DAP-main-2/__pycache__/depth_anything_utils.cpython-310.pyc b/DAP-main-2/__pycache__/depth_anything_utils.cpython-310.pyc deleted file mode 100644 index 38477b23bd45ad7b155363d61803d1d759b6af34..0000000000000000000000000000000000000000 Binary files a/DAP-main-2/__pycache__/depth_anything_utils.cpython-310.pyc and /dev/null differ diff --git a/DAP-main-2/assets/depth_teaser2.pdf b/DAP-main-2/assets/depth_teaser2.pdf deleted file mode 100644 index 3bbbe179122de4898817b7c4eb6905174668b307..0000000000000000000000000000000000000000 --- a/DAP-main-2/assets/depth_teaser2.pdf +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:28e8d4fecc3a5bd905ffead457d35f3602c98e78976c75e5da6317286ae4a385 -size 1872942 diff --git a/DAP-main-2/assets/depth_teaser2_00.png b/DAP-main-2/assets/depth_teaser2_00.png deleted file mode 100644 index e0ae839039f0e1e3a16d03e96132d11d0ea2b0f7..0000000000000000000000000000000000000000 --- a/DAP-main-2/assets/depth_teaser2_00.png +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:a51c421ece1bb8c113a0cd9cf299b0af3588176bfe5fb2ddb8dca2e5d2c4797e -size 7050781 diff --git a/DAP-main-2/assets/teaser.jpg b/DAP-main-2/assets/teaser.jpg deleted file mode 100644 index 4b539a186f55dd46d7f4556cd2e1a3bec0802cc6..0000000000000000000000000000000000000000 --- a/DAP-main-2/assets/teaser.jpg +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:eb088c13826142968a5ba8276fa323bdfb0eba4d0d640e9f0a680a2c8099a479 -size 9126933 diff --git a/DAP-main-2/config/infer.yaml b/DAP-main-2/config/infer.yaml deleted file mode 100644 index 212c8298ee433eb2c82e3a63ca4fc998cfa1e6b1..0000000000000000000000000000000000000000 --- a/DAP-main-2/config/infer.yaml +++ /dev/null @@ -1,19 +0,0 @@ -model: - name: dap - args: - midas_model_type: vitl - fine_tune_type: hypersim - min_depth: 0.01 - max_depth: 1.0 - train_decoder: True - -median_align: False -load_weights_dir: /home/tione/notebook/home/songmeixi_insta360.com/depth/panda_orgindual/ckpt_save/1111/trainw1_2_dualw1_2/weights_0 -input: - height: 512 - width: 1024 -inference: - batch_size: 1 - num_workers: 1 - save_colormap: True - colormap_type: jet diff --git a/DAP-main-2/config/test.yaml b/DAP-main-2/config/test.yaml deleted file mode 100644 index 84f7fdb86bce947cb46bd7692677b14b9771db2e..0000000000000000000000000000000000000000 --- a/DAP-main-2/config/test.yaml +++ /dev/null @@ -1,71 +0,0 @@ -test_dataset_1: - name: stanford2d3d - root_path: /home/tione/notebook/home/wenxuan/PanDA/data/stanford2d3d - list_path: datasets/stanford2d3d_test.txt - args: - height: 512 - width: 1024 - repeat: 1 - augment_color: False - augment_flip: False - augment_rotation: False - batch_size: 32 - num_workers: 64 - -# test_dataset_1: -# name: insta23k -# root_path: /home/tione/notebook/nfs/MLUAV_Data -# list_path: datasets/instadata_list_test.txt -# args: -# height: 512 -# width: 1024 -# repeat: 1 -# augment_color: False -# augment_flip: False -# augment_rotation: False -# batch_size: 32 -# num_workers: 64 - -# test_dataset_1: -# name: deep360 -# root_path: /home/tione/notebook/home/wenxuan/PanDA/data/Deep360 -# list_path: datasets/deep360_test_final.txt -# args: -# height: 512 -# width: 1024 -# repeat: 1 -# augment_color: False -# augment_flip: False -# augment_rotation: False -# batch_size: 32 -# num_workers: 24 - -# test_dataset_1: -# name: m3d -# root_path: /home/tione/notebook/home/wenxuan/PanDA/data/M3D -# list_path: datasets/m3d_test.txt -# args: -# height: 512 -# width: 1024 -# repeat: 1 -# augment_color: False -# augment_flip: False -# augment_rotation: False -# batch_size: 32 -# num_workers: 24 - - -model: - name: dap - args: - midas_model_type: vitl - fine_tune_type: - min_depth: 0.001 - max_depth: 1.0 - train_decoder: True - -median_align: False - - - -load_weights_dir: /home/tione/notebook/home/songmeixi_insta360.com/depth/panda_orgindual/ckpt_save/1111/trainw1_2_dualw1_2/weights_0 diff --git a/DAP-main-2/datasets/M3D.py b/DAP-main-2/datasets/M3D.py deleted file mode 100644 index 415785d80790a3a6858dbdcdf1acfc81721652da..0000000000000000000000000000000000000000 --- a/DAP-main-2/datasets/M3D.py +++ /dev/null @@ -1,135 +0,0 @@ -from __future__ import print_function -import os -import cv2 -import numpy as np -import random -import pyexr -import torch -from torch.utils import data -from torchvision import transforms -from torchvision.transforms import Compose - -from PIL import Image, ImageOps, ImageFilter -import torch.nn.functional as F -from einops import rearrange - -def read_list(list_file): - rgb_depth_list = [] - with open(list_file) as f: - lines = f.readlines() - for line in lines: - rgb_depth_list.append(line.strip().split(" ")) - return rgb_depth_list - -class M3D(data.Dataset): - """The M3D Dataset""" - - def __init__(self, root_dir, list_file, height=504, width=1008, color_augmentation=True, - LR_filp_augmentation=True, yaw_rotation_augmentation=True, repeat=1, is_training=False): - """ - Args: - root_dir (string): Directory of the Stanford2D3D Dataset. - list_file (string): Path to the txt file contain the list of image and depth files. - height, width: input size. - disable_color_augmentation, disable_LR_filp_augmentation, - disable_yaw_rotation_augmentation: augmentation options. - is_training (bool): True if the dataset is the training set. - """ - self.root_dir = root_dir - - self.w = width - self.h = height - - self.max_depth_meters = 100.0 - self.min_depth_meters = 0.01 - - self.color_augmentation = color_augmentation - self.LR_filp_augmentation = LR_filp_augmentation - self.yaw_rotation_augmentation = yaw_rotation_augmentation - - if self.color_augmentation: - try: - self.brightness = (0.8, 1.2) - self.contrast = (0.8, 1.2) - self.saturation = (0.8, 1.2) - self.hue = (-0.1, 0.1) - self.color_aug= transforms.ColorJitter( - self.brightness, self.contrast, self.saturation, self.hue) - except TypeError: - self.brightness = 0.2 - self.contrast = 0.2 - self.saturation = 0.2 - self.hue = 0.1 - self.color_aug = transforms.ColorJitter( - self.brightness, self.contrast, self.saturation, self.hue) - - self.is_training = is_training - - self.to_tensor = transforms.ToTensor() - self.normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) - - self.rgb_depth_list = read_list(list_file) - - def __len__(self): - return len(self.rgb_depth_list) - - def __getitem__(self, idx): - - # Read and process the image file - rgb_name = os.path.join(self.root_dir, self.rgb_depth_list[idx][0]) - rgb = cv2.imread(rgb_name) - # cv2.imwrite('label_rgb.jpg', rgb) - rgb = cv2.cvtColor(rgb, cv2.COLOR_BGR2RGB) - rgb = cv2.resize(rgb, dsize=(self.w, self.h), interpolation=cv2.INTER_CUBIC) - - # Read and process the depth file - depth_name = os.path.join(self.root_dir, self.rgb_depth_list[idx][1]) - # gt_depth = cv2.imread(depth_name, -1) - # gt_depth = cv2.resize(gt_depth, dsize=(self.w, self.h), interpolation=cv2.INTER_NEAREST) - # gt_depth = gt_depth.astype(float)/4000 - # gt_depth[gt_depth > self.max_depth_meters+1] = self.max_depth_meters + 1 - - gt_depth = pyexr.open(depth_name).get() - gt_depth = gt_depth[:, :, 0] - gt_depth = cv2.resize(gt_depth, dsize=(self.w, self.h), interpolation=cv2.INTER_NEAREST) - gt_depth[gt_depth > self.max_depth_meters+1] = self.max_depth_meters + 1 - - if self.is_training and self.yaw_rotation_augmentation: - # random yaw rotation - roll_idx = random.randint(0, self.w) - rgb = np.roll(rgb, roll_idx, 1) - gt_depth = np.roll(gt_depth, roll_idx, 1) - - if self.is_training and self.LR_filp_augmentation and random.random() > 0.5: - rgb = cv2.flip(rgb, 1) - gt_depth = cv2.flip(gt_depth, 1) - - if self.is_training and self.color_augmentation and random.random() > 0.5: - aug_rgb = np.asarray(self.color_aug(transforms.ToPILImage()(rgb))) - else: - aug_rgb = rgb.copy() - - aug_rgb = self.to_tensor(aug_rgb.copy()) - - gt_depth = torch.from_numpy(np.expand_dims(gt_depth, axis=0)).to(torch.float32) - - val_mask = ((gt_depth > 0) & (gt_depth <= self.max_depth_meters)& ~torch.isnan(gt_depth)) - - # _min, _max = torch.quantile(gt_depth[val_mask], torch.tensor([0.02, 1 - 0.02]),) - # gt_depth = gt_depth / 2560.0 - gt_depth_norm = gt_depth / 100.0 - gt_depth_norm = torch.clip(gt_depth_norm, 0.001, 1.0) - - # print(gt_depth_norm.shape) - # Conduct output - inputs = {} - - inputs["rgb"] = self.normalize(aug_rgb) - inputs["gt_depth"] = gt_depth_norm - inputs["val_mask"] = val_mask # 合法区域，不是全true，真把不能用的区域划出来了；其他参与训练的数据集是全true的（除了投影数据集） - inputs["mask_100"] = (gt_depth > 0) & (gt_depth <= 100) - # 对于这个数据集，mask_100设定为全true的，因为求不出来。大于100米的深度gt也有可能是玻璃镜子等物体，反正这个数据集也不参加训练 - - # 这个数据集中，模型预测的mask100应该是被val_mask涵盖的，所以mask100理论上没有影响 - # val_mask控制计算指标的区域 - return inputs \ No newline at end of file diff --git a/DAP-main-2/datasets/__init__.py b/DAP-main-2/datasets/__init__.py deleted file mode 100644 index af2e8079c14221bfd0d2156b237d1fcbdd82a141..0000000000000000000000000000000000000000 --- a/DAP-main-2/datasets/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -from .stanford2d3d import Stanford2D3D -from .deep360 import Deep360 -from .insta23k import Insta23k -from .M3D import M3D diff --git a/DAP-main-2/datasets/__pycache__/M3D.cpython-310.pyc b/DAP-main-2/datasets/__pycache__/M3D.cpython-310.pyc deleted file mode 100644 index 94b219464cfdac1f3611c7725ffc7c2dbc86e4a2..0000000000000000000000000000000000000000 Binary files a/DAP-main-2/datasets/__pycache__/M3D.cpython-310.pyc and /dev/null differ diff --git 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100644 index 7b9f4d70b42188a2e48de97bb72dd70633e4ea79..0000000000000000000000000000000000000000 --- a/DAP-main-2/datasets/deep360.py +++ /dev/null @@ -1,184 +0,0 @@ -from __future__ import print_function -import os -import cv2 -import numpy as np -import random - -import torch -from torch.utils import data -from torchvision import transforms -import torch.nn.functional as F - -def cassini2Equirec(cassini): - if cassini.ndim == 2: - cassini = np.expand_dims(cassini, axis=-1) - source_image = torch.FloatTensor(cassini).unsqueeze(0).transpose(1, 3).transpose(2, 3) - elif cassini.ndim == 3: - source_image = torch.FloatTensor(cassini).unsqueeze(0).transpose(1, 3).transpose(2, 3) - else: - source_image = cassini - - erp_h = source_image.shape[-1] - erp_w = source_image.shape[-2] - - theta_erp_start = np.pi - (np.pi / erp_w) - theta_erp_end = -np.pi - theta_erp_step = 2 * np.pi / erp_w - theta_erp_range = np.arange(theta_erp_start, theta_erp_end, -theta_erp_step) - theta_erp_map = np.array([theta_erp_range for i in range(erp_h)]).astype(np.float32) - - phi_erp_start = 0.5 * np.pi - (0.5 * np.pi / erp_h) - phi_erp_end = -0.5 * np.pi - phi_erp_step = np.pi / erp_h - phi_erp_range = np.arange(phi_erp_start, phi_erp_end, -phi_erp_step) - phi_erp_map = np.array([phi_erp_range for j in range(erp_w)]).astype(np.float32).T - - theta_cassini_map = np.arctan2(np.tan(phi_erp_map), np.cos(theta_erp_map)) - phi_cassini_map = np.arcsin(np.cos(phi_erp_map) * np.sin(theta_erp_map)) - - grid_x = torch.FloatTensor(np.clip(-phi_cassini_map / (0.5 * np.pi), -1, 1)).unsqueeze(-1) - grid_y = torch.FloatTensor(np.clip(-theta_cassini_map / np.pi, -1, 1)).unsqueeze(-1) - grid = torch.cat([grid_x, grid_y], dim=-1).unsqueeze(0).repeat_interleave(source_image.shape[0], dim=0) - - sampled_image = F.grid_sample(source_image, grid, mode='bilinear', align_corners=True, padding_mode='border') # 1, ch, self.output_h, self.output_w - - if cassini.ndim == 3: - erp = sampled_image.transpose(1, 3).transpose(1, 2).data.numpy()[0].astype(cassini.dtype) - return erp.squeeze() - else: - erp = sampled_image.numpy() - return erp.squeeze(1) - - -def read_list(list_file): - rgb_depth_list = [] - with open(list_file) as f: - lines = f.readlines() - for line in lines: - rgb_depth_list.append(line.strip().split(" ")) - return rgb_depth_list - - -class Deep360(data.Dataset): - """The Deep360 Dataset""" - - def __init__(self, root_dir, list_file, height=504, width=1008, color_augmentation=True, - LR_filp_augmentation=True, yaw_rotation_augmentation=True, repeat=1, is_training=False): - """ - Args: - root_dir (string): Directory of the Deep360 Dataset. - list_file (string): Path to the txt file contain the list of image and depth files. - height, width: input size. - disable_color_augmentation, disable_LR_filp_augmentation, - disable_yaw_rotation_augmentation: augmentation options. - is_training (bool): True if the dataset is the training set. - """ - self.root_dir = root_dir - self.rgb_depth_list = read_list(list_file) - - self.w = width - self.h = height - - self.max_depth_meters = 100.0 - self.min_depth_meters = 0.01 - - self.color_augmentation = color_augmentation - self.LR_filp_augmentation = LR_filp_augmentation - self.yaw_rotation_augmentation = yaw_rotation_augmentation - - self.is_training = is_training - - if self.color_augmentation: - try: - self.brightness = (0.8, 1.2) - self.contrast = (0.8, 1.2) - self.saturation = (0.8, 1.2) - self.hue = (-0.1, 0.1) - self.color_aug= transforms.ColorJitter( - self.brightness, self.contrast, self.saturation, self.hue) - except TypeError: - self.brightness = 0.2 - self.contrast = 0.2 - self.saturation = 0.2 - self.hue = 0.1 - self.color_aug = transforms.ColorJitter( - self.brightness, self.contrast, self.saturation, self.hue) - - self.to_tensor = transforms.ToTensor() - self.normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) - - def __len__(self): - return len(self.rgb_depth_list) - - def __getitem__(self, idx): - if torch.is_tensor(idx): - idx = idx.tolist() - - inputs = {} - - rgb_name = self.root_dir + self.rgb_depth_list[idx][0] - rgb = cv2.imread(rgb_name) - if rgb is None: - print(rgb_name) - rgb = cv2.cvtColor(rgb, cv2.COLOR_BGR2RGB) - # print(rgb.shape) - rgb = cassini2Equirec(rgb) - rgb = cv2.resize(rgb, dsize=(self.w, self.h), interpolation=cv2.INTER_CUBIC) - - depth_name = self.root_dir + self.rgb_depth_list[idx][1] - gt_depth = np.load(depth_name)['arr_0'].astype(np.float32) - gt_depth = cassini2Equirec(gt_depth) - gt_depth = cv2.resize(gt_depth, dsize=(self.w, self.h), interpolation=cv2.INTER_NEAREST) - gt_depth[gt_depth > self.max_depth_meters] = self.max_depth_meters - - - - - if self.is_training and self.yaw_rotation_augmentation: - # random yaw rotation - roll_idx = random.randint(0, self.w) - rgb = np.roll(rgb, roll_idx, 1) - gt_depth = np.roll(gt_depth, roll_idx, 1) - - if self.is_training and self.LR_filp_augmentation and random.random() > 0.5: - rgb = cv2.flip(rgb, 1) - gt_depth = cv2.flip(gt_depth, 1) - - if self.is_training and self.color_augmentation and random.random() > 0.5: - aug_rgb = np.asarray(self.color_aug(transforms.ToPILImage()(rgb))) - else: - aug_rgb = rgb.copy() - - aug_rgb = self.to_tensor(aug_rgb.copy()) - - gt_depth = torch.from_numpy(np.expand_dims(gt_depth, axis=0)).to(torch.float32) - - val_mask = ((gt_depth > 0) & (gt_depth <= self.max_depth_meters) & ~torch.isnan(gt_depth)) - - mask_100 = (gt_depth < 100.0) - # Normalize depth - # _min, _max = torch.quantile(gt_depth[val_mask], torch.tensor([0.02, 1 - 0.02]),) - # gt_depth_norm = (gt_depth - 0.01) / (_max - 0.01) - # gt_depth_norm = torch.clip(gt_depth_norm, 0.01, 1.0) - - gt_depth_norm = gt_depth / self.max_depth_meters - gt_depth_norm = torch.clip(gt_depth_norm, 0.001, 1.0) - - - # Conduct output - inputs = {} - - inputs["rgb"] = self.normalize(aug_rgb) - inputs["gt_depth"] = gt_depth_norm - inputs["val_mask"] = val_mask - inputs["mask_100"] = mask_100 - inputs["touying"] = False - # inputs["moge_depth"] = moge_depth - - return inputs - -if __name__ == "__main__": - dataset = Deep360(root_dir='/hpc2hdd/home/zcao740/Documents/Dataset/Deep360', list_file='/hpc2hdd/home/zcao740/Documents/360Depth/Semi-supervision/datasets/deep360_train.txt') - print(len(dataset)) - for i in range(2): - print(dataset[i]) \ No newline at end of file diff --git a/DAP-main-2/datasets/deep360_test_final.txt b/DAP-main-2/datasets/deep360_test_final.txt deleted file mode 100644 index 14da9f49800bf7334f6b255da742db3c10c9b3c2..0000000000000000000000000000000000000000 --- a/DAP-main-2/datasets/deep360_test_final.txt +++ /dev/null @@ -1,600 +0,0 @@ -/ep1_500frames/testing/rgb/002505_12_rgb1.png /ep1_500frames/testing/depth/002505_depth.npz -/ep1_500frames/testing/rgb/002730_12_rgb1.png /ep1_500frames/testing/depth/002730_depth.npz -/ep1_500frames/testing/rgb/002955_12_rgb1.png /ep1_500frames/testing/depth/002955_depth.npz 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1b780badafe7bebb9ebd57703ae4502d2979669b..0000000000000000000000000000000000000000 --- a/DAP-main-2/datasets/insta23k.py +++ /dev/null @@ -1,129 +0,0 @@ -from __future__ import print_function -import os -import cv2 -import numpy as np -import random - -import torch -from torch.utils import data -from torchvision import transforms - - -def read_list(list_file): - rgb_depth_list = [] - with open(list_file) as f: - lines = f.readlines() - for line in lines: - rgb_depth_list.append(line.strip().split(" ")) - return rgb_depth_list - - -class Insta23k(data.Dataset): - """The Structured3D Dataset""" - - def __init__(self, root_dir, list_file, height=504, width=1008, color_augmentation=True, - LR_filp_augmentation=True, yaw_rotation_augmentation=True, repeat=1, is_training=False): - """ - Args: - root_dir (string): Directory of the Stanford2D3D Dataset. - list_file (string): Path to the txt file contain the list of image and depth files. - height, width: input size. - disable_color_augmentation, disable_LR_filp_augmentation, - disable_yaw_rotation_augmentation: augmentation options. - is_training (bool): True if the dataset is the training set. - """ - self.root_dir = root_dir - self.rgb_depth_list = read_list(list_file) - - self.w = width - self.h = height - - self.max_depth_meters = 100.0 - self.min_depth_meters = 0.01 - - self.color_augmentation = color_augmentation - self.LR_filp_augmentation = LR_filp_augmentation - self.yaw_rotation_augmentation = yaw_rotation_augmentation - - self.is_training = is_training - - if self.color_augmentation: - try: - self.brightness = (0.8, 1.2) - self.contrast = (0.8, 1.2) - self.saturation = (0.8, 1.2) - self.hue = (-0.1, 0.1) - self.color_aug= transforms.ColorJitter( - self.brightness, self.contrast, self.saturation, self.hue) - except TypeError: - self.brightness = 0.2 - self.contrast = 0.2 - self.saturation = 0.2 - self.hue = 0.1 - self.color_aug = transforms.ColorJitter( - self.brightness, self.contrast, self.saturation, self.hue) - - self.to_tensor = transforms.ToTensor() - self.normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) - - def __len__(self): - return len(self.rgb_depth_list) - - def __getitem__(self, idx): - if torch.is_tensor(idx): - idx = idx.tolist() - - inputs = {} - - rgb_name = os.path.join(self.root_dir, self.rgb_depth_list[idx][0]) - rgb = cv2.imread(rgb_name) - if rgb is None: - print(rgb_name) - rgb = cv2.cvtColor(rgb, cv2.COLOR_BGR2RGB) - rgb = cv2.resize(rgb, dsize=(self.w, self.h), interpolation=cv2.INTER_CUBIC) - - depth_name = os.path.join(self.root_dir, self.rgb_depth_list[idx][1]) - - gt_depth = np.load(depth_name).astype(np.float32) - - gt_depth = cv2.resize(gt_depth, dsize=(self.w, self.h), interpolation=cv2.INTER_NEAREST) - gt_depth[gt_depth > self.max_depth_meters] = self.max_depth_meters - - if self.is_training and self.yaw_rotation_augmentation: - # random yaw rotation - roll_idx = random.randint(0, self.w) - rgb = np.roll(rgb, roll_idx, 1) - gt_depth = np.roll(gt_depth, roll_idx, 1) - - if self.is_training and self.LR_filp_augmentation and random.random() > 0.5: - rgb = cv2.flip(rgb, 1) - gt_depth = cv2.flip(gt_depth, 1) - - if self.is_training and self.color_augmentation and random.random() > 0.5: - aug_rgb = np.asarray(self.color_aug(transforms.ToPILImage()(rgb))) - else: - aug_rgb = rgb.copy() - - aug_rgb = self.to_tensor(aug_rgb.copy()) - - gt_depth = torch.from_numpy(np.expand_dims(gt_depth, axis=0)).to(torch.float32) - val_mask = ((gt_depth > 0) & (gt_depth <= self.max_depth_meters) & ~torch.isnan(gt_depth)) - - mask_100 = (gt_depth < 100.0) - - # Normalize depth - # _min, _max = torch.quantile(gt_depth[val_mask], torch.tensor([0.02, 1 - 0.02]),) - # gt_depth_norm = (gt_depth - 0.01) / (_max - 0.01) - # gt_depth_norm = torch.clip(gt_depth_norm, 0.01, 1.0) - # print(gt_depth) - gt_depth_norm = gt_depth / self.max_depth_meters - gt_depth_norm = torch.clip(gt_depth_norm, 0.001, 1.0) - - # Conduct output - inputs = {} - inputs["rgb"] = self.normalize(aug_rgb) - inputs["gt_depth"] = gt_depth_norm - inputs["val_mask"] = val_mask - inputs["mask_100"] = mask_100 - inputs["touying"] = False - return inputs \ No newline at end of file diff --git a/DAP-main-2/datasets/instadata_list_test.txt b/DAP-main-2/datasets/instadata_list_test.txt deleted file mode 100644 index 5d1555cf715a48ad818f6b2b134904685491783f..0000000000000000000000000000000000000000 --- a/DAP-main-2/datasets/instadata_list_test.txt +++ /dev/null @@ -1,1343 +0,0 @@ -raw_data_0630/sfc_raw_images_panorama/panorama_3251.png raw_data_0630/sfc_distance_panorama_npy/erp_distance_3251.npy -raw_data_0630/sfc_raw_images_panorama/panorama_209.png raw_data_0630/sfc_distance_panorama_npy/erp_distance_209.npy -raw_data_0701/raw_images_panorama/panorama_3577.png raw_data_0701/erp_distance_npy/erp_distance_3577.npy -raw_data_0701/raw_images_panorama/panorama_3320.png raw_data_0701/erp_distance_npy/erp_distance_3320.npy 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-YVUC4YcDtcY/6_spherical_1_emission_center_0.png YVUC4YcDtcY/6_spherical_1_depth_center_0.exr -YVUC4YcDtcY/7_spherical_1_emission_center_0.png YVUC4YcDtcY/7_spherical_1_depth_center_0.exr -YVUC4YcDtcY/8_spherical_1_emission_center_0.png YVUC4YcDtcY/8_spherical_1_depth_center_0.exr -YVUC4YcDtcY/9_spherical_1_emission_center_0.png YVUC4YcDtcY/9_spherical_1_depth_center_0.exr diff --git a/DAP-main-2/datasets/stanford2d3d.py b/DAP-main-2/datasets/stanford2d3d.py deleted file mode 100644 index a708b8385ca012ef40ff5ada5dde7dc8e8770dcf..0000000000000000000000000000000000000000 --- a/DAP-main-2/datasets/stanford2d3d.py +++ /dev/null @@ -1,147 +0,0 @@ -from __future__ import print_function -import os -import cv2 -import numpy as np -import random - -import torch -from torch.utils import data -from torchvision import transforms - -from .util import Equirec2Cube - - -def read_list(list_file): - rgb_depth_list = [] - with open(list_file) as f: - lines = f.readlines() - for line in lines: - rgb_depth_list.append(line.strip().split(" ")) - return rgb_depth_list - - -class Stanford2D3D(data.Dataset): - """The Stanford2D3D Dataset""" - - def __init__(self, root_dir, list_file, height=512, width=1024, color_augmentation=True, - LR_filp_augmentation=True, yaw_rotation_augmentation=True, repeat=1, is_training=False): - """ - Args: - root_dir (string): Directory of the Stanford2D3D Dataset. - list_file (string): Path to the txt file contain the list of image and depth files. - height, width: input size. - disable_color_augmentation, disable_LR_filp_augmentation, - disable_yaw_rotation_augmentation: augmentation options. - is_training (bool): True if the dataset is the training set. - """ - self.root_dir = root_dir - self.rgb_depth_list = read_list(list_file) - if is_training: - self.rgb_depth_list = 10 * self.rgb_depth_list - self.w = width - self.h = height - - self.max_depth_meters = 100.0 - - self.color_augmentation = color_augmentation - self.LR_filp_augmentation = LR_filp_augmentation - self.yaw_rotation_augmentation = yaw_rotation_augmentation - - self.is_training = is_training - - self.e2c = Equirec2Cube(self.h, self.w, self.h // 2) - - if self.color_augmentation: - try: - self.brightness = (0.8, 1.2) - self.contrast = (0.8, 1.2) - self.saturation = (0.8, 1.2) - self.hue = (-0.1, 0.1) - self.color_aug= transforms.ColorJitter( - self.brightness, self.contrast, self.saturation, self.hue) - except TypeError: - self.brightness = 0.2 - self.contrast = 0.2 - self.saturation = 0.2 - self.hue = 0.1 - self.color_aug = transforms.ColorJitter( - self.brightness, self.contrast, self.saturation, self.hue) - - self.to_tensor = transforms.ToTensor() - self.normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) - - def __len__(self): - return len(self.rgb_depth_list) - - def __getitem__(self, idx): - if torch.is_tensor(idx): - idx = idx.tolist() - - inputs = {} - - rgb_name = os.path.join(self.root_dir, self.rgb_depth_list[idx][0]) - # rgb_name = rgb_name.replace("rgb_", "rgb_pre_") # use the preprocessed rgb images - rgb = cv2.imread(rgb_name) - rgb = cv2.cvtColor(rgb, cv2.COLOR_BGR2RGB) - rgb = cv2.resize(rgb, dsize=(self.w, self.h), interpolation=cv2.INTER_CUBIC) - - depth_name = os.path.join(self.root_dir, self.rgb_depth_list[idx][1]) - gt_depth = cv2.imread(depth_name, -1) - gt_depth = cv2.resize(gt_depth, dsize=(self.w, self.h), interpolation=cv2.INTER_NEAREST) - gt_depth = gt_depth.astype(float) / 512 - gt_depth[gt_depth > self.max_depth_meters+1] = self.max_depth_meters + 1 - - # gt_depth = (gt_depth - gt_depth.min()) / (gt_depth.max() - gt_depth.min()) - # gt_depth = (gt_depth * 255).astype(np.uint8) - # vis_img = np.concatenate([rgb, cv2.cvtColor(gt_depth, cv2.COLOR_GRAY2BGR)], axis=0) - # cv2.imwrite(f"./shit/vis_img_{idx}.png", vis_img) - - if self.is_training and self.yaw_rotation_augmentation: - # random yaw rotation - roll_idx = random.randint(0, self.w) - rgb = np.roll(rgb, roll_idx, 1) - gt_depth = np.roll(gt_depth, roll_idx, 1) - - if self.is_training and self.LR_filp_augmentation and random.random() > 0.5: - rgb = cv2.flip(rgb, 1) - gt_depth = cv2.flip(gt_depth, 1) - - if self.is_training and self.color_augmentation and random.random() > 0.5: - aug_rgb = np.asarray(self.color_aug(transforms.ToPILImage()(rgb))) - else: - aug_rgb = rgb - - cube_rgb = self.e2c.run(rgb) - cube_aug_rgb = self.e2c.run(aug_rgb) - - rgb = self.to_tensor(rgb.copy()) - cube_rgb = self.to_tensor(cube_rgb.copy()) - aug_rgb = self.to_tensor(aug_rgb.copy()) - cube_aug_rgb = self.to_tensor(cube_aug_rgb.copy()) - gt_depth = torch.from_numpy(np.expand_dims(gt_depth, axis=0)).to(torch.float32) - - val_mask = ((gt_depth > 0) & (gt_depth <= self.max_depth_meters) & ~torch.isnan(gt_depth)) - - # 归一化深度值，0.01-1.0 - # _min, _max = torch.quantile(gt_depth[val_mask], torch.tensor([0.02, 1 - 0.02]),) - gt_depth_norm = gt_depth / self.max_depth_meters - gt_depth_norm = torch.clip(gt_depth_norm, 0.001, 1.0) - - - inputs["rgb"] = self.normalize(aug_rgb) - inputs["normalized_rgb"] = self.normalize(aug_rgb) - - inputs["cube_rgb"] = cube_rgb - inputs["normalized_cube_rgb"] = self.normalize(cube_aug_rgb) - - inputs["gt_depth"] = gt_depth_norm - inputs["val_mask"] = val_mask # 合法区域，不是全true，真把不能用的区域划出来了；其他参与训练的数据集是全true的（除了投影数据集） - inputs["mask_100"] = (gt_depth > 0) & (gt_depth <= 100) - # 对于这个数据集，mask_100设定为全true的，因为求不出来。大于100米的深度gt也有可能是玻璃镜子等物体，反正这个数据集也不参加训练 - - # 这个数据集中，模型预测的mask100应该是被val_mask涵盖的，所以mask100理论上没有影响 - # val_mask控制计算指标的区域 - return inputs - - - diff --git a/DAP-main-2/datasets/stanford2d3d_test.txt b/DAP-main-2/datasets/stanford2d3d_test.txt deleted file mode 100644 index 0ec73a68c2c5326c44785d0488e28f10aec53868..0000000000000000000000000000000000000000 --- a/DAP-main-2/datasets/stanford2d3d_test.txt +++ /dev/null @@ -1,373 +0,0 @@ -area_5a/pano/rgb_/camera_029bb1440fca47e2886a88559bbf8d00_hallway_1_frame_equirectangular_domain_rgb.png 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a/DAP-main-2/datasets/util.py +++ /dev/null @@ -1,100 +0,0 @@ -import numpy as np -from scipy.ndimage import map_coordinates -import cv2 - - -# Based on https://github.com/sunset1995/py360convert -class Equirec2Cube: - def __init__(self, equ_h, equ_w, face_w): - ''' - equ_h: int, height of the equirectangular image - equ_w: int, width of the equirectangular image - face_w: int, the length of each face of the cubemap - ''' - - self.equ_h = equ_h - self.equ_w = equ_w - self.face_w = face_w - - self._xyzcube() - self._xyz2coor() - - # For convert R-distance to Z-depth for CubeMaps - cosmap = 1 / np.sqrt((2 * self.grid[..., 0]) ** 2 + (2 * self.grid[..., 1]) ** 2 + 1) - self.cosmaps = np.concatenate(6 * [cosmap], axis=1)[..., np.newaxis] - - def _xyzcube(self): - ''' - Compute the xyz cordinates of the unit cube in [F R B L U D] format. - ''' - self.xyz = np.zeros((self.face_w, self.face_w * 6, 3), np.float32) - rng = np.linspace(-0.5, 0.5, num=self.face_w, dtype=np.float32) - self.grid = np.stack(np.meshgrid(rng, -rng), -1) - - # Front face (z = 0.5) - self.xyz[:, 0 * self.face_w:1 * self.face_w, [0, 1]] = self.grid - self.xyz[:, 0 * self.face_w:1 * self.face_w, 2] = 0.5 - - # Right face (x = 0.5) - self.xyz[:, 1 * self.face_w:2 * self.face_w, [2, 1]] = self.grid[:, ::-1] - self.xyz[:, 1 * self.face_w:2 * self.face_w, 0] = 0.5 - - # Back face (z = -0.5) - self.xyz[:, 2 * self.face_w:3 * self.face_w, [0, 1]] = self.grid[:, ::-1] - self.xyz[:, 2 * self.face_w:3 * self.face_w, 2] = -0.5 - - # Left face (x = -0.5) - self.xyz[:, 3 * self.face_w:4 * self.face_w, [2, 1]] = self.grid - self.xyz[:, 3 * self.face_w:4 * self.face_w, 0] = -0.5 - - # Up face (y = 0.5) - self.xyz[:, 4 * self.face_w:5 * self.face_w, [0, 2]] = self.grid[::-1, :] - self.xyz[:, 4 * self.face_w:5 * self.face_w, 1] = 0.5 - - # Down face (y = -0.5) - self.xyz[:, 5 * self.face_w:6 * self.face_w, [0, 2]] = self.grid - self.xyz[:, 5 * self.face_w:6 * self.face_w, 1] = -0.5 - - def _xyz2coor(self): - - # x, y, z to longitude and latitude - x, y, z = np.split(self.xyz, 3, axis=-1) - lon = np.arctan2(x, z) - c = np.sqrt(x ** 2 + z ** 2) - lat = np.arctan2(y, c) - - # longitude and latitude to equirectangular coordinate - self.coor_x = (lon / (2 * np.pi) + 0.5) * self.equ_w - 0.5 - self.coor_y = (-lat / np.pi + 0.5) * self.equ_h - 0.5 - - def sample_equirec(self, e_img, order=0): - pad_u = np.roll(e_img[[0]], self.equ_w // 2, 1) - pad_d = np.roll(e_img[[-1]], self.equ_w // 2, 1) - e_img = np.concatenate([e_img, pad_d, pad_u], 0) - # pad_l = e_img[:, [0]] - # pad_r = e_img[:, [-1]] - # e_img = np.concatenate([e_img, pad_l, pad_r], 1) - - return map_coordinates(e_img, [self.coor_y, self.coor_x], - order=order, mode='wrap')[..., 0] - - def run(self, equ_img, equ_dep=None): - - h, w = equ_img.shape[:2] - if h != self.equ_h or w != self.equ_w: - equ_img = cv2.resize(equ_img, (self.equ_w, self.equ_h)) - if equ_dep is not None: - equ_dep = cv2.resize(equ_dep, (self.equ_w, self.equ_h), interpolation=cv2.INTER_NEAREST) - - cube_img = np.stack([self.sample_equirec(equ_img[..., i], order=1) - for i in range(equ_img.shape[2])], axis=-1) - - if equ_dep is not None: - cube_dep = np.stack([self.sample_equirec(equ_dep[..., i], order=0) - for i in range(equ_dep.shape[2])], axis=-1) - cube_dep = cube_dep * self.cosmaps - - if equ_dep is not None: - return cube_img, cube_dep - else: - return cube_img \ No newline at end of file diff --git a/DAP-main-2/depth2normal.py b/DAP-main-2/depth2normal.py deleted file mode 100644 index ed5c699dddc4f314b66b4519719f896d0f79a7e3..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth2normal.py +++ /dev/null @@ -1,139 +0,0 @@ -import numpy as np -import cv2 -import torch -import torch.nn.functional as F -from PIL import Image -import utils3d # 你原来的工具库 -import os -# ---------------------------- -# 工具函数 -# ---------------------------- -def spherical_uv_to_directions(uv: np.ndarray): - theta, phi = (1 - uv[..., 0]) * (2 * np.pi), uv[..., 1] * np.pi - directions = np.stack([ - np.sin(phi) * np.cos(theta), - np.sin(phi) * np.sin(theta), - np.cos(phi) - ], axis=-1) - return directions - - -def spherical_uv_to_directions_torch(uv: torch.Tensor, device: str = 'cuda'): - theta, phi = (1 - uv[..., 0]) * (2 * np.pi), uv[..., 1] * np.pi - directions = torch.stack([ - torch.sin(phi) * torch.cos(theta), - torch.sin(phi) * torch.sin(theta), - torch.cos(phi) - ], axis=-1).to(device) - return directions - - -def normal_normalize(normal: np.ndarray): - normal_norm = np.linalg.norm(normal, axis=-1, keepdims=True) - normal_norm[normal_norm < 1e-6] = 1e-6 - return normal / normal_norm - - -def normal_normalize_torch(normal: torch.Tensor): - normal_norm = torch.norm(normal, dim=-1, keepdim=True) - normal_norm = torch.where( - normal_norm < 1e-6, - torch.tensor(1e-6, device=normal_norm.device, dtype=normal_norm.dtype), - normal_norm - ) - return normal / normal_norm - - -def normal_to_rgb(normal: np.ndarray | torch.Tensor, normal_mask: np.ndarray | torch.Tensor = None): - """ normal ([-1,1]) → RGB ([0,255]) """ - if torch.is_tensor(normal): - normal = normal.detach().cpu().numpy() - if normal_mask is not None: - normal_mask = normal_mask.detach().cpu().numpy() - - normal_rgb = (((normal + 1) * 0.5) * 255).astype(np.uint8) - - if normal_mask is not None: - normal_mask_c = np.stack([normal_mask]*3, axis=-1).astype(np.uint8) - normal_rgb = normal_rgb * normal_mask_c - - return normal_rgb - - -# ---------------------------- -# 深度转法线 (numpy版) -# ---------------------------- -def depth2normal(depth: np.ndarray, mask: np.ndarray = None, to_rgb: bool = False): - h, w = depth.shape[:2] - # depth → 三维点 - points = depth[:, :, None] * spherical_uv_to_directions(utils3d.numpy.image_uv(width=w, height=h)) - - if mask is None: - mask = np.ones_like(depth, dtype=bool) - - normal, normal_mask = utils3d.numpy.points_to_normals(points, mask) - - # 调整方向 & normalize - normal = normal * np.array([-1, -1, 1]) - normal = normal_normalize(normal) - - # 重排通道 (和你原代码一致) - normal = np.stack([normal[..., 0], normal[..., 2], normal[..., 1]], axis=-1) - - if to_rgb: - return normal, normal_mask, Image.fromarray(normal_to_rgb(normal, normal_mask)) - else: - return normal, normal_mask - - -# ---------------------------- -# 深度转法线 (torch版) -# ---------------------------- -def depth2normal_torch(depth: torch.Tensor, mask: torch.Tensor = None, to_rgb: bool = False): - h, w = depth.shape[-2:] - points = depth.unsqueeze(-1) * spherical_uv_to_directions_torch(utils3d.torch.image_uv(width=w, height=h), device=depth.device) - - if mask is None: - mask = torch.ones_like(depth, dtype=torch.uint8) - - normal, normal_mask = utils3d.torch.points_to_normals(points, mask) - - # 调整方向 - normal = normal * torch.tensor([-1, -1, 1], device=normal.device, dtype=normal.dtype) - normal = normal_normalize_torch(normal) - - # 重排通道 - normal = torch.stack([normal[..., 0], normal[..., 2], normal[..., 1]], dim=-1) - - if to_rgb: - normal_mask_img = normal_mask.squeeze() - normal_imgs = [Image.fromarray(normal_to_rgb(normal[i], normal_mask_img[i])) for i in range(normal.shape[0])] - return normal, normal_mask, normal_imgs - else: - return normal, normal_mask - - -# ---------------------------- -# 主程序 -# ---------------------------- -if __name__ == "__main__": - import argparse - - parser = argparse.ArgumentParser() - parser.add_argument('--img-path', default='', type=str) - - args = parser.parse_args() - - - #args.img_path是一个文件夹，文件夹中包含多个深度图 - save_out = os.path.dirname(args.img_path) + '/normal' - os.makedirs(save_out, exist_ok=True) - for depth_path in os.listdir(args.img_path): - depth_path = os.path.join(args.img_path, depth_path) - - depth = np.load(depth_path).astype(np.float32) - # depth = cv2.imread(depth_path, cv2.IMREAD_UNCHANGED).astype(np.float32) / 255 - - normal, mask, normal_img = depth2normal(depth, to_rgb=True) - normal_img.save(os.path.join(save_out, depth_path.split('/')[-1].replace('.npy', '.png'))) - # normal_img.save(os.path.join(save_out, depth_path.split('/')[-1])) \ No newline at end of file diff --git a/DAP-main-2/depth2point.py b/DAP-main-2/depth2point.py deleted file mode 100644 index 6305aa266131e895804a6b2d0cee1dd8580dbba7..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth2point.py +++ /dev/null @@ -1,151 +0,0 @@ -import numpy as np -import cv2 -import os -from plyfile import PlyData, PlyElement -import utils3d # 你本地已有的库 -import torch - -def spherical_uv_to_directions(uv: np.ndarray): - theta, phi = (1 - uv[..., 0]) * (2 * np.pi), uv[..., 1] * np.pi - directions = np.stack([ - np.sin(phi) * np.cos(theta), - np.sin(phi) * np.sin(theta), - np.cos(phi) - ], axis=-1) - return directions - -def spherical_uv_to_directions_torch(uv: torch.Tensor): - """ - torch版本的球面UV坐标转方向向量 - Args: - uv: UV坐标，形状为 [H, W, 2] 或 [B, H, W, 2] - Returns: - directions: 方向向量，形状为 [H, W, 3] 或 [B, H, W, 3] - """ - theta = (1 - uv[..., 0]) * (2 * torch.pi) - phi = uv[..., 1] * torch.pi - directions = torch.stack([ - torch.sin(phi) * torch.cos(theta), - torch.sin(phi) * torch.sin(theta), - torch.cos(phi) - ], dim=-1) - return directions - -def save_3d_points(points: np.array, colors: np.array, mask: np.array, filename: str): - points = points.reshape(-1, 3) - colors = colors.reshape(-1, 3) - mask = mask.reshape(-1) - - vertex_data = np.empty(mask.sum(), dtype=[ - ('x', 'f4'), ('y', 'f4'), ('z', 'f4'), - ('red', 'u1'), ('green', 'u1'), ('blue', 'u1') - ]) - vertex_data['x'] = points[mask, 0] - vertex_data['y'] = points[mask, 1] - vertex_data['z'] = points[mask, 2] - vertex_data['red'] = colors[mask, 0] - vertex_data['green'] = colors[mask, 1] - vertex_data['blue'] = colors[mask, 2] - - vertex_element = PlyElement.describe(vertex_data, 'vertex', comments=['point cloud']) - PlyData([vertex_element], text=True).write(filename) - -def depth2pointcloud(depth_path: str, image_path: str, out_ply: str): - # 读取深度图 - depth = cv2.imread(depth_path, cv2.IMREAD_UNCHANGED) - if depth is None: - raise FileNotFoundError(f"无法读取深度图: {depth_path}") - -# 如果读出来是三通道，把它转成灰度 - if depth.ndim == 3: - depth = cv2.cvtColor(depth, cv2.COLOR_BGR2GRAY) - - depth = depth.astype(np.float32) - # 将深度归一化到合理范围（假设输入是16位或8位） - if depth.dtype == np.uint8: - depth = depth / 255.0 - elif depth.dtype == np.uint16: - depth = depth / 65535.0 - - h, w = depth.shape - uv = utils3d.numpy.image_uv(width=w, height=h) # [H,W,2] - dirs = spherical_uv_to_directions(uv) # [H,W,3] - points = depth[..., None] * dirs # [H,W,3] - - # 读取颜色图 - image = cv2.imread(image_path, cv2.IMREAD_COLOR) - if image is None: - raise FileNotFoundError(f"无法读取原图: {image_path}") - image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) - if image.shape[:2] != (h, w): - image = cv2.resize(image, (w, h), interpolation=cv2.INTER_LINEAR) - - mask = depth > 0 - save_3d_points(points, image, mask, out_ply) - print(f"✅ 点云已保存到 {out_ply}") - - -def depth2pts(depth): - # 读取深度图 - depth.squeeze(1) # B,1,H,W -> B,H,W (范围是0-1) - - depth = depth.astype(np.float32) - - b, h, w = depth.shape - - # 生成UV坐标 [H,W,2] - uv = utils3d.numpy.image_uv(width=w, height=h) - # 转换为球面方向 [H,W,3] - dirs = spherical_uv_to_directions(uv) - - # 广播计算3D点云 [B,H,W,3] - points = depth[..., None] * dirs[None, ...] # [B,H,W,1] * [1,H,W,3] = [B,H,W,3] - - return points - -def depth2pts_torch(depth): - """ - 将深度图转换为3D点云，支持batch处理（torch版本） - Args: - depth: 深度图torch tensor，形状为 [B, H, W] 或 [B, 1, H, W]，值范围0-1 - Returns: - points: 3D点云torch tensor，形状为 [B, H, W, 3] - """ - # 确保输入是float32类型 - depth = depth.float() - - # 如果输入是 [B, 1, H, W]，则压缩为 [B, H, W] - if depth.dim() == 4 and depth.shape[1] == 1: - depth = depth.squeeze(1) # B,1,H,W -> B,H,W - - b, h, w = depth.shape - - # 生成UV坐标 [H,W,2] - uv = utils3d.numpy.image_uv(width=w, height=h) - # 转换为torch tensor并移动到相同设备 - uv = torch.from_numpy(uv).to(depth.device).to(depth.dtype) - - # 转换为球面方向 [H,W,3] - dirs = spherical_uv_to_directions_torch(uv) - - # 广播计算3D点云 [B,H,W,3] - points = depth[..., None] * dirs[None, ...] # [B,H,W,1] * [1,H,W,3] = [B,H,W,3] - - return points - - - -if __name__ == "__main__": - # depth_path = "/home/tione/notebook/home/wenxuan/PanDA_dualhead_alltrain/visual_nonormalloss/depth/depth_save_2.png" # 你的深度 PNG - # image_path = "/home/tione/notebook/home/wenxuan/PanDA_dualhead_alltrain/visual_nonormalloss/rgb/rgb_save_2.png" # 你的原图 PNG - # out_ply = "/home/tione/notebook/home/wenxuan/PanDA_dualhead_alltrain/visual_nonormalloss/pts/scene001_points.ply" - path_ = "/home/tione/notebook/home/wenxuan/DAM_dualhead_alltrain/vis_exp2_insta820/rgb/" - for file in os.listdir(path_): - image_path = os.path.join(path_, file) - depth_path = os.path.join(path_.replace("rgb", "depth"), file.replace("rgb", "depth")) - os.makedirs(path_.replace("rgb", "pts"), exist_ok=True) - out_ply = os.path.join(path_.replace("rgb", "pts"), file.replace(".png", ".ply")) - depth2pointcloud(depth_path, image_path, out_ply) - - # os.makedirs("output", exist_ok=True) - # depth2pointcloud(depth_path, image_path, out_ply) diff --git a/DAP-main-2/depth_anything_utils.py b/DAP-main-2/depth_anything_utils.py deleted file mode 100644 index c1911789cd7ab307d9bed14217811a90d795e614..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_utils.py +++ /dev/null @@ -1,249 +0,0 @@ -import os -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 \ No newline at end of file diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/__pycache__/dinov2.cpython-310.pyc b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/__pycache__/dinov2.cpython-310.pyc deleted file mode 100644 index 74a06408b101a4c54fa5b5dc5697740bd0457064..0000000000000000000000000000000000000000 Binary files a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/__pycache__/dinov2.cpython-310.pyc and /dev/null differ diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/__pycache__/dinov3_adpther.cpython-310.pyc b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/__pycache__/dinov3_adpther.cpython-310.pyc deleted file mode 100644 index 810ba6be6e1db9daf36e84d7de67116931b4648c..0000000000000000000000000000000000000000 Binary files a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/__pycache__/dinov3_adpther.cpython-310.pyc and /dev/null differ diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/__pycache__/dpt.cpython-310.pyc b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/__pycache__/dpt.cpython-310.pyc deleted file mode 100644 index d755b0791352de8096e5d8388282419f6b58a0f7..0000000000000000000000000000000000000000 Binary files a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/__pycache__/dpt.cpython-310.pyc and /dev/null differ diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov2.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov2.py deleted file mode 100644 index ec4499a18330523aa3564b16be70e813de000c94..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov2.py +++ /dev/null @@ -1,415 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This source code is licensed under the Apache License, Version 2.0 -# found in the LICENSE file in the root directory of this source tree. - -# References: -# https://github.com/facebookresearch/dino/blob/main/vision_transformer.py -# https://github.com/rwightman/pytorch-image-models/tree/master/timm/models/vision_transformer.py - -from functools import partial -import math -import logging -from typing import Sequence, Tuple, Union, Callable - -import torch -import torch.nn as nn -import torch.utils.checkpoint -from torch.nn.init import trunc_normal_ - -from .dinov2_layers import Mlp, PatchEmbed, SwiGLUFFNFused, MemEffAttention, NestedTensorBlock as Block - - -logger = logging.getLogger("dinov2") - - -def named_apply(fn: Callable, module: nn.Module, name="", depth_first=True, include_root=False) -> nn.Module: - if not depth_first and include_root: - fn(module=module, name=name) - for child_name, child_module in module.named_children(): - child_name = ".".join((name, child_name)) if name else child_name - named_apply(fn=fn, module=child_module, name=child_name, depth_first=depth_first, include_root=True) - if depth_first and include_root: - fn(module=module, name=name) - return module - - -class BlockChunk(nn.ModuleList): - def forward(self, x): - for b in self: - x = b(x) - return x - - -class DinoVisionTransformer(nn.Module): - def __init__( - self, - img_size=224, - patch_size=16, - in_chans=3, - embed_dim=768, - depth=12, - num_heads=12, - mlp_ratio=4.0, - qkv_bias=True, - ffn_bias=True, - proj_bias=True, - drop_path_rate=0.0, - drop_path_uniform=False, - init_values=None, # for layerscale: None or 0 => no layerscale - embed_layer=PatchEmbed, - act_layer=nn.GELU, - block_fn=Block, - ffn_layer="mlp", - block_chunks=1, - num_register_tokens=0, - interpolate_antialias=False, - interpolate_offset=0.1, - ): - """ - Args: - img_size (int, tuple): input image size - patch_size (int, tuple): patch size - in_chans (int): number of input channels - embed_dim (int): embedding dimension - depth (int): depth of transformer - num_heads (int): number of attention heads - mlp_ratio (int): ratio of mlp hidden dim to embedding dim - qkv_bias (bool): enable bias for qkv if True - proj_bias (bool): enable bias for proj in attn if True - ffn_bias (bool): enable bias for ffn if True - drop_path_rate (float): stochastic depth rate - drop_path_uniform (bool): apply uniform drop rate across blocks - weight_init (str): weight init scheme - init_values (float): layer-scale init values - embed_layer (nn.Module): patch embedding layer - act_layer (nn.Module): MLP activation layer - block_fn (nn.Module): transformer block class - ffn_layer (str): "mlp", "swiglu", "swiglufused" or "identity" - block_chunks: (int) split block sequence into block_chunks units for FSDP wrap - num_register_tokens: (int) number of extra cls tokens (so-called "registers") - interpolate_antialias: (str) flag to apply anti-aliasing when interpolating positional embeddings - interpolate_offset: (float) work-around offset to apply when interpolating positional embeddings - """ - super().__init__() - norm_layer = partial(nn.LayerNorm, eps=1e-6) - - self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models - self.num_tokens = 1 - self.n_blocks = depth - self.num_heads = num_heads - self.patch_size = patch_size - self.num_register_tokens = num_register_tokens - self.interpolate_antialias = interpolate_antialias - self.interpolate_offset = interpolate_offset - - self.patch_embed = embed_layer(img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim) - num_patches = self.patch_embed.num_patches - - self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) - self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + self.num_tokens, embed_dim)) - assert num_register_tokens >= 0 - self.register_tokens = ( - nn.Parameter(torch.zeros(1, num_register_tokens, embed_dim)) if num_register_tokens else None - ) - - if drop_path_uniform is True: - dpr = [drop_path_rate] * depth - else: - dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule - - if ffn_layer == "mlp": - logger.info("using MLP layer as FFN") - ffn_layer = Mlp - elif ffn_layer == "swiglufused" or ffn_layer == "swiglu": - logger.info("using SwiGLU layer as FFN") - ffn_layer = SwiGLUFFNFused - elif ffn_layer == "identity": - logger.info("using Identity layer as FFN") - - def f(*args, **kwargs): - return nn.Identity() - - ffn_layer = f - else: - raise NotImplementedError - - blocks_list = [ - block_fn( - dim=embed_dim, - num_heads=num_heads, - mlp_ratio=mlp_ratio, - qkv_bias=qkv_bias, - proj_bias=proj_bias, - ffn_bias=ffn_bias, - drop_path=dpr[i], - norm_layer=norm_layer, - act_layer=act_layer, - ffn_layer=ffn_layer, - init_values=init_values, - ) - for i in range(depth) - ] - if block_chunks > 0: - self.chunked_blocks = True - chunked_blocks = [] - chunksize = depth // block_chunks - for i in range(0, depth, chunksize): - # this is to keep the block index consistent if we chunk the block list - chunked_blocks.append([nn.Identity()] * i + blocks_list[i : i + chunksize]) - self.blocks = nn.ModuleList([BlockChunk(p) for p in chunked_blocks]) - else: - self.chunked_blocks = False - self.blocks = nn.ModuleList(blocks_list) - - self.norm = norm_layer(embed_dim) - self.head = nn.Identity() - - self.mask_token = nn.Parameter(torch.zeros(1, embed_dim)) - - self.init_weights() - - def init_weights(self): - trunc_normal_(self.pos_embed, std=0.02) - nn.init.normal_(self.cls_token, std=1e-6) - if self.register_tokens is not None: - nn.init.normal_(self.register_tokens, std=1e-6) - named_apply(init_weights_vit_timm, self) - - def interpolate_pos_encoding(self, x, w, h): - previous_dtype = x.dtype - npatch = x.shape[1] - 1 - N = self.pos_embed.shape[1] - 1 - if npatch == N and w == h: - return self.pos_embed - pos_embed = self.pos_embed.float() - class_pos_embed = pos_embed[:, 0] - patch_pos_embed = pos_embed[:, 1:] - dim = x.shape[-1] - w0 = w // self.patch_size - h0 = h // self.patch_size - # we add a small number to avoid floating point error in the interpolation - # see discussion at https://github.com/facebookresearch/dino/issues/8 - # DINOv2 with register modify the interpolate_offset from 0.1 to 0.0 - w0, h0 = w0 + self.interpolate_offset, h0 + self.interpolate_offset - # w0, h0 = w0 + 0.1, h0 + 0.1 - - sqrt_N = math.sqrt(N) - sx, sy = float(w0) / sqrt_N, float(h0) / sqrt_N - patch_pos_embed = nn.functional.interpolate( - patch_pos_embed.reshape(1, int(sqrt_N), int(sqrt_N), dim).permute(0, 3, 1, 2), - scale_factor=(sx, sy), - # (int(w0), int(h0)), # to solve the upsampling shape issue - mode="bicubic", - antialias=self.interpolate_antialias - ) - - assert int(w0) == patch_pos_embed.shape[-2] - assert int(h0) == patch_pos_embed.shape[-1] - patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim) - return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=1).to(previous_dtype) - - def prepare_tokens_with_masks(self, x, masks=None): - B, nc, w, h = x.shape - x = self.patch_embed(x) - if masks is not None: - x = torch.where(masks.unsqueeze(-1), self.mask_token.to(x.dtype).unsqueeze(0), x) - - x = torch.cat((self.cls_token.expand(x.shape[0], -1, -1), x), dim=1) - x = x + self.interpolate_pos_encoding(x, w, h) - - if self.register_tokens is not None: - x = torch.cat( - ( - x[:, :1], - self.register_tokens.expand(x.shape[0], -1, -1), - x[:, 1:], - ), - dim=1, - ) - - return x - - def forward_features_list(self, x_list, masks_list): - x = [self.prepare_tokens_with_masks(x, masks) for x, masks in zip(x_list, masks_list)] - for blk in self.blocks: - x = blk(x) - - all_x = x - output = [] - for x, masks in zip(all_x, masks_list): - x_norm = self.norm(x) - output.append( - { - "x_norm_clstoken": x_norm[:, 0], - "x_norm_regtokens": x_norm[:, 1 : self.num_register_tokens + 1], - "x_norm_patchtokens": x_norm[:, self.num_register_tokens + 1 :], - "x_prenorm": x, - "masks": masks, - } - ) - return output - - def forward_features(self, x, masks=None): - if isinstance(x, list): - return self.forward_features_list(x, masks) - - x = self.prepare_tokens_with_masks(x, masks) - - for blk in self.blocks: - x = blk(x) - - x_norm = self.norm(x) - return { - "x_norm_clstoken": x_norm[:, 0], - "x_norm_regtokens": x_norm[:, 1 : self.num_register_tokens + 1], - "x_norm_patchtokens": x_norm[:, self.num_register_tokens + 1 :], - "x_prenorm": x, - "masks": masks, - } - - def _get_intermediate_layers_not_chunked(self, x, n=1): - x = self.prepare_tokens_with_masks(x) - # If n is an int, take the n last blocks. If it's a list, take them - output, total_block_len = [], len(self.blocks) - blocks_to_take = range(total_block_len - n, total_block_len) if isinstance(n, int) else n - for i, blk in enumerate(self.blocks): - x = blk(x) - if i in blocks_to_take: - output.append(x) - assert len(output) == len(blocks_to_take), f"only {len(output)} / {len(blocks_to_take)} blocks found" - return output - - def _get_intermediate_layers_chunked(self, x, n=1): - x = self.prepare_tokens_with_masks(x) - output, i, total_block_len = [], 0, len(self.blocks[-1]) - # If n is an int, take the n last blocks. If it's a list, take them - blocks_to_take = range(total_block_len - n, total_block_len) if isinstance(n, int) else n - for block_chunk in self.blocks: - for blk in block_chunk[i:]: # Passing the nn.Identity() - x = blk(x) - if i in blocks_to_take: - output.append(x) - i += 1 - assert len(output) == len(blocks_to_take), f"only {len(output)} / {len(blocks_to_take)} blocks found" - return output - - def get_intermediate_layers( - self, - x: torch.Tensor, - n: Union[int, Sequence] = 1, # Layers or n last layers to take - reshape: bool = False, - return_class_token: bool = False, - norm=True - ) -> Tuple[Union[torch.Tensor, Tuple[torch.Tensor]]]: - if self.chunked_blocks: - outputs = self._get_intermediate_layers_chunked(x, n) - else: - outputs = self._get_intermediate_layers_not_chunked(x, n) - if norm: - outputs = [self.norm(out) for out in outputs] - class_tokens = [out[:, 0] for out in outputs] - outputs = [out[:, 1 + self.num_register_tokens:] for out in outputs] - if reshape: - B, _, w, h = x.shape - outputs = [ - out.reshape(B, w // self.patch_size, h // self.patch_size, -1).permute(0, 3, 1, 2).contiguous() - for out in outputs - ] - if return_class_token: - return tuple(zip(outputs, class_tokens)) - return tuple(outputs) - - def forward(self, *args, is_training=False, **kwargs): - ret = self.forward_features(*args, **kwargs) - if is_training: - return ret - else: - return self.head(ret["x_norm_clstoken"]) - - -def init_weights_vit_timm(module: nn.Module, name: str = ""): - """ViT weight initialization, original timm impl (for reproducibility)""" - if isinstance(module, nn.Linear): - trunc_normal_(module.weight, std=0.02) - if module.bias is not None: - nn.init.zeros_(module.bias) - - -def vit_small(patch_size=16, num_register_tokens=0, **kwargs): - model = DinoVisionTransformer( - patch_size=patch_size, - embed_dim=384, - depth=12, - num_heads=6, - mlp_ratio=4, - block_fn=partial(Block, attn_class=MemEffAttention), - num_register_tokens=num_register_tokens, - **kwargs, - ) - return model - - -def vit_base(patch_size=16, num_register_tokens=0, **kwargs): - model = DinoVisionTransformer( - patch_size=patch_size, - embed_dim=768, - depth=12, - num_heads=12, - mlp_ratio=4, - block_fn=partial(Block, attn_class=MemEffAttention), - num_register_tokens=num_register_tokens, - **kwargs, - ) - return model - - -def vit_large(patch_size=16, num_register_tokens=0, **kwargs): - model = DinoVisionTransformer( - patch_size=patch_size, - embed_dim=1024, - depth=24, - num_heads=16, - mlp_ratio=4, - block_fn=partial(Block, attn_class=MemEffAttention), - num_register_tokens=num_register_tokens, - **kwargs, - ) - return model - - -def vit_giant2(patch_size=16, num_register_tokens=0, **kwargs): - """ - Close to ViT-giant, with embed-dim 1536 and 24 heads => embed-dim per head 64 - """ - model = DinoVisionTransformer( - patch_size=patch_size, - embed_dim=1536, - depth=40, - num_heads=24, - mlp_ratio=4, - block_fn=partial(Block, attn_class=MemEffAttention), - num_register_tokens=num_register_tokens, - **kwargs, - ) - return model - - -def DINOv2(model_name): - model_zoo = { - "vits": vit_small, - "vitb": vit_base, - "vitl": vit_large, - "vitg": vit_giant2 - } - - return model_zoo[model_name]( - img_size=518, - patch_size=14, - init_values=1.0, - ffn_layer="mlp" if model_name != "vitg" else "swiglufused", - block_chunks=0, - num_register_tokens=0, - interpolate_antialias=False, - interpolate_offset=0.1 - ) \ No newline at end of file diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov2_layers/__init__.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov2_layers/__init__.py deleted file mode 100644 index 8120f4bc83066cb3f825ce32daa3b437f88486f1..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov2_layers/__init__.py +++ /dev/null @@ -1,11 +0,0 @@ -# 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. - -from .mlp import Mlp -from .patch_embed import PatchEmbed -from .swiglu_ffn import SwiGLUFFN, SwiGLUFFNFused -from .block import NestedTensorBlock -from .attention import MemEffAttention diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov2_layers/__pycache__/__init__.cpython-310.pyc b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov2_layers/__pycache__/__init__.cpython-310.pyc deleted file mode 100644 index 55f40f53ecdf9b855bee031d8e6ffdcd2096ff1c..0000000000000000000000000000000000000000 Binary files a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov2_layers/__pycache__/__init__.cpython-310.pyc and /dev/null differ diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov2_layers/__pycache__/attention.cpython-310.pyc b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov2_layers/__pycache__/attention.cpython-310.pyc deleted file mode 100644 index 47b6ec73eed47001b6088a552753273d1982f9e7..0000000000000000000000000000000000000000 Binary files a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov2_layers/__pycache__/attention.cpython-310.pyc and /dev/null differ diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov2_layers/__pycache__/block.cpython-310.pyc b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov2_layers/__pycache__/block.cpython-310.pyc deleted file mode 100644 index 185145ba288db09368774c9e4295865591152e9d..0000000000000000000000000000000000000000 Binary files a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov2_layers/__pycache__/block.cpython-310.pyc and /dev/null differ diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov2_layers/__pycache__/drop_path.cpython-310.pyc b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov2_layers/__pycache__/drop_path.cpython-310.pyc deleted file mode 100644 index 1a1f1c5c6d0edb0912b315d6bc4ece7df894aae8..0000000000000000000000000000000000000000 Binary files a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov2_layers/__pycache__/drop_path.cpython-310.pyc and /dev/null differ diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov2_layers/__pycache__/layer_scale.cpython-310.pyc b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov2_layers/__pycache__/layer_scale.cpython-310.pyc deleted file mode 100644 index 2c83aa43b35d38d73653ec7982074a72b91b60bf..0000000000000000000000000000000000000000 Binary files a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov2_layers/__pycache__/layer_scale.cpython-310.pyc and /dev/null differ diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov2_layers/__pycache__/mlp.cpython-310.pyc b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov2_layers/__pycache__/mlp.cpython-310.pyc deleted file mode 100644 index 4bc70ed47d2a4fd4ae83f269aecde47d415ac2f2..0000000000000000000000000000000000000000 Binary files a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov2_layers/__pycache__/mlp.cpython-310.pyc and /dev/null differ diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov2_layers/__pycache__/patch_embed.cpython-310.pyc b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov2_layers/__pycache__/patch_embed.cpython-310.pyc deleted file mode 100644 index b31ffc039e6dd8c8844df9907fe6b18c9f1831e8..0000000000000000000000000000000000000000 Binary files a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov2_layers/__pycache__/patch_embed.cpython-310.pyc and /dev/null differ diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov2_layers/__pycache__/swiglu_ffn.cpython-310.pyc b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov2_layers/__pycache__/swiglu_ffn.cpython-310.pyc deleted file mode 100644 index 8361356e4763f5563e46037feb30d9e39eed7ff4..0000000000000000000000000000000000000000 Binary files a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov2_layers/__pycache__/swiglu_ffn.cpython-310.pyc and /dev/null differ diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov2_layers/attention.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov2_layers/attention.py deleted file mode 100644 index 815a2bf53dbec496f6a184ed7d03bcecb7124262..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov2_layers/attention.py +++ /dev/null @@ -1,83 +0,0 @@ -# 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. - -# References: -# https://github.com/facebookresearch/dino/blob/master/vision_transformer.py -# https://github.com/rwightman/pytorch-image-models/tree/master/timm/models/vision_transformer.py - -import logging - -from torch import Tensor -from torch import nn - - -logger = logging.getLogger("dinov2") - - -try: - from xformers.ops import memory_efficient_attention, unbind, fmha - - XFORMERS_AVAILABLE = True -except ImportError: - logger.warning("xFormers not available") - XFORMERS_AVAILABLE = False - - -class Attention(nn.Module): - def __init__( - self, - dim: int, - num_heads: int = 8, - qkv_bias: bool = False, - proj_bias: bool = True, - attn_drop: float = 0.0, - proj_drop: float = 0.0, - ) -> None: - super().__init__() - self.num_heads = num_heads - head_dim = dim // num_heads - self.scale = head_dim**-0.5 - - self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) - self.attn_drop = nn.Dropout(attn_drop) - self.proj = nn.Linear(dim, dim, bias=proj_bias) - self.proj_drop = nn.Dropout(proj_drop) - - def forward(self, x: Tensor) -> Tensor: - B, N, C = x.shape - qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) - - q, k, v = qkv[0] * self.scale, qkv[1], qkv[2] - attn = q @ k.transpose(-2, -1) - - attn = attn.softmax(dim=-1) - attn = self.attn_drop(attn) - - x = (attn @ v).transpose(1, 2).reshape(B, N, C) - x = self.proj(x) - x = self.proj_drop(x) - return x - - -class MemEffAttention(Attention): - def forward(self, x: Tensor, attn_bias=None) -> Tensor: - if not XFORMERS_AVAILABLE: - assert attn_bias is None, "xFormers is required for nested tensors usage" - return super().forward(x) - - B, N, C = x.shape - qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads) - - q, k, v = unbind(qkv, 2) - - x = memory_efficient_attention(q, k, v, attn_bias=attn_bias) - x = x.reshape([B, N, C]) - - x = self.proj(x) - x = self.proj_drop(x) - return x - - \ No newline at end of file diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov2_layers/block.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov2_layers/block.py deleted file mode 100644 index 25488f57cc0ad3c692f86b62555f6668e2a66db1..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov2_layers/block.py +++ /dev/null @@ -1,252 +0,0 @@ -# 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. - -# References: -# https://github.com/facebookresearch/dino/blob/master/vision_transformer.py -# https://github.com/rwightman/pytorch-image-models/tree/master/timm/layers/patch_embed.py - -import logging -from typing import Callable, List, Any, Tuple, Dict - -import torch -from torch import nn, Tensor - -from .attention import Attention, MemEffAttention -from .drop_path import DropPath -from .layer_scale import LayerScale -from .mlp import Mlp - - -logger = logging.getLogger("dinov2") - - -try: - from xformers.ops import fmha - from xformers.ops import scaled_index_add, index_select_cat - - XFORMERS_AVAILABLE = True -except ImportError: - logger.warning("xFormers not available") - XFORMERS_AVAILABLE = False - - -class Block(nn.Module): - def __init__( - self, - dim: int, - num_heads: int, - mlp_ratio: float = 4.0, - qkv_bias: bool = False, - proj_bias: bool = True, - ffn_bias: bool = True, - drop: float = 0.0, - attn_drop: float = 0.0, - init_values=None, - drop_path: float = 0.0, - act_layer: Callable[..., nn.Module] = nn.GELU, - norm_layer: Callable[..., nn.Module] = nn.LayerNorm, - attn_class: Callable[..., nn.Module] = Attention, - ffn_layer: Callable[..., nn.Module] = Mlp, - ) -> None: - super().__init__() - # print(f"biases: qkv: {qkv_bias}, proj: {proj_bias}, ffn: {ffn_bias}") - self.norm1 = norm_layer(dim) - self.attn = attn_class( - dim, - num_heads=num_heads, - qkv_bias=qkv_bias, - proj_bias=proj_bias, - attn_drop=attn_drop, - proj_drop=drop, - ) - self.ls1 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity() - self.drop_path1 = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() - - self.norm2 = norm_layer(dim) - mlp_hidden_dim = int(dim * mlp_ratio) - self.mlp = ffn_layer( - in_features=dim, - hidden_features=mlp_hidden_dim, - act_layer=act_layer, - drop=drop, - bias=ffn_bias, - ) - self.ls2 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity() - self.drop_path2 = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() - - self.sample_drop_ratio = drop_path - - def forward(self, x: Tensor) -> Tensor: - def attn_residual_func(x: Tensor) -> Tensor: - return self.ls1(self.attn(self.norm1(x))) - - def ffn_residual_func(x: Tensor) -> Tensor: - return self.ls2(self.mlp(self.norm2(x))) - - if self.training and self.sample_drop_ratio > 0.1: - # the overhead is compensated only for a drop path rate larger than 0.1 - x = drop_add_residual_stochastic_depth( - x, - residual_func=attn_residual_func, - sample_drop_ratio=self.sample_drop_ratio, - ) - x = drop_add_residual_stochastic_depth( - x, - residual_func=ffn_residual_func, - sample_drop_ratio=self.sample_drop_ratio, - ) - elif self.training and self.sample_drop_ratio > 0.0: - x = x + self.drop_path1(attn_residual_func(x)) - x = x + self.drop_path1(ffn_residual_func(x)) # FIXME: drop_path2 - else: - x = x + attn_residual_func(x) - x = x + ffn_residual_func(x) - return x - - -def drop_add_residual_stochastic_depth( - x: Tensor, - residual_func: Callable[[Tensor], Tensor], - sample_drop_ratio: float = 0.0, -) -> Tensor: - # 1) extract subset using permutation - b, n, d = x.shape - sample_subset_size = max(int(b * (1 - sample_drop_ratio)), 1) - brange = (torch.randperm(b, device=x.device))[:sample_subset_size] - x_subset = x[brange] - - # 2) apply residual_func to get residual - residual = residual_func(x_subset) - - x_flat = x.flatten(1) - residual = residual.flatten(1) - - residual_scale_factor = b / sample_subset_size - - # 3) add the residual - x_plus_residual = torch.index_add(x_flat, 0, brange, residual.to(dtype=x.dtype), alpha=residual_scale_factor) - return x_plus_residual.view_as(x) - - -def get_branges_scales(x, sample_drop_ratio=0.0): - b, n, d = x.shape - sample_subset_size = max(int(b * (1 - sample_drop_ratio)), 1) - brange = (torch.randperm(b, device=x.device))[:sample_subset_size] - residual_scale_factor = b / sample_subset_size - return brange, residual_scale_factor - - -def add_residual(x, brange, residual, residual_scale_factor, scaling_vector=None): - if scaling_vector is None: - x_flat = x.flatten(1) - residual = residual.flatten(1) - x_plus_residual = torch.index_add(x_flat, 0, brange, residual.to(dtype=x.dtype), alpha=residual_scale_factor) - else: - x_plus_residual = scaled_index_add( - x, brange, residual.to(dtype=x.dtype), scaling=scaling_vector, alpha=residual_scale_factor - ) - return x_plus_residual - - -attn_bias_cache: Dict[Tuple, Any] = {} - - -def get_attn_bias_and_cat(x_list, branges=None): - """ - this will perform the index select, cat the tensors, and provide the attn_bias from cache - """ - batch_sizes = [b.shape[0] for b in branges] if branges is not None else [x.shape[0] for x in x_list] - all_shapes = tuple((b, x.shape[1]) for b, x in zip(batch_sizes, x_list)) - if all_shapes not in attn_bias_cache.keys(): - seqlens = [] - for b, x in zip(batch_sizes, x_list): - for _ in range(b): - seqlens.append(x.shape[1]) - attn_bias = fmha.BlockDiagonalMask.from_seqlens(seqlens) - attn_bias._batch_sizes = batch_sizes - attn_bias_cache[all_shapes] = attn_bias - - if branges is not None: - cat_tensors = index_select_cat([x.flatten(1) for x in x_list], branges).view(1, -1, x_list[0].shape[-1]) - else: - tensors_bs1 = tuple(x.reshape([1, -1, *x.shape[2:]]) for x in x_list) - cat_tensors = torch.cat(tensors_bs1, dim=1) - - return attn_bias_cache[all_shapes], cat_tensors - - -def drop_add_residual_stochastic_depth_list( - x_list: List[Tensor], - residual_func: Callable[[Tensor, Any], Tensor], - sample_drop_ratio: float = 0.0, - scaling_vector=None, -) -> Tensor: - # 1) generate random set of indices for dropping samples in the batch - branges_scales = [get_branges_scales(x, sample_drop_ratio=sample_drop_ratio) for x in x_list] - branges = [s[0] for s in branges_scales] - residual_scale_factors = [s[1] for s in branges_scales] - - # 2) get attention bias and index+concat the tensors - attn_bias, x_cat = get_attn_bias_and_cat(x_list, branges) - - # 3) apply residual_func to get residual, and split the result - residual_list = attn_bias.split(residual_func(x_cat, attn_bias=attn_bias)) # type: ignore - - outputs = [] - for x, brange, residual, residual_scale_factor in zip(x_list, branges, residual_list, residual_scale_factors): - outputs.append(add_residual(x, brange, residual, residual_scale_factor, scaling_vector).view_as(x)) - return outputs - - -class NestedTensorBlock(Block): - def forward_nested(self, x_list: List[Tensor]) -> List[Tensor]: - """ - x_list contains a list of tensors to nest together and run - """ - assert isinstance(self.attn, MemEffAttention) - - if self.training and self.sample_drop_ratio > 0.0: - - def attn_residual_func(x: Tensor, attn_bias=None) -> Tensor: - return self.attn(self.norm1(x), attn_bias=attn_bias) - - def ffn_residual_func(x: Tensor, attn_bias=None) -> Tensor: - return self.mlp(self.norm2(x)) - - x_list = drop_add_residual_stochastic_depth_list( - x_list, - residual_func=attn_residual_func, - sample_drop_ratio=self.sample_drop_ratio, - scaling_vector=self.ls1.gamma if isinstance(self.ls1, LayerScale) else None, - ) - x_list = drop_add_residual_stochastic_depth_list( - x_list, - residual_func=ffn_residual_func, - sample_drop_ratio=self.sample_drop_ratio, - scaling_vector=self.ls2.gamma if isinstance(self.ls1, LayerScale) else None, - ) - return x_list - else: - - def attn_residual_func(x: Tensor, attn_bias=None) -> Tensor: - return self.ls1(self.attn(self.norm1(x), attn_bias=attn_bias)) - - def ffn_residual_func(x: Tensor, attn_bias=None) -> Tensor: - return self.ls2(self.mlp(self.norm2(x))) - - attn_bias, x = get_attn_bias_and_cat(x_list) - x = x + attn_residual_func(x, attn_bias=attn_bias) - x = x + ffn_residual_func(x) - return attn_bias.split(x) - - def forward(self, x_or_x_list): - if isinstance(x_or_x_list, Tensor): - return super().forward(x_or_x_list) - elif isinstance(x_or_x_list, list): - assert XFORMERS_AVAILABLE, "Please install xFormers for nested tensors usage" - return self.forward_nested(x_or_x_list) - else: - raise AssertionError diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov2_layers/drop_path.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov2_layers/drop_path.py deleted file mode 100644 index af05625984dd14682cc96a63bf0c97bab1f123b1..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov2_layers/drop_path.py +++ /dev/null @@ -1,35 +0,0 @@ -# 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. - -# References: -# https://github.com/facebookresearch/dino/blob/master/vision_transformer.py -# https://github.com/rwightman/pytorch-image-models/tree/master/timm/layers/drop.py - - -from torch import nn - - -def drop_path(x, drop_prob: float = 0.0, training: bool = False): - if drop_prob == 0.0 or not training: - return x - keep_prob = 1 - drop_prob - shape = (x.shape[0],) + (1,) * (x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets - random_tensor = x.new_empty(shape).bernoulli_(keep_prob) - if keep_prob > 0.0: - random_tensor.div_(keep_prob) - output = x * random_tensor - return output - - -class DropPath(nn.Module): - """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).""" - - def __init__(self, drop_prob=None): - super(DropPath, self).__init__() - self.drop_prob = drop_prob - - def forward(self, x): - return drop_path(x, self.drop_prob, self.training) diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov2_layers/layer_scale.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov2_layers/layer_scale.py deleted file mode 100644 index ca5daa52bd81d3581adeb2198ea5b7dba2a3aea1..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov2_layers/layer_scale.py +++ /dev/null @@ -1,28 +0,0 @@ -# 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. - -# Modified from: https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/vision_transformer.py#L103-L110 - -from typing import Union - -import torch -from torch import Tensor -from torch import nn - - -class LayerScale(nn.Module): - def __init__( - self, - dim: int, - init_values: Union[float, Tensor] = 1e-5, - inplace: bool = False, - ) -> None: - super().__init__() - self.inplace = inplace - self.gamma = nn.Parameter(init_values * torch.ones(dim)) - - def forward(self, x: Tensor) -> Tensor: - return x.mul_(self.gamma) if self.inplace else x * self.gamma diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov2_layers/mlp.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov2_layers/mlp.py deleted file mode 100644 index 5e4b315f972f9a9f54aef1e4ef4e81b52976f018..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov2_layers/mlp.py +++ /dev/null @@ -1,41 +0,0 @@ -# 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. - -# References: -# https://github.com/facebookresearch/dino/blob/master/vision_transformer.py -# https://github.com/rwightman/pytorch-image-models/tree/master/timm/layers/mlp.py - - -from typing import Callable, Optional - -from torch import Tensor, nn - - -class Mlp(nn.Module): - def __init__( - self, - in_features: int, - hidden_features: Optional[int] = None, - out_features: Optional[int] = None, - act_layer: Callable[..., nn.Module] = nn.GELU, - drop: float = 0.0, - bias: bool = True, - ) -> None: - super().__init__() - out_features = out_features or in_features - hidden_features = hidden_features or in_features - self.fc1 = nn.Linear(in_features, hidden_features, bias=bias) - self.act = act_layer() - self.fc2 = nn.Linear(hidden_features, out_features, bias=bias) - self.drop = nn.Dropout(drop) - - def forward(self, x: Tensor) -> Tensor: - x = self.fc1(x) - x = self.act(x) - x = self.drop(x) - x = self.fc2(x) - x = self.drop(x) - return x diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov2_layers/patch_embed.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov2_layers/patch_embed.py deleted file mode 100644 index 574abe41175568d700a389b8b96d1ba554914779..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov2_layers/patch_embed.py +++ /dev/null @@ -1,89 +0,0 @@ -# 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. - -# References: -# https://github.com/facebookresearch/dino/blob/master/vision_transformer.py -# https://github.com/rwightman/pytorch-image-models/tree/master/timm/layers/patch_embed.py - -from typing import Callable, Optional, Tuple, Union - -from torch import Tensor -import torch.nn as nn - - -def make_2tuple(x): - if isinstance(x, tuple): - assert len(x) == 2 - return x - - assert isinstance(x, int) - return (x, x) - - -class PatchEmbed(nn.Module): - """ - 2D image to patch embedding: (B,C,H,W) -> (B,N,D) - - Args: - img_size: Image size. - patch_size: Patch token size. - in_chans: Number of input image channels. - embed_dim: Number of linear projection output channels. - norm_layer: Normalization layer. - """ - - def __init__( - self, - img_size: Union[int, Tuple[int, int]] = 224, - patch_size: Union[int, Tuple[int, int]] = 16, - in_chans: int = 3, - embed_dim: int = 768, - norm_layer: Optional[Callable] = None, - flatten_embedding: bool = True, - ) -> None: - super().__init__() - - image_HW = make_2tuple(img_size) - patch_HW = make_2tuple(patch_size) - patch_grid_size = ( - image_HW[0] // patch_HW[0], - image_HW[1] // patch_HW[1], - ) - - self.img_size = image_HW - self.patch_size = patch_HW - self.patches_resolution = patch_grid_size - self.num_patches = patch_grid_size[0] * patch_grid_size[1] - - self.in_chans = in_chans - self.embed_dim = embed_dim - - self.flatten_embedding = flatten_embedding - - self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_HW, stride=patch_HW) - self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity() - - def forward(self, x: Tensor) -> Tensor: - _, _, H, W = x.shape - patch_H, patch_W = self.patch_size - - assert H % patch_H == 0, f"Input image height {H} is not a multiple of patch height {patch_H}" - assert W % patch_W == 0, f"Input image width {W} is not a multiple of patch width: {patch_W}" - - x = self.proj(x) # B C H W - H, W = x.size(2), x.size(3) - x = x.flatten(2).transpose(1, 2) # B HW C - x = self.norm(x) - if not self.flatten_embedding: - x = x.reshape(-1, H, W, self.embed_dim) # B H W C - return x - - def flops(self) -> float: - Ho, Wo = self.patches_resolution - flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1]) - if self.norm is not None: - flops += Ho * Wo * self.embed_dim - return flops diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov2_layers/swiglu_ffn.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov2_layers/swiglu_ffn.py deleted file mode 100644 index b3324b266fb0a50ccf8c3a0ede2ae10ac4dfa03e..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov2_layers/swiglu_ffn.py +++ /dev/null @@ -1,63 +0,0 @@ -# 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. - -from typing import Callable, Optional - -from torch import Tensor, nn -import torch.nn.functional as F - - -class SwiGLUFFN(nn.Module): - def __init__( - self, - in_features: int, - hidden_features: Optional[int] = None, - out_features: Optional[int] = None, - act_layer: Callable[..., nn.Module] = None, - drop: float = 0.0, - bias: bool = True, - ) -> None: - super().__init__() - out_features = out_features or in_features - hidden_features = hidden_features or in_features - self.w12 = nn.Linear(in_features, 2 * hidden_features, bias=bias) - self.w3 = nn.Linear(hidden_features, out_features, bias=bias) - - def forward(self, x: Tensor) -> Tensor: - x12 = self.w12(x) - x1, x2 = x12.chunk(2, dim=-1) - hidden = F.silu(x1) * x2 - return self.w3(hidden) - - -try: - from xformers.ops import SwiGLU - - XFORMERS_AVAILABLE = True -except ImportError: - SwiGLU = SwiGLUFFN - XFORMERS_AVAILABLE = False - - -class SwiGLUFFNFused(SwiGLU): - def __init__( - self, - in_features: int, - hidden_features: Optional[int] = None, - out_features: Optional[int] = None, - act_layer: Callable[..., nn.Module] = None, - drop: float = 0.0, - bias: bool = True, - ) -> None: - out_features = out_features or in_features - hidden_features = hidden_features or in_features - hidden_features = (int(hidden_features * 2 / 3) + 7) // 8 * 8 - super().__init__( - in_features=in_features, - hidden_features=hidden_features, - out_features=out_features, - bias=bias, - ) diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/CODE_OF_CONDUCT.md b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/CODE_OF_CONDUCT.md deleted file mode 100644 index 3232ed665566ec047ce55a929db1581dbda266a1..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/CODE_OF_CONDUCT.md +++ /dev/null @@ -1,80 +0,0 @@ -# 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 . 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 diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/CONTRIBUTING.md b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/CONTRIBUTING.md deleted file mode 100644 index c5f0c3e75e4921e5aa706898b4f6eaccd8e7d009..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/CONTRIBUTING.md +++ /dev/null @@ -1,31 +0,0 @@ -# Contributing to DINOv3 -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: - -## 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 DINOv3, you agree that your contributions will be licensed -under the LICENSE.md file in the root directory of this source tree. diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/LICENSE.md b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/LICENSE.md deleted file mode 100644 index f531b1e6b5ab2318957bbf8ad1bda9f800a23e17..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/LICENSE.md +++ /dev/null @@ -1,66 +0,0 @@ -# DINOv3 License - -*Last Updated: August 19, 2025* - -**“Agreement”** means the terms and conditions for use, reproduction, distribution and modification of the DINO Materials set forth herein. - -**“DINO Materials”** means, collectively, Documentation and the models, software and algorithms, including machine-learning model code, trained model weights, inference-enabling code, training-enabling code, fine-tuning enabling code, and other elements of the foregoing distributed by Meta and made available under this Agreement. - -**“Documentation”** means the specifications, manuals and documentation accompanying -DINO Materials distributed by Meta. - -**“Licensee”** or **“you”** means you, or your employer or any other person or entity (if you are entering into this Agreement on such person or entity’s behalf), of the age required under applicable laws, rules or regulations to provide legal consent and that has legal authority to bind your employer or such other person or entity if you are entering in this Agreement on their behalf. - -**“Meta”** or **“we”** means Meta Platforms Ireland Limited (if you are located in or, if you are an entity, your principal place of business is in the EEA or Switzerland) or Meta Platforms, Inc. (if you are located outside of the EEA or Switzerland). - -**“Sanctions”** means any economic or trade sanctions or restrictions administered or enforced by the United States (including the Office of Foreign Assets Control of the U.S. Department of the Treasury (“OFAC”), the U.S. Department of State and the U.S. Department of Commerce), the United Nations, the European Union, or the United Kingdom. - -**“Trade Controls”** means any of the following: Sanctions and applicable export and import controls. - -By clicking “I Accept” below or by using or distributing any portion or element of the DINO Materials, you agree to be bound by this Agreement. - -## 1. License Rights and Redistribution. - -a. Grant of Rights. You are granted a non-exclusive, worldwide, non-transferable and royalty-free limited license under Meta’s intellectual property or other rights owned by Meta embodied in the DINO Materials to use, reproduce, distribute, copy, create derivative works of, and make modifications to the DINO Materials. - -b. Redistribution and Use. - -i. Distribution of DINO Materials, and any derivative works thereof, are subject to the terms of this Agreement. If you distribute or make the DINO Materials, or any derivative works thereof, available to a third party, you may only do so under the terms of this Agreement and you shall provide a copy of this Agreement with any such DINO Materials. - -ii. If you submit for publication the results of research you perform on, using, or otherwise in connection with DINO Materials, you must acknowledge the use of DINO Materials in your publication. - -iii. Your use of the DINO Materials must comply with applicable laws and regulations, including Trade Control Laws and applicable privacy and data protection laws. - -iv. Your use of the DINO Materials will not involve or encourage others to reverse engineer, decompile or discover the underlying components of the DINO Materials. - -v. You are not the target of Trade Controls and your use of DINO Materials must comply with Trade Controls. You agree not to use, or permit others to use, DINO Materials for any activities subject to the International Traffic in Arms Regulations (ITAR) or end uses prohibited by Trade Controls, including those related to military or warfare purposes, nuclear industries or applications, espionage, or the development or use of guns or illegal weapons. - -## 2. User Support. - -Your use of the DINO Materials is done at your own discretion; Meta does not process any information nor provide any service in relation to such use. Meta is under no obligation to provide any support services for the DINO Materials. Any support provided is “as is”, “with all faults”, and without warranty of any kind. - -## 3. Disclaimer of Warranty. - -UNLESS REQUIRED BY APPLICABLE LAW, THE DINO MATERIALS AND ANY OUTPUT AND RESULTS THEREFROM ARE PROVIDED ON AN “AS IS” BASIS, WITHOUT WARRANTIES OF ANY KIND, AND META DISCLAIMS ALL WARRANTIES OF ANY KIND, BOTH EXPRESS AND IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE FOR DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING THE DINO MATERIALS AND ASSUME ANY RISKS ASSOCIATED WITH YOUR USE OF THE DINO MATERIALS AND ANY OUTPUT AND RESULTS. - -## 4. Limitation of Liability. - -IN NO EVENT WILL META OR ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, FOR ANY LOST PROFITS OR ANY DIRECT OR INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL, EXEMPLARY OR PUNITIVE DAMAGES, EVEN IF META OR ITS AFFILIATES HAVE BEEN ADVISED OF THE POSSIBILITY OF ANY OF THE FOREGOING. - -## 5. Intellectual Property. - -a. Subject to Meta’s ownership of DINO Materials and derivatives made by or for Meta, with respect to any derivative works and modifications of the DINO Materials that are made by you, as between you and Meta, you are and will be the owner of such derivative works and modifications. - -b. If you institute litigation or other proceedings against Meta or any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the DINO Materials, outputs or results, or any portion of any of the foregoing, constitutes infringement of intellectual property or other rights owned or licensable by you, then any licenses granted to you under this Agreement shall terminate as of the date such litigation or claim is filed or instituted. You will indemnify and hold harmless Meta from and against any claim by any third party arising out of or related to your use or distribution of the DINO Materials. - -## 6. Term and Termination. - -The term of this Agreement will commence upon your acceptance of this Agreement or access to the DINO Materials and will continue in full force and effect until terminated in accordance with the terms and conditions herein. Meta may terminate this Agreement if you are in breach of any term or condition of this Agreement. Upon termination of this Agreement, you shall delete and cease use of the DINO Materials. Sections 3, 4 and 7 shall survive the termination of this Agreement. - -## 7. Governing Law and Jurisdiction. - -This Agreement will be governed and construed under the laws of the State of California without regard to choice of law principles, and the UN Convention on Contracts for the International Sale of Goods does not apply to this Agreement. The courts of California shall have exclusive jurisdiction of any dispute arising out of this Agreement. - -## 8. Modifications and Amendments. - -Meta may modify this Agreement from time to time; provided that they are similar in spirit to the current version of the Agreement, but may differ in detail to address new problems or concerns. All such changes will be effective immediately. Your continued use of the DINO Materials after any modification to this Agreement constitutes your agreement to such modification. Except as provided in this Agreement, no modification or addition to any provision of this Agreement will be binding unless it is in writing and signed by an authorized representative of both you and Meta. diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/MODEL_CARD.md b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/MODEL_CARD.md deleted file mode 100644 index 2bd45fb07beab1ad5f7a734608e79d5fd8899b42..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/MODEL_CARD.md +++ /dev/null @@ -1,432 +0,0 @@ -# Model Card for DINOv3 - -DINOv3 is a family of versatile vision foundation models that outperforms the specialized state of the art across a broad range of settings, without fine-tuning. DINOv3 produces high-quality dense features that achieve outstanding performance on various vision tasks, significantly surpassing previous self- and weakly-supervised foundation models. - -## Model Details - -These are Vision Transformer and ConvNeXt models trained following the method described in the DINOv3 paper. 12 models are provided: - -- 10 models pretrained on web data (LVD-1689M dataset) - - 1 ViT-7B trained from scratch, - - 5 ViT-S/S+/B/L/H+ models distilled from the ViT-7B, - - 4 ConvNeXt-{T/S/B/L} models distilled from the ViT-7B, -- 2 models pretrained on satellite data (SAT-493M dataset) - - 1 ViT-7B trained from scratch - - 1 ViT-L distilled from the ViT-7B - - -Each Transformer-based model takes an image as input and returns a class token, patch tokens (and register tokens). These models follow a ViT architecture, with a patch size of 16. For a 224x224 image, this results in 1 class token + 4 register tokens + 196 patch tokens = 201 tokens (for DINOv2 with registers this resulted in 1 + 4 + 256 = 261 tokens). - -The models can accept larger images provided the image shapes are multiples of the patch size (16). 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, ConvNeXt -- **License:** [DINOv3 License](https://ai.meta.com/resources/models-and-libraries/dinov3-license/) - -### Model Sources - -- **Repository:** [https://github.com/facebookresearch/dinov3](https://github.com/facebookresearch/dinov3) -- **Paper:** [https://arxiv.org/abs/2508.10104](https://arxiv.org/abs/2508.10104) - -## 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 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 -- on geometric and semantic 3D keypoint correspondances -- on depth estimation, semantic segmentation, using linear layers -- on unsupervised object discovery -- on video segmentation tracking -- on video classification, using a small 4-layer attentive probe - -### Downstream Use - -While fine-tuning the models can yield some gains, it is recommended to keep this option as a last resort: the frozen features are expected to provide good performance out-of-the-box. - -## Bias, Risks, and Limitations - -Compared to DINOv2 and SEERv2, DINOv3 delivers somewhat consistent performance across income categories on geographical fairness and diversity, although with a notable performance drop in the low-income bucket compared to the highest-income bucket. - -DINOv3 also achieves relatively good scores across different regions, improving over its predecessor DINOv2. However, a relative difference is still observed between Europe and Africa. - -### Recommendations - -Fine-tuning is expected to 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 - -model = torch.hub.load( - repo_or_dir='facebookresearch/dinov3', - model='', - weights='', -) - -# where MODEL_NAME can be one of: -# - dinov3_vits16 -# - dinov3_vits16plus -# - dinov3_vitb16 -# - dinov3_vitl16 -# - dinov3_vith16plus -# - dinov3_vit7b16 -# - dinov3_convnext_tiny -# - dinov3_convnext_small -# - dinov3_convnext_base -# - dinov3_convnext_large - -# For instance -dinov3_vits16 = torch.hub.load( - repo_or_dir='facebookresearch/dinov3', - model='dinov3_vits16', - weights='', -) -``` - -## Training Details - -### Training Data - -- Web dataset (LVD-1689M): a curated dataset of 1,689 millions of images extracted from a large data -pool of 17 billions web images collected from public posts on Instagram - -- Satellite dataset (SAT-493M): a dataset of 493 millions of 512x512 images sampled randomly from Maxar RGB ortho-rectified imagery at 0.6 meter resolution - -### Training Procedure - -**Training objective:** - -- DINO self-distillation loss with multi-crop -- iBOT masked-image modeling loss -- KoLeo regularization on [CLS] tokens -- Gram anchoring - -- **Training regime:** PyTorch FSDP2 (with bf16 and fp8 matrix multiplications) - -**Distillation:** - -- Distillation follows the standard DINOv3 pretraining procedure, except the teacher is a frozen pretrained ViT-7B. - -## Evaluation - -**Results** - -The reader is referred to the associated paper for details on the evaluation protocols - -*Results for ViT backbones pretrained (or distilled) on web (LVD-1689M)* - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Global TasksDense Tasks
ModelIN-ReaLIN-RObj.NetOx.-HADE20kNYU↓DAVISNAVISPair
DINOv3 ViT-S/1687.060.450.949.547.00.40372.756.350.4
DINOv3 ViT-S+/1688.068.854.650.048.80.39975.557.155.2
DINOv3 ViT-B/1689.376.764.158.551.80.37377.258.857.2
DINOv3 ViT-L/1690.288.174.863.154.90.35279.962.361.3
DINOv3 ViT-H+/1690.390.078.664.554.80.35279.363.356.3
DINOv3 ViT-7B/1690.491.191.172.855.90.30979.764.458.7
- -*Results for ConvNeXt backbones distilled on web (LVD-1689M)* - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Global TasksDense Tasks
ModelIN-ReaLIN-RObj.NetADE20kNYU↓
- @256px@512px@256px@512px@256px@512px
DINOv3 ConvNeXt Tiny86.687.773.774.152.658.742.70.448
DINOv3 ConvNeXt Small87.988.773.774.152.658.744.80.432
DINOv3 ConvNeXt Base88.589.277.278.256.261.346.30.420
DINOv3 ConvNeXt Large88.989.481.382.459.365.247.80.403
- -*Results for ViT backbones pretrained (or distilled) on satellite (SAT-493M)* - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
(GEO-Bench) Classification
Model - m-BEnetm-brick-kiln - m-eurosatm-forestnetm-pv4germ-so2satmean
DINOv3 ViT-L/1673.096.594.160.696.057.479.6
DINOv3 ViT-7B/1674.097.294.862.396.162.181.1
(GEO-Bench) Segmentation
Modelm-cashewm-chesapeakem-NeonTreem-nz-cattlem-pv4ger-segm-SA-cropmean
DINOv3 ViT-L/1694.275.661.883.795.236.874.5
DINOv3 ViT-7B/1694.176.662.683.495.537.675.0
- - -## Environmental Impact - -- **Hardware Type:** Nvidia H100 -- **Hours used:** 61,440 hours for ViT-7B model training -- **Cloud Provider:** Private infrastructure -- **Compute Region:** USA -- **Carbon Emitted:** 18t CO2eq - -## Technical Specifications - -### Model Architecture and Objective - -Vision Transformer models: - -- ViT-S (21M parameters): patch size 16, embedding dimension 384, 4 register tokens, 6 heads, MLP FFN, RoPE -- ViT-S+ (29M parameters): patch size 16, embedding dimension 384, 4 register tokens, 6 heads, SwiGLU FFN, RoPE -- ViT-B (86M parameters): patch size 16, embedding dimension 768, 4 register tokens, 12 heads, MLP FFN, RoPE -- ViT-L (300M parameters): patch size 16, embedding dimension 1024, 4 register tokens, 16 heads, MLP FFN, RoPE -- ViT-H+ (840M parameters): patch size 16, embedding dimension 1280, 4 register tokens, 20 heads, SwiGLU FFN, RoPE -- ViT-7B (6716M parameters): patch size 16, embedding dimension 4096, 4 register tokens, 32 heads, SwiGLU FFN, RoPE - -ConvNeXt models: - -- ConvNeXt Tiny (29M parameters) -- ConvNeXt Small (50M parameters) -- ConvNeXt Base (89M parameters) -- ConvNeXt Large (198M parameters) - -### Compute Infrastructure - -#### Hardware - -Nvidia H100 GPUs - -#### Software - -PyTorch 2.7 - -## More Information - -See the [blog post](https://ai.meta.com/blog/dinov3-self-supervised-vision-model/) and the associated [website](https://ai.meta.com/dinov3/). - -## Citation - -**BibTeX** - -``` -@misc{simeoni2025dinov3, - title={{DINOv3}}, - author={Sim{\'e}oni, Oriane and Vo, Huy V. and Seitzer, Maximilian and Baldassarre, Federico and Oquab, Maxime and Jose, Cijo and Khalidov, Vasil and Szafraniec, Marc and Yi, Seungeun and Ramamonjisoa, Micha{\"e}l and Massa, Francisco and Haziza, Daniel and Wehrstedt, Luca and Wang, Jianyuan and Darcet, Timoth{\'e}e and Moutakanni, Th{\'e}o and Sentana, Leonel and Roberts, Claire and Vedaldi, Andrea and Tolan, Jamie and Brandt, John and Couprie, Camille and Mairal, Julien and J{\'e}gou, Herv{\'e} and Labatut, Patrick and Bojanowski, Piotr}, - year={2025}, - eprint={2508.10104}, - archivePrefix={arXiv}, - primaryClass={cs.CV}, - url={https://arxiv.org/abs/2508.10104}, -} -``` diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/README.md b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/README.md deleted file mode 100644 index 18b1a35ea4d1d20f9ef6fb795b71864558276395..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/README.md +++ /dev/null @@ -1,734 +0,0 @@ -🆕 [2025-08-14] :fire: DINOv3 backbones are now available in [Hugging Face Hub](https://huggingface.co/collections/facebook/dinov3-68924841bd6b561778e31009) and [supported](https://huggingface.co/docs/transformers/model_doc/dinov3) by the Hugging Face [Transformers](https://huggingface.co/docs/transformers/index) library - -# DINOv3 🦖🦖🦖 - -**[Meta AI Research, FAIR](https://ai.meta.com/research/)** - -Oriane Siméoni, Huy V. Vo, Maximilian Seitzer, Federico Baldassarre, Maxime Oquab,
-Cijo Jose, Vasil Khalidov, Marc Szafraniec, Seungeun Yi, Michaël Ramamonjisoa,
-Francisco Massa, Daniel Haziza, Luca Wehrstedt, Jianyuan Wang,
-Timothée Darcet, Théo Moutakanni, Leonel Sentana, Claire Roberts,
-Andrea Vedaldi, Jamie Tolan, John Brandt, Camille Couprie,
-Julien Mairal, Hervé Jégou, Patrick Labatut, Piotr Bojanowski - -[ :scroll: [`Paper`](https://arxiv.org/abs/2508.10104)] [ :newspaper: [`Blog`](https://ai.meta.com/blog/dinov3-self-supervised-vision-model/)] [ :globe_with_meridians: [`Website`](https://ai.meta.com/dinov3/)] [ :book: [`BibTeX`](#citing-dinov3)] - -Reference PyTorch implementation and models for DINOv3. For details, see the **[DINOv3](https://arxiv.org/abs/2508.10104)** paper. - -## Overview - -
- market - - High-resolution dense features.
We visualize the cosine similarity maps obtained with DINOv3 output features
between the patches marked with a red cross and all other patches. -
- -
- -An extended family of versatile vision foundation models producing high-quality dense features and achieving outstanding performance on various vision tasks including outperforming the specialized state of the art across a broad range of settings, without fine-tuning - -## Pretrained models - -:information_source: Please follow the link provided below to get access to all the model weights: once accepted, an e-mail will be sent with the complete list of URLs pointing to all the available model weights (both backbones and adapters). These URLs can then be used to either: -- download the model or adapter weights to a local filesystem and point `torch.hub.load()` to these local weights via the `weights` or `backbone_weights` parameters, or -- directly invoke `torch.hub.load()` to download and load a backbone or an adapter from its URL via also the `weights` or `backbone_weights` parameters. - -See the example code snippets below. - -:warning: Please use `wget` instead of a web browser to download the weights. - -ViT models pretrained on web dataset (LVD-1689M): - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
ModelParametersPretraining
Dataset		Download
ViT-S/16 distilled 21MLVD-1689M[link]
ViT-S+/16 distilled29MLVD-1689M[link]
ViT-B/16 distilled86MLVD-1689M[link]
ViT-L/16 distilled300MLVD-1689M[link]
ViT-H+/16 distilled840MLVD-1689M[link]
ViT-7B/166,716MLVD-1689M[link]
- -ConvNeXt models pretrained on web dataset (LVD-1689M): - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
ModelParametersPretraining
Dataset		Download
ConvNeXt Tiny29MLVD-1689M[link]
ConvNeXt Small50MLVD-1689M[link]
ConvNeXt Base89MLVD-1689M[link]
ConvNeXt Large198MLVD-1689M[link]
- -ViT models pretrained on satellite dataset (SAT-493M): - - - - - - - - - - - - - - - - - - - - - - - -
ModelParametersPretraining
Dataset		Download
ViT-L/16 distilled300MSAT-493M[link]
ViT-7B/166,716MSAT-493M[link]
- - -### Pretrained backbones (via PyTorch [Hub](https://docs.pytorch.org/docs/stable/hub.html)) - -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. - -```python -import torch - -REPO_DIR = - -# DINOv3 ViT models pretrained on web images -dinov3_vits16 = torch.hub.load(REPO_DIR, 'dinov3_vits16', source='local', weights=) -dinov3_vits16plus = torch.hub.load(REPO_DIR, 'dinov3_vits16plus', source='local', weights=) -dinov3_vitb16 = torch.hub.load(REPO_DIR, 'dinov3_vitb16', source='local', weights=) -dinov3_vitl16 = torch.hub.load(REPO_DIR, 'dinov3_vitl16', source='local', weights=) -dinov3_vith16plus = torch.hub.load(REPO_DIR, 'dinov3_vith16plus', source='local', weights=) -dinov3_vit7b16 = torch.hub.load(REPO_DIR, 'dinov3_vit7b16', source='local', weights=) - -# DINOv3 ConvNeXt models pretrained on web images -dinov3_convnext_tiny = torch.hub.load(REPO_DIR, 'dinov3_convnext_tiny', source='local', weights=) -dinov3_convnext_small = torch.hub.load(REPO_DIR, 'dinov3_convnext_small', source='local', weights=) -dinov3_convnext_base = torch.hub.load(REPO_DIR, 'dinov3_convnext_base', source='local', weights=) -dinov3_convnext_large = torch.hub.load(REPO_DIR, 'dinov3_convnext_large', source='local', weights=) - -# DINOv3 ViT models pretrained on satellite imagery -dinov3_vitl16 = torch.hub.load(REPO_DIR, 'dinov3_vitl16', source='local', weights=) -dinov3_vit7b16 = torch.hub.load(REPO_DIR, 'dinov3_vit7b16', source='local', weights=) -``` - -### Pretrained backbones (via Hugging Face [Transformers](https://huggingface.co/docs/transformers/)) - -All the backbones are available in the the [DINOv3](https://huggingface.co/collections/facebook/dinov3-68924841bd6b561778e31009) collection on Hugging Face Hub and supported via the Hugging Face [Transformers](https://huggingface.co/docs/transformers/index) library. Please refer to the corresponding documentation for usage, but below is a short example that demonstrates how to obtain an image embedding with either [Pipeline] or the [AutoModel] class. - -```python -from transformers import pipeline -from transformers.image_utils import load_image - -url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg" -image = load_image(url) - -feature_extractor = pipeline( - model="facebook/dinov3-convnext-tiny-pretrain-lvd1689m", - task="image-feature-extraction", -) -features = feature_extractor(image) -``` - -```python -import torch -from transformers import AutoImageProcessor, AutoModel -from transformers.image_utils import load_image - -url = "http://images.cocodataset.org/val2017/000000039769.jpg" -image = load_image(url) - -pretrained_model_name = "facebook/dinov3-convnext-tiny-pretrain-lvd1689m" -processor = AutoImageProcessor.from_pretrained(pretrained_model_name) -model = AutoModel.from_pretrained( - pretrained_model_name, - device_map="auto", -) - -inputs = processor(images=image, return_tensors="pt").to(model.device) -with torch.inference_mode(): - outputs = model(**inputs) - -pooled_output = outputs.pooler_output -print("Pooled output shape:", pooled_output.shape) -``` - -where `model` and `pretrained_model_name` above can be one of: -- `facebook/dinov3-vits16-pretrain-lvd1689m` -- `facebook/dinov3-vits16plus-pretrain-lvd1689m` -- `facebook/dinov3-vitb16-pretrain-lvd1689m` -- `facebook/dinov3-vitl16-pretrain-lvd1689m` -- `facebook/dinov3-vith16plus-pretrain-lvd1689m` -- `facebook/dinov3-vit7b16-pretrain-lvd1689m` -- `facebook/dinov3-convnext-base-pretrain-lvd1689m` -- `facebook/dinov3-convnext-large-pretrain-lvd1689m` -- `facebook/dinov3-convnext-small-pretrain-lvd1689m` -- `facebook/dinov3-convnext-tiny-pretrain-lvd1689m` -- `facebook/dinov3-vitl16-pretrain-sat493m` -- `facebook/dinov3-vit7b16-pretrain-sat493m` - -### Image transforms - -For models using the LVD-1689M weights (pretrained on web images), please use the following transform (standard ImageNet evaluation transform): - -```python -import torchvision - -def make_transform(resize_size: int = 224): - to_tensor = transforms.ToTensor() - resize = transforms.Resize((resize_size, resize_size), antialias=True) - normalize = transforms.Normalize( - mean=(0.485, 0.456, 0.406), - std=(0.229, 0.224, 0.225), - ) - return transforms.Compose([to_tensor, resize, normalize]) -``` - - -For models using the SAT-493M weights (pretrained on satellite imagery), please use the following transform: - - -```python -import torchvision - -def make_transform(resize_size: int = 224): - to_tensor = transforms.ToTensor() - resize = transforms.Resize((resize_size, resize_size), antialias=True) - normalize = transforms.Normalize( - mean=(0.430, 0.411, 0.296), - std=(0.213, 0.156, 0.143), - ) - return transforms.Compose([to_tensor, resize, normalize]) -``` - -### Pretrained heads - Image classification - - - - - - - - - - - - - - - - - - -
BackbonePretraining
Dataset		Head
Dataset		Download
ViT-7B/16LVD-1689MImageNet[link]
- - -The (full) classifier models can be loaded via PyTorch Hub: - -```python -import torch - -# DINOv3 -dinov3_vit7b16_lc = torch.hub.load(REPO_DIR, 'dinov3_vit7b16_lc', source="local", weights=, backbone_weights=) - -``` - -### Pretrained heads - Depther trained on SYNTHMIX dataset - - - - - - - - - - - - - - - - - - -
BackbonePretraining
Dataset		Head
Dataset		Download
ViT-7B/16LVD-1689MSYNTHMIX[link]
- - -```python -depther = torch.hub.load(REPO_DIR, 'dinov3_vit7b16_dd', source="local", weights=, backbone_weights=) -``` - -Full example code of depther on an image - -```python -from PIL import Image -import torch -from torchvision import transforms -import matplotlib.pyplot as plt -from matplotlib import colormaps - -def get_img(): - import requests - url = "http://images.cocodataset.org/val2017/000000039769.jpg" - image = Image.open(requests.get(url, stream=True).raw).convert("RGB") - return image - -def make_transform(resize_size: int | list[int] = 768): - to_tensor = transforms.ToTensor() - resize = transforms.Resize((resize_size, resize_size), antialias=True) - normalize = transforms.Normalize( - mean=(0.485, 0.456, 0.406), - std=(0.229, 0.224, 0.225), - ) - return transforms.Compose([to_tensor, resize, normalize]) - -depther = torch.hub.load(REPO_DIR, 'dinov3_vit7b16_dd', source="local", weights=, backbone_weights=) - -img_size = 1024 -img = get_img() -transform = make_transform(img_size) -with torch.inference_mode(): - with torch.autocast('cuda', dtype=torch.bfloat16): - batch_img = transform(img)[None] - batch_img = batch_img - depths = depther(batch_img) - -plt.figure(figsize=(12, 6)) -plt.subplot(121) -plt.imshow(img) -plt.axis("off") -plt.subplot(122) -plt.imshow(depths[0,0].cpu(), cmap=colormaps["Spectral"]) -plt.axis("off") - -``` - -### Pretrained heads - Detector trained on COCO2017 dataset - - - - - - - - - - - - - - - - - - -
BackbonePretraining
Dataset		Head
Dataset		Download
ViT-7B/16LVD-1689MCOCO2017[link]
- - -```python -detector = torch.hub.load(REPO_DIR, 'dinov3_vit7b16_de', source="local", weights=, backbone_weights=) -``` - -### Pretrained heads - Segmentor trained on ADE20K dataset - - - - - - - - - - - - - - - - - - -
BackbonePretraining
Dataset		Head
Dataset		Download
ViT-7B/16LVD-1689MADE20K[link]
- -```python -segmentor = torch.hub.load(REPO_DIR, 'dinov3_vit7b16_ms', source="local", weights=, backbone_weights=) -``` - -Full example code of segmentator on an image - -```python -import sys -sys.path.append(REPO_DIR) - -from PIL import Image -import torch -from torchvision import transforms -import matplotlib.pyplot as plt -from matplotlib import colormaps -from functools import partial -from dinov3.eval.segmentation.inference import make_inference - - -def get_img(): - import requests - url = "http://images.cocodataset.org/val2017/000000039769.jpg" - image = Image.open(requests.get(url, stream=True).raw).convert("RGB") - return image - -def make_transform(resize_size: int | list[int] = 768): - to_tensor = transforms.ToTensor() - resize = transforms.Resize((resize_size, resize_size), antialias=True) - normalize = transforms.Normalize( - mean=(0.485, 0.456, 0.406), - std=(0.229, 0.224, 0.225), - ) - return transforms.Compose([to_tensor, resize, normalize]) - -segmentor = torch.hub.load(REPO_DIR, 'dinov3_vit7b16_ms', source="local", weights=, backbone_weights=) - -img_size = 896 -img = get_img() -transform = make_transform(img_size) -with torch.inference_mode(): - with torch.autocast('cuda', dtype=torch.bfloat16): - batch_img = transform(img)[None] - pred_vit7b = segmentor(batch_img) # raw predictions - # actual segmentation map - segmentation_map_vit7b = make_inference( - batch_img, - segmentor, - inference_mode="slide", - decoder_head_type="m2f", - rescale_to=(img.size[-1], img.size[-2]), - n_output_channels=150, - crop_size=(img_size, img_size), - stride=(img_size, img_size), - output_activation=partial(torch.nn.functional.softmax, dim=1), - ).argmax(dim=1, keepdim=True) -plt.figure(figsize=(12, 6)) -plt.subplot(121) -plt.imshow(img) -plt.axis("off") -plt.subplot(122) -plt.imshow(segmentation_map_vit7b[0,0].cpu(), cmap=colormaps["Spectral"]) -plt.axis("off") -``` - - - - -### Pretrained heads - Zero-shot tasks with `dino.txt` - - - - - - - - - - - - - - -
BackboneDownload
ViT-L/16 distilled - [link], - vocabulary, - vocabulary license -
- -The (full) dino.txt model can be loaded via PyTorch Hub: - -```python -import torch -# DINOv3 -dinov3_vitl16_dinotxt_tet1280d20h24l, tokenizer = torch.hub.load(REPO_DIR, 'dinov3_vitl16_dinotxt_tet1280d20h24l', weights=, backbone_weights=) -``` - - -## Installation - -The training and evaluation code requires PyTorch version >= 2.7.1 as well as a few 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: - -*[micromamba](https://mamba.readthedocs.io/en/latest/user_guide/micromamba.html)* **(Recommended)** - Clone the repository and then create and activate a `dinov3` conda environment using the provided environment definition: - -```shell -micromamba env create -f conda.yaml -micromamba activate dinov3 -``` - -## Getting started - -Several notebooks are provided to get started applying DINOv3: -- [PCA of patch features](notebooks/pca.ipynb): display the PCA of DINOv3 patch features on a foreground object (rainbow visualizations from the paper) [[Run in Google Colab]](https://colab.research.google.com/github/facebookresearch/dinov3/blob/main/notebooks/pca.ipynb) -- [Foreground segmentation](notebooks/foreground_segmentation.ipynb): train a linear foreground segmentation model based on DINOv3 features [[Run in Google Colab]](https://colab.research.google.com/github/facebookresearch/dinov3/blob/main/notebooks/foreground_segmentation.ipynb) -- [Dense and sparse matching](notebooks/dense_sparse_matching.ipynb): match patches from objects on two different images based on DINOv3 features [[Run in Google Colab]](https://colab.research.google.com/github/facebookresearch/dinov3/blob/main/notebooks/dense_sparse_matching.ipynb) -- [Segmentation tracking](notebooks/segmentation_tracking.ipynb): video segmentation tracking using a non-parametric method based on DINOv3 features [[Run in Google Colab]](https://colab.research.google.com/github/facebookresearch/dinov3/blob/main/notebooks/segmentation_tracking.ipynb) - -## Data preparation - -### ImageNet-1k - -The root directory of the dataset should hold the following contents: - -- `/test/ILSVRC2012_test_00000001.JPEG` -- `/test/[..]` -- `/test/ILSVRC2012_test_00100000.JPEG` -- `/train/n01440764/n01440764_10026.JPEG` -- `/train/[...]` -- `/train/n15075141/n15075141_9993.JPEG` -- `/val/n01440764/ILSVRC2012_val_00000293.JPEG` -- `/val/[...]` -- `/val/n15075141/ILSVRC2012_val_00049174.JPEG` -- `/labels.txt` - -The provided dataset implementation expects a few additional metadata files to be present under the extra directory: - -- `/class-ids-TRAIN.npy` -- `/class-ids-VAL.npy` -- `/class-names-TRAIN.npy` -- `/class-names-VAL.npy` -- `/entries-TEST.npy` -- `/entries-TRAIN.npy` -- `/entries-VAL.npy` - -These metadata files can be generated (once) with the following lines of Python code: - -```python -from dinov3.data.datasets import ImageNet - -for split in ImageNet.Split: - dataset = ImageNet(split=split, root="", 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](dinov3/data/datasets/image_net_22k.py) to match your local setup. - -
- -:warning: To execute the commands provided in the next sections for training and evaluation, the `dinov3` package should be included in the Python module search path, i.e. simply prefix the command to run with `PYTHONPATH=.`. - -## Training - -### Fast setup: training DINOv3 ViT-L/16 on ImageNet-1k - -Run DINOv3 pre-training on 4 H100-80GB nodes (32 GPUs) in a SLURM cluster environment with submitit: - -```shell - PYTHONPATH=${PWD} python -m dinov3.run.submit dinov3/train/train.py \ - --nodes 4 \ - --config-file dinov3/configs/train/vitl_im1k_lin834.yaml \ - --output-dir \ - train.dataset_path=ImageNet22k:root=:extra= -``` -Training time is approximately 14 hours and the resulting checkpoint should reach 82.0% on k-NN eval and 83.5% on linear eval. - -The training code saves the weights of the teacher in the eval folder every 12500 iterations for evaluation. - -### Exact DINOv3 setup: training DINOv3 ViT-7B/16 - -DINOv3 ViT-7B/16 is trained on a private dataset. The training involves 3 stages: -- Pretraining -- Gram anchoring -- High resolution adaptation - -#### Pretraining - -Launch DINOV3 ViT-7B/16 pretraining on 32 nodes (256 GPUs) in a SLURM cluster environment with submitit. - -```shell -PYTHONPATH=${PWD} python -m dinov3.run.submit dinov3/train/train.py \ - --nodes 32 \ - --config-file dinov3/configs/train/dinov3_vit7b16_pretrain.yaml \ - --output-dir \ - train.dataset_path=:root=:extra= -``` - -#### Gram anchoring - -```shell -PYTHONPATH=${PWD} python -m dinov3.run.submit dinov3/train/train.py \ - --nodes 32 \ - --config-file dinov3/configs/train/dinov3_vit7b16_gram_anchor.yaml \ - --output-dir \ - train.dataset_path=:root=:extra= \ - gram.ckpt= -``` - -#### High-resolution adaptation - - -```shell -PYTHONPATH=${PWD} python -m dinov3.run.submit dinov3/train/train.py \ - --nodes 32 \ - --config-file dinov3/configs/train/dinov3_vit7b16_high_res_adapt.yaml \ - --output-dir \ - train.dataset_path=:root=:extra= \ - gram.ckpt= \ - student.resume_from_teacher_chkpt= -``` - -## Multi-distillation - -### Test setup: - -```shell -PYTHONPATH=${PWD} python -m dinov3.run.submit dinov3/train/train.py \ - --nodes 1 \ - --config-file dinov3/configs/train/multi_distillation_test.yaml \ - --output-dir \ - --multi-distillation \ - train.dataset_path=:root=:extra= -``` - -## Evaluation - -The training code regularly saves the teacher weights. In order to evaluate the model, run the following evaluation on a single node: - - -### Logistic regression classification on ImageNet-1k - -```shell -PYTHONPATH=${PWD} python -m dinov3.run.submit dinov3/eval/log_regression.py \ - model.config_file=/config.yaml \ - model.pretrained_weights=/teacher_checkpoint.pth \ - output_dir= \ - train.dataset=ImageNet:split=TRAIN:root=:extra= \ - eval.test_dataset=ImageNet:split=VAL:root=:extra= -``` - -### k-NN classification on ImageNet-1k - -```shell -PYTHONPATH=${PWD} python -m dinov3.run.submit dinov3/eval/knn.py \ - model.config_file=/config.yaml \ - model.pretrained_weights=/teacher_checkpoint.pth \ - output_dir= \ - train.dataset=ImageNet:split=TRAIN:root=:extra= \ - eval.test_dataset=ImageNet:split=VAL:root=:extra= -``` - -### Linear classification with data augmentation on ImageNet-1k - -```shell -PYTHONPATH=${PWD} python -m dinov3.run.submit dinov3/eval/linear.py \ - model.config_file=/config.yaml \ - model.pretrained_weights=/teacher_checkpoint.pth \ - output_dir= \ - train.dataset=ImageNet:split=TRAIN:root=:extra= \ - train.val_dataset=ImageNet:split=VAL:root=:extra= -``` - - -### Text alignment on DINOv3 using dino.txt - -Text alignment can be done following the method from `dino.txt` aka [DINOv2 Meets Text](https://arxiv.org/abs/2412.16334). - -```shell -PYTHONPATH=${PWD} python -m dinov3.run.submit dinov3/eval/text/train_dinotxt.py \ - --nodes 4 \ - # An example config for text alignment is here: dinov3/eval/text/configs/dinov3_vitl_text.yaml \ - trainer_config_file="" \ - output-dir= -``` -Launching the above trains text alignment on 4 nodes with 8 gpus each (32 gpus in total). -Please note that the text alignment model in the DINOv3 paper was trained on a private dataset and here we have given an example config in ```dinov3/eval/text/configs/dinov3_vitl_text.yaml``` using ```CocoCaptions``` dataset for illustration purposes. -Please adapt the provided ```CocoCaptions``` dataset class, the dataset can be found [here](https://www.kaggle.com/datasets/nikhil7280/coco-image-caption) - -## License - -DINOv3 code and model weights are released under the DINOv3 License. See [LICENSE.md](LICENSE.md) for additional details. - -## Contributing - -See [contributing](CONTRIBUTING.md) and the [code of conduct](CODE_OF_CONDUCT.md). - -## Citing DINOv3 - -If you find this repository useful, please consider giving a star :star: and citation :t-rex:: - -``` -@misc{simeoni2025dinov3, - title={{DINOv3}}, - author={Sim{\'e}oni, Oriane and Vo, Huy V. and Seitzer, Maximilian and Baldassarre, Federico and Oquab, Maxime and Jose, Cijo and Khalidov, Vasil and Szafraniec, Marc and Yi, Seungeun and Ramamonjisoa, Micha{\"e}l and Massa, Francisco and Haziza, Daniel and Wehrstedt, Luca and Wang, Jianyuan and Darcet, Timoth{\'e}e and Moutakanni, Th{\'e}o and Sentana, Leonel and Roberts, Claire and Vedaldi, Andrea and Tolan, Jamie and Brandt, John and Couprie, Camille and Mairal, Julien and J{\'e}gou, Herv{\'e} and Labatut, Patrick and Bojanowski, Piotr}, - year={2025}, - eprint={2508.10104}, - archivePrefix={arXiv}, - primaryClass={cs.CV}, - url={https://arxiv.org/abs/2508.10104}, -} -``` diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/__pycache__/hubconf.cpython-310.pyc b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/__pycache__/hubconf.cpython-310.pyc deleted file mode 100644 index 03b79bd3129683d3afb60413201b7cbed3c7272f..0000000000000000000000000000000000000000 Binary files a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/__pycache__/hubconf.cpython-310.pyc and /dev/null differ diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/conda.yaml b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/conda.yaml deleted file mode 100644 index db4c17be5c918075002b11c48abbef4a9d3043cd..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/conda.yaml +++ /dev/null @@ -1,23 +0,0 @@ -name: dinov3 -channels: - - defaults - - conda-forge -dependencies: - - python=3.11 - - omegaconf - - pip - - pip: - - ftfy # needed for dino.txt - - iopath - - omegaconf - - pandas - - regex # needed for dino.txt - - pandas - - scikit-learn - - scikit-learn-intelex - - submitit - - termcolor - - torch - - torchvision - - torchmetrics - diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/__init__.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/__init__.py deleted file mode 100644 index b70f615df55246ea150f0c76e02b0b8b98d16b2b..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/__init__.py +++ /dev/null @@ -1,6 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -__version__ = "0.0.1" diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/__pycache__/__init__.cpython-310.pyc b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/__pycache__/__init__.cpython-310.pyc deleted file mode 100644 index 0e4401b4f87403ce13f9def12df532f9241321ce..0000000000000000000000000000000000000000 Binary files a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/__pycache__/__init__.cpython-310.pyc and /dev/null differ diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/checkpointer/__init__.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/checkpointer/__init__.py deleted file mode 100644 index 120712ccd2ec68848a027ca2a54b662f4e109205..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/checkpointer/__init__.py +++ /dev/null @@ -1,18 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -from .checkpointer import ( - CheckpointRetentionPolicy, - cleanup_checkpoint, - find_all_checkpoints, - find_latest_checkpoint, - init_fsdp_model_from_checkpoint, - init_model_from_checkpoint_for_evals, - keep_checkpoint_copy, - keep_last_n_checkpoints, - load_checkpoint, - register_dont_save_hooks, - save_checkpoint, -) diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/checkpointer/checkpointer.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/checkpointer/checkpointer.py deleted file mode 100644 index 7a984110c68be859f5612be5d290bf27d5a77a55..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/checkpointer/checkpointer.py +++ /dev/null @@ -1,352 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -""" -Suggested file structure: - -output_dir/ -|-- ckpt/ -| |-- 0/ -| |-- 99/ -| |-- 199/ -| |-- 199_keep/ -| |-- 299/ -| `-- ... -`-- eval/ - `-- 0/ - `-- 99/ - `-- ckpt/ - -Distributed checkpointer docs: -- https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html -- https://pytorch.org/docs/stable/distributed.checkpoint.html -""" - -import logging -import shutil -import subprocess -import tempfile -from enum import Enum -from pathlib import Path -from typing import List, Sequence, Set - -import torch -import torch.distributed as dist -import torch.distributed.checkpoint as dcp -import torch.distributed.checkpoint.filesystem as dcpfs -import torch.distributed.checkpoint.state_dict as dcpsd -from torch.distributed.checkpoint.stateful import Stateful - -logger = logging.getLogger("dinov3") - - -class CheckpointRetentionPolicy(Enum): - ALL = "all" # keep all checkpoints - BEST = "best" - LAST = "last" - LAST_AND_BEST = "last_and_best" - NONE = "none" # do not keep any checkpoints - - @property - def keep_filters(self) -> Set[str]: - """Files that match these patterns are not deleted by cleanup""" - if self == CheckpointRetentionPolicy.LAST: - return set(["final"]) - if self == CheckpointRetentionPolicy.BEST: - return set(["best"]) - if self == CheckpointRetentionPolicy.LAST_AND_BEST: - return set(["final", "best"]) - if self == CheckpointRetentionPolicy.ALL: - return set() - return set() - - @property - def max_to_keep(self) -> int | None: - """ - maximum "periodic" checkpoints to keep concurrently, ie. saved with `step` and not `save`. `None` for keep all - """ - if self == CheckpointRetentionPolicy.ALL: - return None - return 1 - - -def save_checkpoint( - ckpt_dir: str | Path, # output_dir/ckpt/199 - *, - iteration: int | str, - model: torch.nn.Module, - optimizer: torch.optim.Optimizer | None = None, - overwrite: bool = True, - process_group: dist.ProcessGroup = None, - **others: Stateful, -): - """Save a plain/DDP/FSDP/FSDP2 model, its optimizer, an integer iteration and other stateful objects.""" - rank = torch.distributed.get_rank(group=process_group) - - # Rank 0 checks if the checkpoint directory exists, but all ranks need to know if if exists, - # so they can raise an error when overwrite is False. If overwrite is True, rank 0 will delete it - # and other ranks wait for the deletion to finish. - ckpt_dir = Path(ckpt_dir) - ckpt_dir_exists = [ckpt_dir.exists() if rank == 0 else None] - src_rank = 0 - if process_group is not None: - src_rank = torch.distributed.get_global_rank(group=process_group, group_rank=0) - torch.distributed.broadcast_object_list(ckpt_dir_exists, src=src_rank, group=process_group) - ckpt_dir_exists = ckpt_dir_exists[0] - if ckpt_dir_exists: - if overwrite: - if rank == 0: - if ckpt_dir.is_dir(): - shutil.rmtree(ckpt_dir) - else: - ckpt_dir.unlink() - logger.info(f"Deleted: {ckpt_dir}") - torch.distributed.barrier(group=process_group) - else: - raise RuntimeError(f"Checkpoint already exists: {ckpt_dir}") - - # Rank 0 creates a temporary directory for the checkpoint and broadcasts the name to all ranks. - ckpt_dir.parent.mkdir(parents=True, exist_ok=True) - ckpt_dir_tmp = [tempfile.mkdtemp(dir=ckpt_dir.parent, prefix=ckpt_dir.name) if rank == 0 else None] - torch.distributed.broadcast_object_list(ckpt_dir_tmp, src=src_rank, group=process_group) - ckpt_dir_tmp = Path(ckpt_dir_tmp[0]) - - to_save = {"iteration": iteration} - to_save["model"] = dcpsd.get_model_state_dict(model) - if optimizer is not None: - to_save["optimizer"] = dcpsd.get_optimizer_state_dict(model, optimizer) - to_save.update(others) - dcp.save( - to_save, - storage_writer=dcpfs.FileSystemWriter(ckpt_dir_tmp), - process_group=process_group, - ) - - # Rank 0 renames the temporary directory to the final checkpoint directory. All ranks wait for the rename. - if rank == 0: - ckpt_dir_tmp.rename(ckpt_dir) - torch.distributed.barrier() - - logger.info(f"Saved: {ckpt_dir}") - - -def load_checkpoint( - ckpt_dir: str | Path, # output_dir/ckpt/199 - *, - model: torch.nn.Module, - optimizer: torch.optim.Optimizer | None = None, - strict_loading: bool = True, - process_group: dist.ProcessGroup = None, - **others: Stateful, -) -> int | None: - """ - Load a plain/DDP/FSDP/FSDP2 model, its optimizer, an integer iteration and other stateful objects. - Can you take a checkpoint saved on N ranks and load it on M ranks? Sure you can! - Activation checkpointing and torch-compile can also be different between save and load, no problem. - """ - ckpt_dir = Path(ckpt_dir) - to_load = {"iteration": None} - to_load["model"] = dcpsd.get_model_state_dict(model) - if optimizer is not None: - to_load["optimizer"] = dcpsd.get_optimizer_state_dict(model, optimizer) - to_load.update(others) - dcp.load( - to_load, - storage_reader=dcpfs.FileSystemReader(ckpt_dir), - planner=dcp.default_planner.DefaultLoadPlanner(allow_partial_load=not strict_loading), - process_group=process_group, - ) - iteration = to_load["iteration"] - dcpsd.set_model_state_dict(model, to_load["model"]) - if optimizer is not None: - dcpsd.set_optimizer_state_dict(model, optimizer, to_load["optimizer"]) - logger.info(f"Loaded: {ckpt_dir}") - return iteration - - -def register_dont_save_hooks(module: torch.nn.Module, dont_save: Sequence[str]): - """ - Registers save/load state dict hooks such that the weights in `dont_save` are not persisted in the checkpoint. - - Typical use case: a classification model composed of a frozen backbone and a trainable head. - If the frozen backbone is loaded from torch hub, it does't make sense to save a copy of it in each checkpoint. - """ - - def state_dict_post_hook(module, state_dict, prefix, local_metadata): - # Remove frozen weights so they won't get saved. - # If this module is not the top-level module, its weights will have a prefix in the state dict. - nonlocal _dont_save - for k in _dont_save: - del state_dict[prefix + k] - - def load_state_dict_pre_hook( - module, - state_dict, - prefix, - local_metadata, - strict, - missing_keys, - unexpected_keys, - error_msgs, - ): - # This pre hook exists only to pass the prefix to the post hook when loading the state dict. - nonlocal _prefix - assert _prefix is None - _prefix = prefix - - def load_state_dict_post_hook(module, incompatible_keys): - # Remove the frozen weights from the missing keys so they don't raise an error. - nonlocal _prefix - assert _prefix is not None - to_remove = [] - for missing_key in incompatible_keys.missing_keys: - k = missing_key.removeprefix(_prefix) - k = k.replace("_checkpoint_wrapped_module.", "") # Added by activation checkpointing - if k in _dont_save: - to_remove.append(missing_key) - for r in to_remove: - incompatible_keys.missing_keys.remove(r) - _prefix = None - - _dont_save = set(name.replace("_checkpoint_wrapped_module.", "") for name in dont_save) - _prefix = None - module.register_state_dict_post_hook(state_dict_post_hook) - module.register_load_state_dict_pre_hook(load_state_dict_pre_hook) - module.register_load_state_dict_post_hook(load_state_dict_post_hook) - - -def find_all_checkpoints(ckpt_dir: Path | str) -> list[Path]: - """Find all checkpoints in a directory, i.e. subdirs with integer name. Sorted from first to last.""" - ckpt_dir = Path(ckpt_dir) - if not ckpt_dir.is_dir(): - return [] - checkpoints = [p for p in ckpt_dir.iterdir() if p.is_dir() and _is_int(p.name)] - checkpoints.sort(key=lambda p: int(p.name)) - return checkpoints - - -def find_latest_checkpoint(ckpt_dir: Path | str) -> Path | None: - """Find the latest checkpoint in a directory, i.e. the subdir with the highest integer name.""" - checkpoints = find_all_checkpoints(ckpt_dir) - if len(checkpoints) == 0: - return None - return checkpoints[-1] - - -def keep_last_n_checkpoints(ckpt_dir: Path | str, n: int | None): - """In a directory with integer-named subdirs, keep only the n subdirs with the highest number.""" - if n is None: - return - checkpoints = find_all_checkpoints(ckpt_dir) - for ckpt_dir in checkpoints[:-n]: - try: - shutil.rmtree(ckpt_dir) - logger.info(f"Deleted: {ckpt_dir}") - except Exception: - logger.exception(f"Failed to delete: {ckpt_dir}") - - -def keep_checkpoint_copy(src: Path | str): - """Copy a file/directory next to itself with a _keep suffix. Files are hardlinked.""" - src = Path(src) - dst = src.parent / f"{src.name}_keep" - subprocess.check_output(["cp", "--recursive", "--link", src, dst]) - logger.info(f"Copied: {src} -> {dst}") - - -def _is_int(s: str) -> bool: - try: - int(s) - return True - except ValueError: - return False - - -# Initialize a FSDP2 model from DCP or PyTorch standard checkpoint -def init_fsdp_model_from_checkpoint( - model: torch.nn.Module, - checkpoint_path: str, - skip_load_keys: List[str] | None = None, - keys_not_sharded: List[str] | None = None, - process_group: dist.ProcessGroup = None, -): - if not Path(checkpoint_path).is_dir(): # PyTorch standard checkpoint - logger.info(f"Loading pretrained weights from {checkpoint_path}") - chkpt = torch.load(checkpoint_path, map_location="cpu")["teacher"] - from torch.distributed.device_mesh import DeviceMesh, init_device_mesh - - if process_group is None: - world_mesh = init_device_mesh( - "cuda", - mesh_shape=(dist.get_world_size(),), - mesh_dim_names=("dp",), - ) - else: - world_mesh = DeviceMesh.from_group(process_group, "cuda") - chkpt = { - key: ( - torch.distributed.tensor.distribute_tensor(tensor, world_mesh, src_data_rank=None) - if not any(key_not_sharded in key for key_not_sharded in keys_not_sharded) - else tensor - ) - for key, tensor in chkpt.items() - } - model.load_state_dict( - { - key: tensor - for key, tensor in chkpt.items() - if not any(skip_load_key in key for skip_load_key in skip_load_keys) - } - ) - else: # DCP checkpoint - load_checkpoint(ckpt_dir=checkpoint_path, model=model, process_group=process_group) - - -# Initialize a standard non distributed PyTorch model from PyTorch standard checkpoint for evals -def init_model_from_checkpoint_for_evals( - model: torch.nn.Module, pretrained_weights: str | Path, checkpoint_key: str = None -): - state_dict = torch.load(pretrained_weights, map_location="cpu") - if checkpoint_key is not None and checkpoint_key in state_dict: - logger.info(f"Take key {checkpoint_key} in provided checkpoint dict") - state_dict = state_dict[checkpoint_key] - # remove `module.` prefix - state_dict = {k.replace("module.", ""): v for k, v in state_dict.items()} - # remove `backbone.` prefix induced by multicrop wrapper - state_dict = {k.replace("backbone.", ""): v for k, v in state_dict.items()} - msg = model.load_state_dict(state_dict, strict=False) - logger.info("Pretrained weights found at {} and loaded with msg: {}".format(pretrained_weights, msg)) - - -def cleanup_checkpoint(ckpt_dir: str, checkpoint_retention_policy: CheckpointRetentionPolicy): - """ - ckpt_dir is the directory containing each individual checkpoint directories (either at iteration, best (validation performance) or final) - |-- ckpt_dir/ - | |-- 0/ - | |--checkpoint.pth or dcp_sharded_checkpoint_dir - | |-- 99/ - |--checkpoint.pth or dcp_sharded_checkpoint_dir - | |-- 199/ - |--checkpoint.pth or dcp_sharded_checkpoint_dir - | |-- best/ - |--checkpoint.pth or dcp_sharded_checkpoint_dir - | |-- 299/ - |--checkpoint.pth or dcp_sharded_checkpoint_dir - | |-- final/ - |--checkpoint.pth or dcp_sharded_checkpoint_dir - """ - ckpt_dir = Path(ckpt_dir) - if not ckpt_dir.is_dir(): - return [] - checkpoint_filters = checkpoint_retention_policy.keep_filters - checkpoints = [p for p in ckpt_dir.iterdir() if p.is_dir()] - for checkpoint in checkpoints: - if checkpoint in checkpoint_filters: - continue - try: - shutil.rmtree(checkpoint) - logger.info(f"Deleted: {checkpoint}") - except Exception: - logger.exception(f"Failed to delete: {checkpoint}") diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/configs/__init__.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/configs/__init__.py deleted file mode 100644 index 3719887dfb7c33ad67c2546e33e2f1c9f426760c..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/configs/__init__.py +++ /dev/null @@ -1,16 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -from .config import ( - DinoV3SetupArgs, - apply_scaling_rules_to_cfg, - exit_job, - get_cfg_from_args, - get_default_config, - setup_config, - setup_job, - setup_multidistillation, - write_config, -) diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/configs/config.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/configs/config.py deleted file mode 100644 index 6aceaa86f9b952cc554cb6ae80ed16a242d67d0e..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/configs/config.py +++ /dev/null @@ -1,222 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import logging -import math -import os -import pathlib -import sys -from dataclasses import dataclass, field -from datetime import timedelta -from typing import Any, List, Optional, Sequence, Tuple - -from omegaconf import DictConfig, OmegaConf - -import dinov3.distributed as distributed -from dinov3.logging import cleanup_logging, setup_logging -from dinov3.utils import fix_random_seeds, get_conda_env, get_sha - -logger = logging.getLogger("dinov3") - - -@dataclass -class DinoV3SetupArgs: - config_file: str - pretrained_weights: str | None = None - shard_unsharded_model: bool = False - output_dir: str = "" - opts: List[Any] = field(default_factory=lambda: []) - - def __post_init__(self): - # When loaded from benchmark.yaml, self.opts is a frozen omegaconf.ListConfig, - # which works everywhere except when we want to modify it or when - # we try to json-serialize it. So we convert it to a regular list here. - if OmegaConf.is_config(self.opts): - self.opts = OmegaConf.to_object(self.opts) - - -def apply_scaling_rules_to_cfg(cfg): # to fix - assert distributed.is_enabled(), "Setup distributed to get global size !" - if "schedules" in cfg: - # For schedules v2, the scaling rules are applied when building the schedules, the config is not modified - return cfg - - if cfg.optim.scaling_rule == "linear_wrt_256": - old_lr = cfg.optim.lr - cfg.optim.lr *= cfg.train.batch_size_per_gpu * distributed.get_world_size() / 256.0 - logger.info(f"linear scaling learning rate; old: {old_lr}, new: {cfg.optim.lr}") - elif cfg.optim.scaling_rule == "sqrt_wrt_1024": - old_lr = cfg.optim.lr - cfg.optim.lr *= 4 * math.sqrt(cfg.train.batch_size_per_gpu * distributed.get_world_size() / 1024.0) - logger.info(f"sqrt scaling learning rate; old: {old_lr}, new: {cfg.optim.lr}") - return cfg - - -def write_config(cfg, output_dir, name="config.yaml"): - logger.info(OmegaConf.to_yaml(cfg)) - output_dir = os.path.abspath(output_dir) - saved_cfg_path = os.path.join(output_dir, name) - with open(saved_cfg_path, "w") as f: - OmegaConf.save(config=cfg, f=f) - return saved_cfg_path - - -def get_default_config() -> DictConfig: - p = pathlib.Path(__file__).parent / "ssl_default_config.yaml" - return OmegaConf.load(p) - - -def get_cfg_from_args(args: DinoV3SetupArgs, multidistillation=False, strict=True): - overrides = [*args.opts] - if args.output_dir is not None: - overrides.append(f"train.output_dir={os.path.realpath(args.output_dir)}") - - # Config file - cfg = OmegaConf.load(args.config_file) - - # Command line overrides - opts_cfg = OmegaConf.from_cli(overrides) - - if multidistillation: - cfg = OmegaConf.merge(cfg, opts_cfg) - else: - # Default config - default_cfg = get_default_config() - if strict: - OmegaConf.set_struct(default_cfg, True) - cfg = OmegaConf.merge(default_cfg, cfg, opts_cfg) - return cfg - - -def setup_config(args: DinoV3SetupArgs, strict_cfg=True): - """ - Create configs and perform basic setups. - """ - # Create the cfg with OmegaConf - cfg = get_cfg_from_args(args, strict=strict_cfg) - # setup distributed, logging, and random seeds - logger.info("\n".join("%s: %s" % (k, str(v)) for k, v in sorted(dict(vars(args)).items()))) - # dump config before modifying so it can be reloaded - if args.output_dir is not None: - write_config(cfg, args.output_dir) - # modify the config inplace by applying scaling rules - apply_scaling_rules_to_cfg(cfg) - return cfg - - -def _enumerate_all_subgroup_ranks(all_subgroup_rank_spans: Sequence[Tuple[int, int]]): - """Expands a specification of process subgroups from spans to enumerated ranks. - - Args: - all_group_rank_spans: a sequence of rank spans (first rank, last rank), - one for each process group. Example: ((0, 1), (2, 3), (4, 7)). - """ - for first, last in all_subgroup_rank_spans: - assert first <= last - return tuple(tuple(range(first, last + 1)) for first, last in all_subgroup_rank_spans) - - -def setup_multidistillation(args: DinoV3SetupArgs): - base_output_dir = args.output_dir - os.makedirs(args.output_dir, exist_ok=True) - # get config file for this rank - base_cfg = OmegaConf.load(args.config_file) - assert base_cfg.multidistillation.enabled - - global_batch_size = base_cfg.multidistillation.global_batch_size - - distributed.enable(overwrite=True) - seed = getattr(args, "seed", 0) - rank = distributed.get_rank() - - # build process subgroups - all_subgroup_rank_spans = tuple( - (student.ranks_range[0], student.ranks_range[1] - 1) for student in base_cfg.multidistillation.students - ) - all_subgroup_ranks = _enumerate_all_subgroup_ranks(all_subgroup_rank_spans) - distributed.new_subgroups(all_subgroup_ranks) - - found = False - for student in base_cfg.multidistillation.students: - if rank in range(*student.ranks_range): - found = True - break - assert found, "rank of worker not in defined range" - - name = student.name - config_path = student.config_path - n_gpus = student.ranks_range[1] - student.ranks_range[0] - assert global_batch_size % n_gpus == 0 - total_n_gpus = distributed.get_world_size() - - args.output_dir = os.path.join(base_output_dir, name) - args.opts += [f"train.output_dir={args.output_dir}"] - args.opts += [f"train.batch_size_per_gpu={global_batch_size // total_n_gpus}"] - args.config_file = os.path.abspath(config_path) - default_cfg = get_default_config() - cfg = OmegaConf.load(args.config_file) - cfg = OmegaConf.merge(default_cfg, cfg, base_cfg, OmegaConf.from_cli(args.opts)) - - global logger - setup_logging(output=args.output_dir, level=logging.INFO) - - fix_random_seeds(seed + rank) - - write_config(cfg, args.output_dir) - apply_scaling_rules_to_cfg(cfg) - - return cfg - - -def setup_job( - output_dir: Optional[str] = None, - distributed_enabled: bool = True, - logging_enabled: bool = True, - seed: Optional[int] = 0, - restrict_print_to_main_process: bool = True, - distributed_timeout: timedelta | None = None, -): - """ - Setup methods that should be done in every fairvit job - Initializes logging, distributed, random seeds and other utilities. - """ - if output_dir is not None: - output_dir = os.path.realpath(output_dir) - os.makedirs(output_dir, exist_ok=True) - - if logging_enabled: - setup_logging( - output=output_dir, - level=logging.INFO, - log_to_stdout_only_in_main_process=restrict_print_to_main_process, - ) - - if distributed_enabled: - distributed.enable( - overwrite=True, - nccl_async_error_handling=True, - restrict_print_to_main_process=restrict_print_to_main_process, - timeout=distributed_timeout, - ) - - if seed is not None: - rank = distributed.get_rank() - fix_random_seeds(seed + rank) - - logger = logging.getLogger("dinov3") - logger.info("git:\n {}\n".format(get_sha())) - - # Log some python info - conda_env_name, conda_env_path = get_conda_env() - logger.info(f"conda env name: {conda_env_name}") - logger.info(f"conda env path: {conda_env_path}") - logger.info(f"python path: {sys.path}") - - -def exit_job(distributed_enabled: bool = True, logging_enabled: bool = True): - if distributed_enabled: - distributed.disable() - if logging_enabled: - cleanup_logging() diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/configs/ssl_default_config.yaml b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/configs/ssl_default_config.yaml deleted file mode 100644 index 87ae3a0a33887c8f501d4d94f724f06942bab2a5..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/configs/ssl_default_config.yaml +++ /dev/null @@ -1,205 +0,0 @@ -MODEL: - META_ARCHITECTURE: SSLMetaArch - DEVICE: cuda - WEIGHTS: '' - DTYPE: float32 -compute_precision: - param_dtype: bf16 - reduce_dtype: fp32 - sharding_strategy: SHARD_GRAD_OP -dino: - loss_weight: 1.0 - global_ignore_diagonal: true # Whether to ignore A-A and B-B global pairs, default as in DINOv2, ignored by SSLMetaArch - head_n_prototypes: 65536 - head_bottleneck_dim: 256 - head_norm_last_layer: false - head_nlayers: 3 - head_hidden_dim: 2048 - koleo_loss_weight: 0.1 - koleo_loss_distributed: false - koleo_topk: 1 - koleo_distributed_replicas: 0 - koleo_distributed_loss_group_size: null # Size of the nearest neighbor set for distributed Koleo. If None, uses global batch size. - koleo_distributed_loss_group_data: true # group data from adjacent ranks to make sure koleo is applied on the same data distribution - force_weight_norm: false - reweight_dino_local_loss: false # If true, reweighting of DINO loss - local_loss_weight_schedule: # Schedule for local loss weight, enabled if reweight_dino_local_loss is true - start: 0.5 - peak: 0.5 - end: 0.5 - warmup_epochs: 0 -ibot: - loss_weight: 1.0 - mask_sample_probability: 0.5 - mask_ratio_min_max: - - 0.1 - - 0.5 - mask_random_circular_shift: false - force_masking_even_with_zero_weight: False - separate_head: true - head_n_prototypes: 65536 - head_bottleneck_dim: 256 - head_norm_last_layer: false - head_nlayers: 3 - head_hidden_dim: 2048 -gram: - use_loss: false # (bool) if true gram is used, else not - compute_stats: false # (bool): if true compute auxilliary stats - loss_weight: 1.0 # (float): weight of the loss - ema_teacher: false # (bool): using the EMA teacher as GRAM teacher - ckpt: null #(str): Checkpoint to the teacher - it_load_ema_teacher: -1 # (int): iteration at which the ema teacher is loaded into the gram teacher - rep_update: true # (bool): if true GRAM teacher updated every gram.update_frequency after iter gram.it_first_update steps - update_frequency: 50000 # (int): update frequency - it_first_update: 0 # (int): iteration of the first update - max_updates: null # (int): maximum number of updates to gram teacher. If None, it is unlimited - normalized: true # (bool): normalization of the features - img_level: false # (bool): if true GRAM computation at the image else, otherwise at the local batch level - remove_neg: false # (bool): if true remove the negative similarities before applying the loss - remove_only_teacher_neg: false # (bool): remove negative similarities of the teacher - tokens_used: all # (str): In [all, masked, unmasked] - global_teacher_resize_method: bicubic # Method for resizing the outputs of the gram teacher - global_teacher_resize_antialias: false # Whether to use antialiasing when resizing the outputs of the gram teacher - loss_weight_schedule: null # (dict): If not None, use a schedule for the loss weight instead of `loss_weight` -train: - batch_size_per_gpu: 64 - dataset_path: ImageNet:split=TRAIN - data_config: null - output_dir: . - saveckp_freq: 20 - seed: 0 - num_workers: 10 - OFFICIAL_EPOCH_LENGTH: 1250 - monitor_gradient_norm: false - chunk_schedule: [] - use_teacher_head: true - learn_from_teacher_tokens: false - centering: "sinkhorn_knopp" # or "sinkhorn_knopp" - checkpointing: false - checkpointing_full: false # aggressive checkpointing - compile: true - cudagraphs: false - sharded_eval_checkpoint: false - cache_dataset: false -student: - arch: vit_large - patch_size: 16 - drop_path_rate: 0.3 - layerscale: 1.0e-05 - pretrained_weights: '' - ffn_layer: "mlp" - ffn_ratio: 4.0 - resume_from_teacher_chkpt: "" - qkv_bias: true - proj_bias: true - ffn_bias: true - norm_layer: "layernorm" - n_storage_tokens: 0 - mask_k_bias: false - untie_cls_and_patch_norms: false # If true, use separate norms for CLS/reg and patch/mask tokens - untie_global_and_local_cls_norm: false # If true, use separate norms for local and global crop CLS token during training - in_chans: 3 - pos_embed_type: rope - pos_embed_rope_base: 100.0 - pos_embed_rope_min_period: null - pos_embed_rope_max_period: null - pos_embed_rope_normalize_coords: separate # min, max, separate - pos_embed_rope_shift_coords: null - pos_embed_rope_jitter_coords: null - pos_embed_rope_rescale_coords: null - pos_embed_rope_dtype: bf16 - fp8_enabled: False # Convert Linear layers to operate in fp8 precision - fp8_filter: "blocks" # Regex that must appear in module path; empty means everything -teacher: - momentum_teacher: 0.992 - final_momentum_teacher: 1 - warmup_teacher_temp: 0.04 - teacher_temp: 0.07 - warmup_teacher_temp_epochs: 30 - in_chans: 3 -distillation: # teacher - enabled: false - full_cfg_path: "" - checkpoint_path: "" -multidistillation: - enabled: false -hrft: # non-hrft'd student - enabled: false - checkpoint_path: "" # teacher_checkpoint path -optim: - epochs: 100 - optimizer: adamw - weight_decay: 0.04 - weight_decay_end: 0.4 - lr: 0.001 - warmup_epochs: 10 - min_lr: 1.0e-06 - schedule_trunc_extra: 0.0 # Compute the schedule for (1 + schedule_trunc_extra) steps and truncate, .25 is a good choice - clip_grad: 3.0 - freeze_last_layer_epochs: 1 - scaling_rule: sqrt_wrt_1024 - patch_embed_lr_mult: 0.2 - dino_head_wd_multiplier: 1.0 - layerwise_decay: 0.9 - multi_tensor_optim: true - dump_fsdp_weights_path: "" - 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 - global_local_crop_pairs_ratios: 1.0 - gram_teacher_crops_size: null # If not None, return crops for gram teacher - localcrops_subset_of_globalcrops: false - share_color_jitter: false - horizontal_flips: true - gram_teacher_no_distortions: false # If True, no distortions are applied to gram teacher crops - rgb_mean: - - 0.485 - - 0.456 - - 0.406 - rgb_std: - - 0.229 - - 0.224 - - 0.225 -evaluation: - eval_period_iterations: 12500 - low_freq_every: 5 - config_files: # Must be in fairvit/eval/configs - high_freq: benchmark_high_frequency.yaml # More often - low_freq: benchmark_low_frequency.yaml # Less often -checkpointing: - period: 3750 - max_to_keep: 3 - keep_every: 99999999999999999 # Save a checkpoint every N iterations, regardless of max_to_keep and period - -# Example of constant schedules with schedules v2 -# # schedules: -# # lr: -# # start: 0.0 -# # peak: 1e-3 -# # end: 1e-6 -# # warmup_epochs: 10 -# # freeze_last_layer_epochs: 1 -# # weight_decay: -# # start: 0.04 -# # peak: 0.04 -# # end: 0.04 -# # warmup_epochs: 0 -# # momentum: -# # start: 0.992 -# # peak: 0.992 -# # end: 0.992 -# # warmup_epochs: 0 -# # teacher_temp: -# # start: 0.04 -# # peak: 0.07 -# # end: 0.07 -# # warmup_epochs: 30 diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/configs/train/dinov3_vit7b16_gram_anchor.yaml b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/configs/train/dinov3_vit7b16_gram_anchor.yaml deleted file mode 100644 index 05cf7dfef6fda0199e10b45a761f14201a3db6b5..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/configs/train/dinov3_vit7b16_gram_anchor.yaml +++ /dev/null @@ -1,203 +0,0 @@ -MODEL: - META_ARCHITECTURE: SSLMetaArch - DEVICE: cuda - WEIGHTS: '' - DTYPE: float32 -compute_precision: - param_dtype: bf16 - reduce_dtype: fp32 - sharding_strategy: SHARD_GRAD_OP -dino: - loss_weight: 1.0 - global_ignore_diagonal: true - head_n_prototypes: 262144 - head_bottleneck_dim: 512 - head_norm_last_layer: false - head_nlayers: 3 - head_hidden_dim: 8192 - koleo_loss_weight: 0.1 - koleo_loss_distributed: false - koleo_topk: 1 - koleo_distributed_replicas: 0 - koleo_distributed_loss_group_size: null - koleo_distributed_loss_group_data: true - force_weight_norm: false - reweight_dino_local_loss: true - local_loss_weight_schedule: - start: 1 - peak: 1 - end: 0.5 - warmup_epochs: 1000 - cosine_epochs: 1 -ibot: - loss_weight: 1.0 - mask_sample_probability: 0.5 - mask_ratio_min_max: - - 0.1 - - 0.5 - mask_random_circular_shift: false - force_masking_even_with_zero_weight: false - separate_head: true - head_n_prototypes: 98304 - head_bottleneck_dim: 384 - head_norm_last_layer: false - head_nlayers: 3 - head_hidden_dim: 4096 -gram: - use_loss: true - compute_stats: false - loss_weight: 1.0 - ema_teacher: false - ckpt: ignore - it_load_ema_teacher: -1 - rep_update: true - update_frequency: 10000 - it_first_update: 1010000 - max_updates: 3 - normalized: true - img_level: true - remove_neg: false - remove_only_teacher_neg: false - tokens_used: all - global_teacher_resize_method: bicubic - global_teacher_resize_antialias: false - loss_weight_schedule: - start: 0 - peak: 0 - end: 2.0 - warmup_epochs: 1000 - cosine_epochs: 1 -train: - batch_size_per_gpu: 16 - dataset_path: null - saveckp_freq: 20 - seed: 0 - num_workers: 10 - OFFICIAL_EPOCH_LENGTH: 1000 - monitor_gradient_norm: false - chunk_schedule: [] - cache_dataset: true - use_teacher_head: true - learn_from_teacher_tokens: false - centering: sinkhorn_knopp - checkpointing: true - checkpointing_full: true - compile: true - cudagraphs: false - cell_augmentation: false - cell_augmentation_type: hpa - sharded_eval_checkpoint: true -student: - arch: vit_7b - patch_size: 16 - drop_path_rate: 0.4 - layerscale: 1.0e-05 - patch_drop: 0.0 - pretrained_weights: '' - ffn_layer: swiglu64 - ffn_ratio: 3 - resume_from_teacher_chkpt: '' - qkv_bias: false - proj_bias: true - ffn_bias: true - norm_layer: layernormbf16 - n_storage_tokens: 4 - untie_cls_and_patch_norms: false - untie_global_and_local_cls_norm: true - mask_k_bias: true - in_chans: 3 - pos_embed_type: rope - pos_embed_rope_base: 100 - pos_embed_rope_min_period: null - pos_embed_rope_max_period: null - pos_embed_rope_normalize_coords: separate - pos_embed_rope_shift_coords: null - pos_embed_rope_jitter_coords: null - pos_embed_rope_rescale_coords: 2 - pos_embed_rope_dtype: fp32 - fp8_enabled: true - fp8_filter: blocks -teacher: - momentum_teacher: null - final_momentum_teacher: null - warmup_teacher_temp: null - teacher_temp: null - warmup_teacher_temp_epochs: null - in_chans: 3 -distillation: - enabled: false - full_cfg_path: '' - checkpoint_path: '' -multidistillation: - enabled: false -hrft: - enabled: false - checkpoint_path: '' -optim: - epochs: 1200 - optimizer: adamw - weight_decay: null - weight_decay_end: null - lr: null - warmup_epochs: null - min_lr: null - schedule_trunc_extra: null - clip_grad: 30.0 - freeze_last_layer_epochs: null - scaling_rule: sqrt_wrt_1024 - patch_embed_lr_mult: 0.2 - dino_head_wd_multiplier: 1.0 - layerwise_decay: 0.98 - multi_tensor_optim: true - dump_fsdp_weights_path: '' - adamw_beta1: 0.9 - adamw_beta2: 0.99 -crops: - global_crops_scale: - - 0.32 - - 1.0 - local_crops_number: 8 - local_crops_scale: - - 0.05 - - 0.32 - global_crops_size: 256 - local_crops_size: 112 - gram_teacher_crops_size: 512 - localcrops_subset_of_globalcrops: false - share_color_jitter: false - horizontal_flips: false - gram_teacher_no_distortions: true - rgb_mean: - - 0.485 - - 0.456 - - 0.406 - rgb_std: - - 0.229 - - 0.224 - - 0.225 -checkpointing: - period: 1000 - max_to_keep: 3 - keep_every: 50000 -schedules: - lr: - start: 0 - peak: 3.0e-05 - end: 3.0e-05 - warmup_epochs: 100 - freeze_last_layer_epochs: 5 - weight_decay: - start: 0.04 - peak: 0.04 - end: 0.04 - warmup_epochs: 0 - teacher_temp: - start: 0.04 - peak: 0.07 - end: 0.07 - warmup_epochs: 100 - momentum: - start: 0.999 - peak: 0.999 - end: 0.999 - warmup_epochs: 0 diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/configs/train/dinov3_vit7b16_high_res_adapt.yaml b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/configs/train/dinov3_vit7b16_high_res_adapt.yaml deleted file mode 100644 index 313a104a7cbf757cc0288f10d8d22eb960bfe644..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/configs/train/dinov3_vit7b16_high_res_adapt.yaml +++ /dev/null @@ -1,224 +0,0 @@ -MODEL: - META_ARCHITECTURE: SSLMetaArch - DEVICE: cuda - WEIGHTS: '' - DTYPE: float32 -compute_precision: - param_dtype: bf16 - reduce_dtype: fp32 - sharding_strategy: SHARD_GRAD_OP -dino: - loss_weight: 1.0 - global_ignore_diagonal: true - head_n_prototypes: 262144 - head_bottleneck_dim: 512 - head_norm_last_layer: false - head_nlayers: 3 - head_hidden_dim: 8192 - koleo_loss_weight: 0.1 - koleo_loss_distributed: true - koleo_topk: 1 - koleo_distributed_replicas: 0 - koleo_distributed_loss_group_size: 16 - force_weight_norm: false - reweight_dino_local_loss: true - local_loss_weight_schedule: - start: 0.5 - peak: 0.5 - end: 0.5 - warmup_epochs: 0 - cosine_epochs: 0 - koleo_distributed_loss_group_data: true -ibot: - loss_weight: 1.0 - mask_sample_probability: 0.5 - mask_ratio_min_max: - - 0.1 - - 0.5 - mask_random_circular_shift: false - force_masking_even_with_zero_weight: false - separate_head: true - head_n_prototypes: 98304 - head_bottleneck_dim: 384 - head_norm_last_layer: false - head_nlayers: 3 - head_hidden_dim: 4096 -gram: - use_loss: true - compute_stats: false - loss_weight: 1.0 - ema_teacher: false - it_load_ema_teacher: -1 - rep_update: false - update_frequency: 10000 - it_first_update: 1010000 - max_updates: 3 - normalized: true - img_level: true - remove_neg: false - remove_only_teacher_neg: false - tokens_used: all - global_teacher_resize_method: bicubic - global_teacher_resize_antialias: false - loss_weight_schedule: - start: 1.5 - peak: 1.5 - end: 1.5 - warmup_epochs: 0 - cosine_epochs: 0 -train: - batch_size_per_gpu: 8 - dataset_path: null - saveckp_freq: 20 - seed: 0 - num_workers: 2 - OFFICIAL_EPOCH_LENGTH: 1000 - monitor_gradient_norm: false - chunk_schedule: [] - cache_dataset: true - use_teacher_head: true - learn_from_teacher_tokens: false - centering: sinkhorn_knopp - checkpointing: true - checkpointing_full: true - compile: true - cudagraphs: false - cell_augmentation: false - cell_augmentation_type: hpa - sharded_eval_checkpoint: true -student: - arch: vit_7b - patch_size: 16 - drop_path_rate: 0.4 - layerscale: 1.0e-05 - patch_drop: 0.0 - pretrained_weights: '' - ffn_layer: swiglu64 - ffn_ratio: 3 - resume_from_teacher_chkpt: '' - qkv_bias: false - proj_bias: true - ffn_bias: true - norm_layer: layernormbf16 - n_storage_tokens: 4 - untie_cls_and_patch_norms: false - untie_global_and_local_cls_norm: true - mask_k_bias: true - in_chans: 3 - pos_embed_type: rope - pos_embed_rope_base: 100 - pos_embed_rope_min_period: null - pos_embed_rope_max_period: null - pos_embed_rope_normalize_coords: separate - pos_embed_rope_shift_coords: null - pos_embed_rope_jitter_coords: null - pos_embed_rope_rescale_coords: 2 - pos_embed_rope_dtype: fp32 - fp8_enabled: true - fp8_filter: blocks -teacher: - momentum_teacher: null - final_momentum_teacher: null - warmup_teacher_temp: null - teacher_temp: null - warmup_teacher_temp_epochs: null - in_chans: 3 -distillation: - enabled: false - full_cfg_path: '' - checkpoint_path: '' -multidistillation: - enabled: false -hrft: - enabled: false - checkpoint_path: '' -optim: - epochs: 30 - optimizer: adamw - weight_decay: null - weight_decay_end: null - lr: null - warmup_epochs: null - min_lr: null - schedule_trunc_extra: null - clip_grad: 30.0 - freeze_last_layer_epochs: null - scaling_rule: sqrt_wrt_1024 - patch_embed_lr_mult: 0.2 - dino_head_wd_multiplier: 1.0 - layerwise_decay: 0.98 - multi_tensor_optim: true - dump_fsdp_weights_path: '' - adamw_beta1: 0.9 - adamw_beta2: 0.99 -crops: - global_crops_scale: - - 0.32 - - 1.0 - local_crops_number: 8 - local_crops_scale: - - 0.05 - - 0.32 - global_crops_size: - - 512 - - 768 - - 768 - - 768 - - 768 - local_crops_size: - - 112 - - 112 - - 168 - - 224 - - 336 - global_local_crop_pairs_ratios: - - 0.3 - - 0.3 - - 0.3 - - 0.05 - - 0.05 - gram_teacher_crops_size: - - 768 - - 1152 - - 1152 - - 1152 - - 1152 - localcrops_subset_of_globalcrops: false - share_color_jitter: false - horizontal_flips: false - gram_teacher_no_distortions: true - rgb_mean: - - 0.485 - - 0.456 - - 0.406 - rgb_std: - - 0.229 - - 0.224 - - 0.225 -checkpointing: - period: 250 - max_to_keep: 3 - keep_every: 50000 -schedules: - lr: - start: 0 - peak: 0 - end: 1.25e-05 - warmup_epochs: 0 - freeze_last_layer_epochs: 0 - cosine_epochs: 10 - weight_decay: - start: 0.04 - peak: 0.04 - end: 0.04 - warmup_epochs: 0 - teacher_temp: - start: 0.07 - peak: 0.07 - end: 0.07 - warmup_epochs: 0 - momentum: - start: 0.999 - peak: 0.999 - end: 0.999 - warmup_epochs: 0 diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/configs/train/dinov3_vit7b16_pretrain.yaml b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/configs/train/dinov3_vit7b16_pretrain.yaml deleted file mode 100644 index 7b93be46ecedb582befb9d4fcb7ed185410f2517..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/configs/train/dinov3_vit7b16_pretrain.yaml +++ /dev/null @@ -1,172 +0,0 @@ -MODEL: - META_ARCHITECTURE: SSLMetaArch - DEVICE: cuda - WEIGHTS: '' - DTYPE: float32 -compute_precision: - param_dtype: bf16 - reduce_dtype: fp32 - sharding_strategy: SHARD_GRAD_OP -dino: - loss_weight: 1.0 - global_ignore_diagonal: true - head_n_prototypes: 262144 - head_bottleneck_dim: 512 - head_norm_last_layer: false - head_nlayers: 3 - head_hidden_dim: 8192 - koleo_loss_weight: 0.1 - koleo_loss_distributed: false - koleo_topk: 1 - koleo_distributed_replicas: 0 - koleo_distributed_loss_group_size: null - force_weight_norm: false -ibot: - loss_weight: 1.0 - mask_sample_probability: 0.5 - mask_ratio_min_max: - - 0.1 - - 0.5 - mask_random_circular_shift: false - force_masking_even_with_zero_weight: false - separate_head: true - head_n_prototypes: 98304 - head_bottleneck_dim: 384 - head_norm_last_layer: false - head_nlayers: 3 - head_hidden_dim: 4096 -gram: - use_loss: false - compute_stats: false -train: - batch_size_per_gpu: 16 - dataset_path: null - saveckp_freq: 20 - seed: 0 - num_workers: 10 - OFFICIAL_EPOCH_LENGTH: 1000 - monitor_gradient_norm: false - chunk_schedule: [] - cache_dataset: true - use_teacher_head: true - learn_from_teacher_tokens: false - centering: sinkhorn_knopp - checkpointing: true - checkpointing_full: false - compile: true - cudagraphs: false - cell_augmentation: false - cell_augmentation_type: hpa - sharded_eval_checkpoint: true -student: - arch: vit_7b - patch_size: 16 - drop_path_rate: 0.4 - layerscale: 1.0e-05 - patch_drop: 0.0 - pretrained_weights: '' - ffn_layer: swiglu64 - ffn_ratio: 3 - resume_from_teacher_chkpt: '' - qkv_bias: false - proj_bias: true - ffn_bias: true - norm_layer: layernormbf16 - n_storage_tokens: 4 - untie_cls_and_patch_norms: false - untie_global_and_local_cls_norm: true - mask_k_bias: true - in_chans: 3 - pos_embed_type: rope - pos_embed_rope_base: 100 - pos_embed_rope_min_period: null - pos_embed_rope_max_period: null - pos_embed_rope_normalize_coords: separate - pos_embed_rope_shift_coords: null - pos_embed_rope_jitter_coords: null - pos_embed_rope_rescale_coords: 2 - pos_embed_rope_dtype: fp32 - fp8_enabled: true - fp8_filter: blocks -teacher: - momentum_teacher: null - final_momentum_teacher: null - warmup_teacher_temp: null - teacher_temp: null - warmup_teacher_temp_epochs: null - in_chans: 3 -distillation: - enabled: false - full_cfg_path: '' - checkpoint_path: '' -multidistillation: - enabled: false -hrft: - enabled: false - checkpoint_path: '' -optim: - epochs: 1000 - optimizer: adamw - weight_decay: null - weight_decay_end: null - lr: null - warmup_epochs: null - min_lr: null - schedule_trunc_extra: null - clip_grad: 30.0 - freeze_last_layer_epochs: null - scaling_rule: sqrt_wrt_1024 - patch_embed_lr_mult: 0.2 - dino_head_wd_multiplier: 1.0 - layerwise_decay: 0.98 - multi_tensor_optim: true - dump_fsdp_weights_path: '' - adamw_beta1: 0.9 - adamw_beta2: 0.99 -crops: - global_crops_scale: - - 0.32 - - 1.0 - local_crops_number: 8 - local_crops_scale: - - 0.05 - - 0.32 - global_crops_size: 256 - local_crops_size: 112 - localcrops_subset_of_globalcrops: false - share_color_jitter: false - horizontal_flips: false - rgb_mean: - - 0.485 - - 0.456 - - 0.406 - rgb_std: - - 0.229 - - 0.224 - - 0.225 -checkpointing: - period: 1000 - max_to_keep: 3 - keep_every: 50000 -schedules: - lr: - start: 0 - peak: 5.0e-05 - end: 5.0e-05 - warmup_epochs: 100 - freeze_last_layer_epochs: 5 - weight_decay: - start: 0.04 - peak: 0.04 - end: 0.04 - warmup_epochs: 0 - teacher_temp: - start: 0.04 - peak: 0.07 - end: 0.07 - warmup_epochs: 100 - momentum: - start: 0.994 - peak: 0.994 - end: 0.994 - warmup_epochs: 0 diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/configs/train/dinov3_vitl16_lvd1689m_distilled.yaml b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/configs/train/dinov3_vitl16_lvd1689m_distilled.yaml deleted file mode 100644 index 5d81e605593401de00ea6ac68630714a2c707fcc..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/configs/train/dinov3_vitl16_lvd1689m_distilled.yaml +++ /dev/null @@ -1,251 +0,0 @@ -MODEL: - META_ARCHITECTURE: MultiDistillationMetaArch - DEVICE: cuda - WEIGHTS: '' - DTYPE: float32 -compute_precision: - param_dtype: bf16 - reduce_dtype: fp32 - sharding_strategy: SHARD_GRAD_OP -dino: - loss_weight: 1.0 - global_ignore_diagonal: true - head_n_prototypes: 262144 - head_bottleneck_dim: 512 - head_norm_last_layer: false - head_nlayers: 3 - head_hidden_dim: 8192 - koleo_loss_weight: 0.1 - koleo_loss_distributed: false - koleo_topk: 1 - koleo_distributed_replicas: 0 - koleo_distributed_loss_group_size: null - koleo_distributed_loss_group_data: true - force_weight_norm: false - reweight_dino_local_loss: false - local_loss_weight_schedule: - start: 0.5 - peak: 0.5 - end: 0.5 - warmup_epochs: 0 -ibot: - loss_weight: 1.0 - mask_sample_probability: 0.5 - mask_ratio_min_max: - - 0.1 - - 0.5 - mask_random_circular_shift: false - force_masking_even_with_zero_weight: false - separate_head: true - head_n_prototypes: 98304 - head_bottleneck_dim: 384 - head_norm_last_layer: false - head_nlayers: 3 - head_hidden_dim: 4096 -coding_rate_loss: - use_cls_loss: false - cls_loss_weight: 0.2 - use_masked_patches_loss: false - masked_patches_loss_weight: 0.1 - epsilon: 8 -gram: - use_loss: false - compute_stats: false - loss_weight: 1.0 - ema_teacher: false - ckpt: null - it_load_ema_teacher: -1 - rep_update: true - update_frequency: 50000 - it_first_update: 0 - max_updates: null - normalized: true - img_level: false - remove_neg: false - remove_only_teacher_neg: false - tokens_used: all - global_teacher_resize_method: bicubic - global_teacher_resize_antialias: false - loss_weight_schedule: null -train: - batch_size_per_gpu: 3 - dataset_path: - output_dir: - saveckp_freq: 20 - seed: 0 - num_workers: 2 - OFFICIAL_EPOCH_LENGTH: 1250 - monitor_gradient_norm: false - chunk_schedule: [] - cache_dataset: true - use_teacher_head: true - learn_from_teacher_tokens: false - centering: sinkhorn_knopp - checkpointing: true - checkpointing_full: true - compile: true - cudagraphs: false - cell_augmentation: false - cell_augmentation_type: hpa - sharded_eval_checkpoint: false -student: - arch: vit_large - patch_size: 16 - drop_path_rate: 0.0 - layerscale: 1.0e-05 - drop_path_uniform: true - drop_path_shape: uniform - patch_drop: 0.0 - pretrained_weights: '' - sin_cos_embeddings: false - fourier_embeddings: false - fourier_encoding_dim: 64 - multiple_pos_embeddings: false - cls_pos_embedding: false - reg_pos_embedding: false - ffn_layer: mlp - ffn_ratio: 4.0 - resume_from_teacher_chkpt: - block_chunks: 0 - qkv_bias: true - proj_bias: true - ffn_bias: true - norm_layer: layernormbf16 - n_storage_tokens: 4 - mask_attention: false - mask_register_attention: false - untie_cls_and_patch_norms: false - untie_global_and_local_cls_norm: false - interpolate_offset: 0.0 - interpolate_antialias: true - mask_k_bias: true - init_std_cls: 0.02 - init_std_reg: 0.02 - rescale_weights_by_layer_id: false - in_chans: 3 - pos_embed_grid_size: 48 - pos_embed_type: ropenew - pos_embed_rope_gamma: 1.0 - pos_embed_rope_init_multi_frequencies: false - pos_embed_rope_base: 100 - pos_embed_rope_min_period: null - pos_embed_rope_max_period: null - pos_embed_rope_normalize_coords: separate - pos_embed_rope_shift_coords: null - pos_embed_rope_jitter_coords: null - pos_embed_rope_rescale_coords: 2 - pos_embed_rope_dtype: bf16 - sparse24_ranges: [] - sparse24_filter: - - mlp - sparse24_default: false - fp8_enabled: false - fp8_filter: blocks -teacher: - momentum_teacher: 0.994 - final_momentum_teacher: 1 - warmup_teacher_temp: 0.04 - teacher_temp: 0.07 - warmup_teacher_temp_epochs: 120 - in_chans: 3 -distillation: - enabled: true - full_cfg_path: - checkpoint_path: -multidistillation: - enabled: true - global_batch_size: 1920 - students: - - name: vits_mlp4_4 - config_path: - ranks_range: - - 0 - - 48 - - name: vitsp_swiglu6_1 - config_path: - ranks_range: - - 48 - - 96 - - name: vitb_mlp4_3 - config_path: - ranks_range: - - 96 - - 176 - - name: vitl_mlp4_1 - config_path: - ranks_range: - - 176 - - 296 -hrft: - enabled: false - checkpoint_path: '' -optim: - epochs: 20 - optimizer: adamw - weight_decay: 0.04 - weight_decay_end: 0.2 - lr: 0.0002 - warmup_epochs: 0 - min_lr: 1.0e-06 - schedule_trunc_extra: 0.0 - clip_grad: 3.0 - freeze_last_layer_epochs: 0 - scaling_rule: sqrt_wrt_1024 - patch_embed_lr_mult: 0.2 - dino_head_wd_multiplier: 1.0 - layerwise_decay: 0.99 - multi_tensor_optim: true - dump_fsdp_weights_path: '' - 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: 256 - local_crops_size: 112 - global_local_crop_pairs_ratios: 1.0 - gram_teacher_crops_size: 256 - localcrops_subset_of_globalcrops: false - share_color_jitter: false - horizontal_flips: false - gram_teacher_no_distortions: false - rgb_mean: - - 0.485 - - 0.456 - - 0.406 - rgb_std: - - 0.229 - - 0.224 - - 0.225 -checkpointing: - period: 3750 - max_to_keep: 3 - keep_every: 99999999999999999 -schedules: - weight_decay: - start: 0.04 - peak: 0.04 - end: 0.04 - warmup_epochs: 0 - teacher_temp: - start: 0.04 - peak: 0.07 - end: 0.07 - warmup_epochs: 0 - lr: - start: 0 - peak: 0 - end: 5.0e-05 - warmup_epochs: 0 - freeze_last_layer_epochs: 0 - cosine_epochs: 10 - momentum: - start: 0.994 - peak: 0.994 - end: 1.0 - warmup_epochs: 0 diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/configs/train/multi_distillation_test.yaml b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/configs/train/multi_distillation_test.yaml deleted file mode 100644 index 984c075be3557c808b625956c456eece82ed655f..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/configs/train/multi_distillation_test.yaml +++ /dev/null @@ -1,27 +0,0 @@ -MODEL: - META_ARCHITECTURE: MultiDistillationMetaArch -multidistillation: - enabled: true - global_batch_size: 256 - students: - - name: vits - config_path: dinov3/configs/train/multidist_tests/vits_p16.yaml - ranks_range: - - 0 - - 4 - - name: vitb - config_path: dinov3/configs/train/multidist_tests/vitb_p16.yaml - ranks_range: - - 4 - - 8 -distillation: # teacher - enabled: true - full_cfg_path: dinov3/configs/train/vitl_im1k_lin834.yaml - checkpoint_path: ignore -train: - dataset_path: ImageNet:split=TRAIN - cache_dataset: false - centering: "sinkhorn_knopp" - compile: true -ibot: - separate_head: true diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/configs/train/multidist_tests/vitb_p16.yaml b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/configs/train/multidist_tests/vitb_p16.yaml deleted file mode 100644 index bbf715426d3c0dddd96cbd76b3cb67a91e68d4cf..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/configs/train/multidist_tests/vitb_p16.yaml +++ /dev/null @@ -1,7 +0,0 @@ -# this corresponds to the default config -train: - dataset_path: ImageNet:split=TRAIN - checkpointing: true -student: - drop_path_rate: 0.1 - arch: vit_base diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/configs/train/multidist_tests/vits_p16.yaml b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/configs/train/multidist_tests/vits_p16.yaml deleted file mode 100644 index 3c7f831cae01de083174c7a36a13e99125dd2e61..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/configs/train/multidist_tests/vits_p16.yaml +++ /dev/null @@ -1,6 +0,0 @@ -# this corresponds to the default config -train: - dataset_path: ImageNet:split=TRAIN -student: - drop_path_rate: 0.1 - arch: vit_small diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/configs/train/vitl_im1k_lin834.yaml b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/configs/train/vitl_im1k_lin834.yaml deleted file mode 100644 index 46428be4ac7be9c4aefed38f88b26f34a2f60fa8..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/configs/train/vitl_im1k_lin834.yaml +++ /dev/null @@ -1,143 +0,0 @@ -# tested on RSC: /checkpoint/dino/qas/rope/vitl16_im1k/ -# gives 82.2 im1k-knn, 83.3 im1k-linear -# runs with a total batch size of 2048 (64/gpu, 4 nodes here) -# runs at 0.57s/iter -MODEL: - META_ARCHITECTURE: SSLMetaArch - DEVICE: cuda - WEIGHTS: '' - DTYPE: float32 -compute_precision: - param_dtype: bf16 - reduce_dtype: fp32 - sharding_strategy: SHARD_GRAD_OP -dino: - loss_weight: 1.0 - global_ignore_diagonal: true - head_n_prototypes: 65536 - head_bottleneck_dim: 256 - head_norm_last_layer: false - head_nlayers: 3 - head_hidden_dim: 2048 - koleo_loss_weight: 0.1 - koleo_loss_distributed: false - koleo_topk: 1 - koleo_distributed_replicas: 0 - force_weight_norm: false -ibot: - loss_weight: 1.0 - mask_sample_probability: 0.5 - mask_ratio_min_max: - - 0.1 - - 0.5 - mask_random_circular_shift: false - force_masking_even_with_zero_weight: false - separate_head: true - head_n_prototypes: 65536 - head_bottleneck_dim: 256 - head_norm_last_layer: false - head_nlayers: 3 - head_hidden_dim: 2048 -train: - batch_size_per_gpu: 64 - dataset_path: ImageNet:split=TRAIN - output_dir: /checkpoint/dino/qas/rope/vitl16_im1k - saveckp_freq: 20 - seed: 0 - num_workers: 10 - OFFICIAL_EPOCH_LENGTH: 1250 - monitor_gradient_norm: false - chunk_schedule: [] - cache_dataset: true - use_teacher_head: true - learn_from_teacher_tokens: false - centering: sinkhorn_knopp - checkpointing: false - compile: true - cudagraphs: false - cell_augmentation: false - cell_augmentation_type: hpa -student: - arch: vit_large - patch_size: 16 - drop_path_rate: 0.3 - layerscale: 1.0e-05 - patch_drop: 0.0 - pretrained_weights: '' - ffn_layer: mlp - ffn_ratio: 4.0 - resume_from_teacher_chkpt: '' - qkv_bias: true - proj_bias: true - ffn_bias: true - norm_layer: layernorm - n_storage_tokens: 0 - mask_k_bias: false - in_chans: 3 - pos_embed_type: rope - pos_embed_rope_base: 100.0 - pos_embed_rope_min_period: null - pos_embed_rope_max_period: null - pos_embed_rope_normalize_coords: separate # min, max, separate - pos_embed_rope_shift_coords: null - pos_embed_rope_jitter_coords: null - pos_embed_rope_rescale_coords: null - pos_embed_rope_dtype: bf16 - fp8_enabled: False # Convert Linear layers to operate in fp8 precision - fp8_filter: "blocks" # Regex that must appear in module path; empty means everything -teacher: - momentum_teacher: 0.992 - final_momentum_teacher: 1 - warmup_teacher_temp: 0.04 - teacher_temp: 0.07 - warmup_teacher_temp_epochs: 30 - in_chans: 3 -distillation: - enabled: false - full_cfg_path: '' - checkpoint_path: '' -multidistillation: - enabled: false -hrft: - enabled: false - checkpoint_path: '' -optim: - epochs: 100 - optimizer: adamw - weight_decay: 0.04 - weight_decay_end: 0.4 - lr: 0.001 - 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 - dino_head_wd_multiplier: 1.0 - layerwise_decay: 0.9 - multi_tensor_optim: true - dump_fsdp_weights_path: '' - 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 - localcrops_subset_of_globalcrops: false - share_color_jitter: false - horizontal_flips: true -evaluation: - eval_period_iterations: 12500 - low_freq_every: 5 - config_files: - high_freq: benchmark_high_frequency.yaml - low_freq: benchmark_low_frequency.yaml -checkpointing: - period: 3750 - max_to_keep: 3 \ No newline at end of file diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/data/__init__.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/data/__init__.py deleted file mode 100644 index 8506ec58de482636b88d08b57674ed09e726595f..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/data/__init__.py +++ /dev/null @@ -1,12 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -from .adapters import DatasetWithEnumeratedTargets -from .augmentations import DataAugmentationDINO -from .collate import collate_data_and_cast -from .loaders import SamplerType, make_data_loader, make_dataset -from .meta_loaders import CombinedDataLoader -from .masking import MaskingGenerator -from .transforms import 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a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/data/adapters.py +++ /dev/null @@ -1,68 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -from typing import Any, Optional, Tuple - -from torch.utils.data import Dataset - - -def extend_samples_with_index(dataset_class): - class DatasetWithIndex(dataset_class): - def __init__(self, **kwargs) -> None: - root = dataset_class.get_root() - super().__init__(root=root, **kwargs) - - def __getitem__(self, index: int): - image, target = super().__getitem__(index) - return image, target, index - - return DatasetWithIndex - - -class DatasetWithEnumeratedTargets(Dataset): - """ - If pad_dataset is set, pads based on torch's DistributedSampler implementation, which - with drop_last=False pads the last batch to be a multiple of the world size. - https://github.com/pytorch/pytorch/blob/main/torch/utils/data/distributed.py#L91 - """ - - def __init__(self, dataset: Dataset, pad_dataset: bool = False, num_replicas: Optional[int] = None): - self._dataset = dataset - self._size = len(self._dataset) - self._padded_size = self._size - self._pad_dataset = pad_dataset - if self._pad_dataset: - assert num_replicas is not None, "num_replicas should be set if pad_dataset is True" - self._padded_size = num_replicas * ((len(dataset) + num_replicas - 1) // num_replicas) - - def get_image_relpath(self, index: int) -> str: - assert self._pad_dataset or index < self._size - return self._dataset.get_image_relpath(index % self._size) - - def get_image_data(self, index: int) -> bytes: - assert self._pad_dataset or index < self._size - return self._dataset.get_image_data(index % self._size) - - def get_target(self, index: int) -> Tuple[Any, int]: - target = self._dataset.get_target(index % self._size) - if index >= self._size: - assert self._pad_dataset - return (-1, target) - return (index, target) - - def get_sample_decoder(self, index: int) -> Any: - assert self._pad_dataset or index < self._size - return self._dataset.get_sample_decoder(index % self._size) - - def __getitem__(self, index: int) -> Tuple[Any, Tuple[Any, int]]: - image, target = self._dataset[index % self._size] - if index >= self._size: - assert self._pad_dataset - return image, (-1, target) - target = index if target is None else target - return image, (index, target) - - def __len__(self) -> int: - return self._padded_size diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/data/augmentations.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/data/augmentations.py deleted file mode 100644 index 710cfd1b4de9c4d990279e6e4e4afec4ee8823b3..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/data/augmentations.py +++ /dev/null @@ -1,227 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import logging - -import numpy as np -import torch -from torch import nn -from torchvision.transforms import v2 - -from dinov3.data.transforms import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, GaussianBlur, make_normalize_transform - -logger = logging.getLogger("dinov3") - - -class DataAugmentationDINO(object): - def __init__( - self, - global_crops_scale, - local_crops_scale, - local_crops_number, - global_crops_size=224, - local_crops_size=96, - gram_teacher_crops_size=None, - gram_teacher_no_distortions=False, - teacher_no_color_jitter=False, - local_crops_subset_of_global_crops=False, - patch_size=16, - share_color_jitter=False, - horizontal_flips=True, - mean=IMAGENET_DEFAULT_MEAN, - std=IMAGENET_DEFAULT_STD, - ): - self.global_crops_scale = global_crops_scale - self.local_crops_scale = local_crops_scale - self.local_crops_number = local_crops_number - self.global_crops_size = global_crops_size - self.local_crops_size = local_crops_size - self.gram_teacher_crops_size = gram_teacher_crops_size - self.gram_teacher_no_distortions = gram_teacher_no_distortions - self.teacher_no_color_jitter = teacher_no_color_jitter - self.local_crops_subset_of_global_crops = local_crops_subset_of_global_crops - self.patch_size = patch_size - self.share_color_jitter = share_color_jitter - self.mean = mean - self.std = std - - logger.info("###################################") - logger.info("Using data augmentation parameters:") - logger.info(f"global_crops_scale: {global_crops_scale}") - logger.info(f"local_crops_scale: {local_crops_scale}") - logger.info(f"local_crops_number: {local_crops_number}") - logger.info(f"global_crops_size: {global_crops_size}") - logger.info(f"local_crops_size: {local_crops_size}") - logger.info(f"gram_crops_size: {gram_teacher_crops_size}") - logger.info(f"gram_teacher_no_distortions: {gram_teacher_no_distortions}") - logger.info(f"teacher_no_color_jitter: {teacher_no_color_jitter}") - logger.info(f"local_crops_subset_of_global_crops: {local_crops_subset_of_global_crops}") - logger.info(f"patch_size if local_crops_subset_of_global_crops: {patch_size}") - logger.info(f"share_color_jitter: {share_color_jitter}") - logger.info(f"horizontal flips: {horizontal_flips}") - logger.info("###################################") - - # Global crops and gram teacher crops can have different sizes. We first take a crop of the maximum size - # and then resize it to the desired size for global and gram teacher crops. - global_crop_max_size = max(global_crops_size, gram_teacher_crops_size if gram_teacher_crops_size else 0) - - # random resized crop and flip - self.geometric_augmentation_global = v2.Compose( - [ - v2.RandomResizedCrop( - global_crop_max_size, - scale=global_crops_scale, - interpolation=v2.InterpolationMode.BICUBIC, - ), - v2.RandomHorizontalFlip(p=0.5 if horizontal_flips else 0.0), - ] - ) - - resize_global = nn.Identity() # Resize transform applied to global crops after random crop - self.resize_global_post_transf = ( - nn.Identity() - ) # Resize transform applied to global crops after all other transforms - self.resize_gram_teacher = None # Resize transform applied to crops for gram teacher - if gram_teacher_crops_size is not None: - # All resize transforms will do nothing if the crop size is already the desired size. - if gram_teacher_no_distortions: - # When there a no distortions for the gram teacher crop, we can resize before the distortions. - # This is the preferred order, because it keeps the image size for the augmentations consistent, - # which matters e.g. for GaussianBlur. - resize_global = v2.Resize( - global_crops_size, - interpolation=v2.InterpolationMode.BICUBIC, - ) - else: - # When there a no distortions for the gram teacher crop, we need to resize after the distortions, - # because the distortions are shared between global and gram teacher crops. - self.resize_global_post_transf = v2.Resize( - global_crops_size, - interpolation=v2.InterpolationMode.BICUBIC, - ) - - self.resize_gram_teacher = v2.Resize( - gram_teacher_crops_size, - interpolation=v2.InterpolationMode.BICUBIC, - ) - - self.geometric_augmentation_local = v2.Compose( - [ - v2.RandomResizedCrop( - local_crops_size, - scale=local_crops_scale, - interpolation=v2.InterpolationMode.BICUBIC, - ), - v2.RandomHorizontalFlip(p=0.5 if horizontal_flips else 0.0), - ] - ) - - # color distortions / blurring - color_jittering = v2.Compose( - [ - v2.RandomApply( - [v2.ColorJitter(brightness=0.4, contrast=0.4, saturation=0.2, hue=0.1)], - p=0.8, - ), - v2.RandomGrayscale(p=0.2), - ] - ) - - global_transfo1_extra = GaussianBlur(p=1.0) - - global_transfo2_extra = v2.Compose( - [ - GaussianBlur(p=0.1), - v2.RandomSolarize(threshold=128, p=0.2), - ] - ) - - local_transfo_extra = GaussianBlur(p=0.5) - - # normalization - self.normalize = v2.Compose( - [ - v2.ToImage(), - v2.ToDtype(torch.float32, scale=True), - make_normalize_transform(mean=mean, std=std), - ] - ) - - if self.share_color_jitter: - self.color_jittering = color_jittering - self.global_transfo1 = v2.Compose([resize_global, global_transfo1_extra, self.normalize]) - self.global_transfo2 = v2.Compose([resize_global, global_transfo2_extra, self.normalize]) - self.local_transfo = v2.Compose([local_transfo_extra, self.normalize]) - else: - self.global_transfo1 = v2.Compose( - [resize_global, color_jittering, global_transfo1_extra, self.normalize] - ) - self.global_transfo2 = v2.Compose( - [resize_global, color_jittering, global_transfo2_extra, self.normalize] - ) - self.local_transfo = v2.Compose([color_jittering, local_transfo_extra, self.normalize]) - - def __call__(self, image): - output = {} - output["weak_flag"] = True # some residual from mugs - - if self.share_color_jitter: - image = self.color_jittering(image) - - # global crops: - im1_base = self.geometric_augmentation_global(image) - global_crop_1_transf = self.global_transfo1(im1_base) - global_crop_1 = self.resize_global_post_transf(global_crop_1_transf) - - im2_base = self.geometric_augmentation_global(image) - global_crop_2_transf = self.global_transfo2(im2_base) - global_crop_2 = self.resize_global_post_transf(global_crop_2_transf) - - output["global_crops"] = [global_crop_1, global_crop_2] - - # global crops for teacher: - if self.teacher_no_color_jitter: - output["global_crops_teacher"] = [ - self.normalize(im1_base), - self.normalize(im2_base), - ] - else: - output["global_crops_teacher"] = [global_crop_1, global_crop_2] - - if self.gram_teacher_crops_size is not None: - # crops for gram teacher: - if self.gram_teacher_no_distortions: - gram_crop_1 = self.normalize(self.resize_gram_teacher(im1_base)) - gram_crop_2 = self.normalize(self.resize_gram_teacher(im2_base)) - else: - gram_crop_1 = self.resize_gram_teacher(global_crop_1_transf) - gram_crop_2 = self.resize_gram_teacher(global_crop_2_transf) - output["gram_teacher_crops"] = [gram_crop_1, gram_crop_2] - - # local crops: - if self.local_crops_subset_of_global_crops: - _local_crops = [self.local_transfo(im1_base) for _ in range(self.local_crops_number // 2)] + [ - self.local_transfo(im2_base) for _ in range(self.local_crops_number // 2) - ] - - local_crops = [] - offsets = [] - gs = self.global_crops_size - ls = self.local_crops_size - for img in _local_crops: - rx, ry = np.random.randint(0, (gs - ls) // self.patch_size, 2) * self.patch_size - local_crops.append(img[:, rx : rx + ls, ry : ry + ls]) - offsets.append((rx, ry)) - - output["local_crops"] = local_crops - output["offsets"] = offsets - else: - local_crops = [ - self.local_transfo(self.geometric_augmentation_local(image)) for _ in range(self.local_crops_number) - ] - output["local_crops"] = local_crops - output["offsets"] = () - - return output diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/data/collate.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/data/collate.py deleted file mode 100644 index 8470008d456930e1675ecb1abea575aac745fade..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/data/collate.py +++ /dev/null @@ -1,125 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import random - -import torch - - -def collate_data_and_cast( - samples_list, - mask_ratio_tuple, - mask_probability, - dtype, - n_tokens=None, - mask_generator=None, - random_circular_shift=False, - local_batch_size=None, -): - n_global_crops = len(samples_list[0][0]["global_crops"]) - n_local_crops = len(samples_list[0][0]["local_crops"]) - - collated_global_crops = torch.stack( - [s[0]["global_crops"][i] for i in range(n_global_crops) for s in samples_list] - ) # [n_global_crops, B, ...] - collated_local_crops = torch.stack([s[0]["local_crops"][i] for i in range(n_local_crops) for s in samples_list]) - if "gram_teacher_crops" in samples_list[0][0]: - collated_gram_teacher_crops = torch.stack( - [s[0]["gram_teacher_crops"][i] for i in range(n_global_crops) for s in samples_list] - ) # [n_global_crops, B, ...] - else: - collated_gram_teacher_crops = None - - if local_batch_size is not None: - # multi-distillation case, number of masks is different because the number of samples masked - # is different of the number of samples passed into the teacher initially - B = n_global_crops * local_batch_size - else: - B = len(collated_global_crops) - N = n_tokens - n_samples_masked = int(B * mask_probability) - probs = torch.linspace(*mask_ratio_tuple, n_samples_masked + 1) - upperbound = 0 - masks_list = [] - for i in range(0, n_samples_masked): - prob_max = probs[i + 1] - mask = torch.BoolTensor(mask_generator(int(N * prob_max))) - if random_circular_shift: # apply le random circular shift to - shift_x, shift_y = ( - random.randint(0, mask.shape[0] - 1), - random.randint(0, mask.shape[1] - 1), - ) - mask = torch.roll(mask, (shift_x, shift_y), (0, 1)) - masks_list.append(mask) - upperbound += int(N * prob_max) - for _ in range(n_samples_masked, B): - masks_list.append(torch.BoolTensor(mask_generator(0))) - - random.shuffle(masks_list) - - collated_masks = torch.stack(masks_list).flatten(1) - mask_indices_list = collated_masks.flatten().nonzero().flatten() - - masks_weight = (1 / collated_masks.sum(-1).clamp(min=1.0)).unsqueeze(-1).expand_as(collated_masks)[collated_masks] - - out = { - "collated_global_crops": collated_global_crops.to(dtype), - "collated_local_crops": collated_local_crops.to(dtype), - "collated_masks": collated_masks, - "mask_indices_list": mask_indices_list, - "masks_weight": masks_weight, - "upperbound": upperbound, - "n_masked_patches": torch.full((1,), fill_value=mask_indices_list.shape[0], dtype=torch.long), - } - if collated_gram_teacher_crops is not None: - out["collated_gram_teacher_crops"] = collated_gram_teacher_crops.to(dtype) - return out - - -# def get_batch_subset(collated_data_batch, target_bs): -def get_batch_subset(collated_data_batch, divide_by): - old_bs = collated_data_batch["collated_global_crops"].shape[0] // 2 - target_bs = (old_bs + divide_by - 1) // divide_by - collated_global_crops = ( - collated_data_batch["collated_global_crops"].unflatten(0, (2, old_bs)).narrow(1, 0, target_bs).flatten(0, 1) - ) - collated_local_crops = ( - collated_data_batch["collated_local_crops"].unflatten(0, (-1, old_bs)).narrow(1, 0, target_bs).flatten(0, 1) - ) - - masks_old_bs = collated_data_batch["collated_masks"].shape[0] // 2 - masks_target_bs = masks_old_bs // divide_by - collated_masks = ( - collated_data_batch["collated_masks"] - .unflatten(0, (2, masks_old_bs)) - .narrow(1, 0, masks_target_bs) - .flatten(0, 1) - ) - mask_indices_list = collated_masks.flatten().nonzero().flatten() - - while mask_indices_list.shape[0] == 0: - _unbind = list(collated_data_batch["collated_masks"].unbind(0)) - random.shuffle(_unbind) - _bind = torch.stack(_unbind, dim=0) - collated_masks = _bind.unflatten(0, (2, masks_old_bs)).narrow(1, 0, masks_target_bs).flatten(0, 1) - mask_indices_list = collated_masks.flatten().nonzero().flatten() - - masks_weight = (1 / collated_masks.sum(-1).clamp(min=1.0)).unsqueeze(-1).expand_as(collated_masks)[collated_masks] - upperbound = collated_data_batch["upperbound"] - - new_batch = { - "collated_global_crops": collated_global_crops, - "collated_local_crops": collated_local_crops, - "collated_masks": collated_masks, - "mask_indices_list": mask_indices_list, - "masks_weight": masks_weight, - "upperbound": upperbound, - "n_masked_patches": torch.full((1,), fill_value=mask_indices_list.shape[0], dtype=torch.long), - } - - if "global_batch_size" in collated_data_batch.keys(): - new_batch["global_batch_size"] = collated_data_batch["global_batch_size"] // divide_by - - return new_batch diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/data/datasets/__init__.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/data/datasets/__init__.py deleted file mode 100644 index 9075e9fecb2fb4a37361df7106e9b6e6a56df41a..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/data/datasets/__init__.py +++ /dev/null @@ -1,10 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -from .ade20k import ADE20K -from .coco_captions import CocoCaptions -from .image_net import ImageNet -from .image_net_22k import ImageNet22k -from .nyu import NYU diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/data/datasets/__pycache__/__init__.cpython-310.pyc b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/data/datasets/__pycache__/__init__.cpython-310.pyc deleted file mode 100644 index 564df644687128689776ab64767fb7d9f8beade1..0000000000000000000000000000000000000000 Binary files a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/data/datasets/__pycache__/__init__.cpython-310.pyc and /dev/null differ diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/data/datasets/__pycache__/ade20k.cpython-310.pyc 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17ebe7320fa4e8138292bbf86529e60cf2339085..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/data/datasets/ade20k.py +++ /dev/null @@ -1,88 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import os -from enum import Enum -from typing import Any, Callable, List, Optional, Tuple, Union - -from PIL import Image - -from .decoders import Decoder, DenseTargetDecoder, ImageDataDecoder -from .extended import ExtendedVisionDataset - - -class _Split(Enum): - TRAIN = "train" - VAL = "val" - - @property - def dirname(self) -> str: - return { - _Split.TRAIN: "training", - _Split.VAL: "validation", - }[self] - - -def _file_to_segmentation_path(file_name: str, segm_base_path: str) -> str: - file_name_noext = os.path.splitext(file_name)[0] - return os.path.join(segm_base_path, file_name_noext + ".png") - - -def _load_segmentation(root: str, split_file_names: List[str]): - segm_base_path = "annotations" - segmentation_paths = [_file_to_segmentation_path(file_name, segm_base_path) for file_name in split_file_names] - return segmentation_paths - - -def _load_file_paths(root: str, split: _Split) -> Tuple[List[str], List[str]]: - with open(os.path.join(root, f"ADE20K_object150_{split.value}.txt")) as f: - split_file_names = sorted(f.read().strip().split("\n")) - - all_segmentation_paths = _load_segmentation(root, split_file_names) - file_names = [os.path.join("images", el) for el in split_file_names] - return file_names, all_segmentation_paths - - -class ADE20K(ExtendedVisionDataset): - Split = Union[_Split] - Labels = Union[Image.Image] - - def __init__( - self, - split: "ADE20K.Split", - root: Optional[str] = None, - transforms: Optional[Callable] = None, - transform: Optional[Callable] = None, - target_transform: Optional[Callable] = None, - image_decoder: Decoder = ImageDataDecoder, - target_decoder: Decoder = DenseTargetDecoder, - ) -> None: - super().__init__( - root=root, - transforms=transforms, - transform=transform, - target_transform=target_transform, - image_decoder=image_decoder, - target_decoder=target_decoder, - ) - - self.image_paths, self.target_paths = _load_file_paths(root, split) - - def get_image_data(self, index: int) -> bytes: - image_relpath = self.image_paths[index] - image_full_path = os.path.join(self.root, image_relpath) - with open(image_full_path, mode="rb") as f: - image_data = f.read() - return image_data - - def get_target(self, index: int) -> Any: - target_relpath = self.target_paths[index] - target_full_path = os.path.join(self.root, target_relpath) - with open(target_full_path, mode="rb") as f: - target_data = f.read() - return target_data - - def __len__(self) -> int: - return len(self.image_paths) diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/data/datasets/coco_captions.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/data/datasets/coco_captions.py deleted file mode 100644 index 9622982ed3ada27fb13b15c297126b0aacadf32b..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/data/datasets/coco_captions.py +++ /dev/null @@ -1,88 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import json -import os -import random -from enum import Enum -from typing import Callable, Dict, List, Optional, Union - -from .decoders import ImageDataDecoder, TargetDecoder -from .extended import ExtendedVisionDataset - -# Dataset: https://www.kaggle.com/datasets/nikhil7280/coco-image-caption - - -class _Split(Enum): - TRAIN = "train" - VAL = "val" - - -def read_images_and_captions(root: str, split: _Split) -> List[Dict]: - image_dir = None - if _Split(split) == _Split.TRAIN: - annotations_full_path = os.path.join( - root, "annotations_trainval2014/annotations/captions_train2014.json" - ) - image_dir = os.path.join(root, "train2014/train2014") - else: - annotations_full_path = os.path.join( - root, "annotations_trainval2017/annotations/captions_train2017.json" - ) - image_dir = os.path.join(root, "val2017/val2017") - with open(annotations_full_path) as f: - all_annotations = json.load(f) - data = {} - for item in all_annotations["images"]: - id = item["id"] - data[id] = { - "id": None, - "image": os.path.join(image_dir, item["file_name"]), - "captions": [], - } - for item in all_annotations["annotations"]: - data[item["image_id"]]["id"] = item["image_id"] - data[item["image_id"]]["captions"].append(item["caption"]) - return list(data.values()) - - -class CocoCaptions(ExtendedVisionDataset): - Split = Union[_Split] - - def __init__( - self, - *, - split: "CocoCaptions.Split", - root: Optional[str] = None, - transforms: Optional[Callable] = None, - transform: Optional[Callable] = None, - target_transform: Optional[Callable] = None, - ) -> None: - super().__init__( - root=root, - transforms=transforms, - transform=transform, - target_transform=target_transform, - image_decoder=ImageDataDecoder, - target_decoder=TargetDecoder, - ) - - self.image_captions = read_images_and_captions(root, split) - - def get_image_relpath(self, index: int) -> str: - image_path = self.image_captions[index]["image"] - return image_path - - def get_image_data(self, index: int) -> bytes: - image_path = self.get_image_relpath(index) - with open(image_path, mode="rb") as f: - image_data = f.read() - return image_data - - def get_target(self, index: int) -> str: - return random.choice(self.image_captions[index]["captions"]) - - def __len__(self) -> int: - return len(self.image_captions) diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/data/datasets/decoders.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/data/datasets/decoders.py deleted file mode 100644 index 44715ee67f03dfc9aca1c2fde76c0d2f8488e198..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/data/datasets/decoders.py +++ /dev/null @@ -1,40 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -from io import BytesIO -from typing import Any - -from PIL import Image - - -class Decoder: - def decode(self) -> Any: - raise NotImplementedError - - -class ImageDataDecoder(Decoder): - def __init__(self, image_data: bytes) -> None: - self._image_data = image_data - - def decode(self) -> Image: - f = BytesIO(self._image_data) - return Image.open(f).convert(mode="RGB") - - -class TargetDecoder(Decoder): - def __init__(self, target: Any): - self._target = target - - def decode(self) -> Any: - return self._target - - -class DenseTargetDecoder(Decoder): - def __init__(self, image_data: bytes) -> None: - self._image_data = image_data - - def decode(self) -> Image: - f = BytesIO(self._image_data) - return Image.open(f) diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/data/datasets/extended.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/data/datasets/extended.py deleted file mode 100644 index f7e0d5db29664b70136afed3eddb3f7326d8a59c..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/data/datasets/extended.py +++ /dev/null @@ -1,46 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -from typing import Any, Tuple - -from torchvision.datasets import VisionDataset - -from .decoders import Decoder, ImageDataDecoder, TargetDecoder - - -class ExtendedVisionDataset(VisionDataset): - def __init__( - self, - image_decoder: Decoder = ImageDataDecoder, - target_decoder: Decoder = TargetDecoder, - *args, - **kwargs, - ) -> None: - super().__init__(*args, **kwargs) # type: ignore - self.image_decoder = image_decoder - self.target_decoder = target_decoder - - def get_image_data(self, index: int) -> bytes: - raise NotImplementedError - - def get_target(self, index: int) -> Any: - raise NotImplementedError - - def __getitem__(self, index: int) -> Tuple[Any, Any]: - try: - image_data = self.get_image_data(index) - image = self.image_decoder(image_data).decode() - except Exception as e: - raise RuntimeError(f"can not read image for sample {index}") from e - target = self.get_target(index) - target = self.target_decoder(target).decode() - - if self.transforms is not None: - image, target = self.transforms(image, target) - - return image, target - - def __len__(self) -> int: - raise NotImplementedError diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/data/datasets/image_net.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/data/datasets/image_net.py deleted file mode 100644 index f148cecfb95f138db739f6f25cb2c81391b1ad79..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/data/datasets/image_net.py +++ /dev/null @@ -1,297 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import csv -import logging -import os -from enum import Enum -from typing import Callable, List, Optional, Tuple, Union - -import numpy as np - -from .decoders import ImageDataDecoder, TargetDecoder -from .extended import ExtendedVisionDataset - -logger = logging.getLogger("dinov3") -_Target = int - - -class _Split(Enum): - TRAIN = "train" - VAL = "val" - TEST = "test" # NOTE: torchvision does not support the test split - - @property - def length(self) -> int: - split_lengths = { - _Split.TRAIN: 1_281_167, - _Split.VAL: 50_000, - _Split.TEST: 100_000, - } - return split_lengths[self] - - def get_dirname(self, class_id: Optional[str] = None) -> str: - return self.value if class_id is None else os.path.join(self.value, class_id) - - def get_image_relpath(self, actual_index: int, class_id: Optional[str] = None) -> str: - dirname = self.get_dirname(class_id) - if self == _Split.TRAIN: - basename = f"{class_id}_{actual_index}" - else: # self in (_Split.VAL, _Split.TEST): - basename = f"ILSVRC2012_{self.value}_{actual_index:08d}" - return os.path.join(dirname, basename + ".JPEG") - - def parse_image_relpath(self, image_relpath: str) -> Tuple[str, int]: - assert self != _Split.TEST - dirname, filename = os.path.split(image_relpath) - class_id = os.path.split(dirname)[-1] - basename, _ = os.path.splitext(filename) - actual_index = int(basename.split("_")[-1]) - return class_id, actual_index - - -class ImageNet(ExtendedVisionDataset): - Target = Union[_Target] - Split = Union[_Split] - - def __init__( - self, - *, - split: "ImageNet.Split", - root: str, - extra: str, - transforms: Optional[Callable] = None, - transform: Optional[Callable] = None, - target_transform: Optional[Callable] = None, - ) -> None: - super().__init__( - root=root, - transforms=transforms, - transform=transform, - target_transform=target_transform, - image_decoder=ImageDataDecoder, - target_decoder=TargetDecoder, - ) - self._extra_root = extra - self._split = split - - self._entries = None - self._class_ids = None - self._class_names = None - - @property - def split(self) -> "ImageNet.Split": - return self._split - - def _get_extra_full_path(self, extra_path: str) -> str: - return os.path.join(self._extra_root, extra_path) - - def _load_extra(self, extra_path: str) -> np.ndarray: - extra_full_path = self._get_extra_full_path(extra_path) - return np.load(extra_full_path, mmap_mode="r") - - def _save_extra(self, extra_array: np.ndarray, extra_path: str) -> None: - extra_full_path = self._get_extra_full_path(extra_path) - os.makedirs(self._extra_root, exist_ok=True) - np.save(extra_full_path, extra_array) - - @property - def _entries_path(self) -> str: - return f"entries-{self._split.value.upper()}.npy" - - @property - def _class_ids_path(self) -> str: - return f"class-ids-{self._split.value.upper()}.npy" - - @property - def _class_names_path(self) -> str: - return f"class-names-{self._split.value.upper()}.npy" - - def _get_entries(self) -> np.ndarray: - if self._entries is None: - self._entries = self._load_extra(self._entries_path) - assert self._entries is not None - return self._entries - - def _get_class_ids(self) -> np.ndarray: - if self._split == _Split.TEST: - raise AssertionError("Class IDs are not available in TEST split") - if self._class_ids is None: - self._class_ids = self._load_extra(self._class_ids_path) - assert self._class_ids is not None - return self._class_ids - - def _get_class_names(self) -> np.ndarray: - if self._split == _Split.TEST: - raise AssertionError("Class names are not available in TEST split") - if self._class_names is None: - self._class_names = self._load_extra(self._class_names_path) - assert self._class_names is not None - return self._class_names - - def find_class_id(self, class_index: int) -> str: - class_ids = self._get_class_ids() - return str(class_ids[class_index]) - - def find_class_name(self, class_index: int) -> str: - class_names = self._get_class_names() - return str(class_names[class_index]) - - def get_image_data(self, index: int) -> bytes: - entries = self._get_entries() - actual_index = entries[index]["actual_index"] - - class_id = self.get_class_id(index) - - image_relpath = self.split.get_image_relpath(actual_index, class_id) - image_full_path = os.path.join(self.root, image_relpath) - with open(image_full_path, mode="rb") as f: - image_data = f.read() - return image_data - - def get_target(self, index: int) -> Optional[Target]: - entries = self._get_entries() - class_index = entries[index]["class_index"] - return None if self.split == _Split.TEST else int(class_index) - - def get_targets(self) -> Optional[np.ndarray]: - entries = self._get_entries() - return None if self.split == _Split.TEST else entries["class_index"] - - def get_class_id(self, index: int) -> Optional[str]: - entries = self._get_entries() - class_id = entries[index]["class_id"] - return None if self.split == _Split.TEST else str(class_id) - - def get_class_name(self, index: int) -> Optional[str]: - entries = self._get_entries() - class_name = entries[index]["class_name"] - return None if self.split == _Split.TEST else str(class_name) - - def __len__(self) -> int: - entries = self._get_entries() - assert len(entries) == self.split.length - return len(entries) - - def _load_labels(self, labels_path: str) -> List[Tuple[str, str]]: - labels_full_path = os.path.join(self.root, labels_path) - labels = [] - - try: - with open(labels_full_path, "r") as f: - reader = csv.reader(f) - for row in reader: - class_id, class_name = row - labels.append((class_id, class_name)) - except OSError as e: - raise RuntimeError(f'can not read labels file "{labels_full_path}"') from e - - return labels - - def _dump_entries(self) -> None: - split = self.split - if split == ImageNet.Split.TEST: - dataset = None - sample_count = split.length - max_class_id_length, max_class_name_length = 0, 0 - else: - labels_path = "labels.txt" - logger.info(f'loading labels from "{labels_path}"') - labels = self._load_labels(labels_path) - - # NOTE: Using torchvision ImageFolder for consistency - from torchvision.datasets import ImageFolder - - dataset_root = os.path.join(self.root, split.get_dirname()) - dataset = ImageFolder(dataset_root) - sample_count = len(dataset) - max_class_id_length, max_class_name_length = -1, -1 - for sample in dataset.samples: - _, class_index = sample - class_id, class_name = labels[class_index] - max_class_id_length = max(len(class_id), max_class_id_length) - max_class_name_length = max(len(class_name), max_class_name_length) - - dtype = np.dtype( - [ - ("actual_index", " old_percent: - logger.info(f"creating entries: {percent}%") - old_percent = percent - - actual_index = index + 1 - class_index = np.uint32(-1) - class_id, class_name = "", "" - entries_array[index] = (actual_index, class_index, class_id, class_name) - else: - class_names = {class_id: class_name for class_id, class_name in labels} - - assert dataset - old_percent = -1 - for index in range(sample_count): - percent = 100 * (index + 1) // sample_count - if percent > old_percent: - logger.info(f"creating entries: {percent}%") - old_percent = percent - - image_full_path, class_index = dataset.samples[index] - image_relpath = os.path.relpath(image_full_path, self.root) - class_id, actual_index = split.parse_image_relpath(image_relpath) - class_name = class_names[class_id] - entries_array[index] = (actual_index, class_index, class_id, class_name) - - logger.info(f'saving entries to "{self._entries_path}"') - self._save_extra(entries_array, self._entries_path) - - def _dump_class_ids_and_names(self) -> None: - split = self.split - if split == ImageNet.Split.TEST: - return - - entries_array = self._load_extra(self._entries_path) - - max_class_id_length, max_class_name_length, max_class_index = -1, -1, -1 - for entry in entries_array: - class_index, class_id, class_name = ( - entry["class_index"], - entry["class_id"], - entry["class_name"], - ) - max_class_index = max(int(class_index), max_class_index) - max_class_id_length = max(len(str(class_id)), max_class_id_length) - max_class_name_length = max(len(str(class_name)), max_class_name_length) - - class_count = max_class_index + 1 - class_ids_array = np.empty(class_count, dtype=f"U{max_class_id_length}") - class_names_array = np.empty(class_count, dtype=f"U{max_class_name_length}") - for entry in entries_array: - class_index, class_id, class_name = ( - entry["class_index"], - entry["class_id"], - entry["class_name"], - ) - class_ids_array[class_index] = class_id - class_names_array[class_index] = class_name - - logger.info(f'saving class IDs to "{self._class_ids_path}"') - self._save_extra(class_ids_array, self._class_ids_path) - - logger.info(f'saving class names to "{self._class_names_path}"') - self._save_extra(class_names_array, self._class_names_path) - - def dump_extra(self) -> None: - self._dump_entries() - self._dump_class_ids_and_names() diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/data/datasets/image_net_22k.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/data/datasets/image_net_22k.py deleted file mode 100644 index f226f2300f6dc8f847e853f763b9ff0e5a543cb0..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/data/datasets/image_net_22k.py +++ /dev/null @@ -1,301 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import os -import warnings -from dataclasses import dataclass -from enum import Enum -from functools import lru_cache -from gzip import GzipFile -from io import BytesIO -from mmap import ACCESS_READ, mmap -from typing import Any, Callable, List, Optional, Set, Tuple - -import numpy as np - -from .extended import ExtendedVisionDataset - -_Labels = int - -_DEFAULT_MMAP_CACHE_SIZE = 16 # Warning: This can exhaust file descriptors - - -@dataclass -class _ClassEntry: - block_offset: int - maybe_filename: Optional[str] = None - - -@dataclass -class _Entry: - class_index: int # noqa: E701 - start_offset: int - end_offset: int - filename: str - - -class _Split(Enum): - TRAIN = "train" - VAL = "val" - - @property - def length(self) -> int: - return { - _Split.TRAIN: 11_797_647, - _Split.VAL: 561_050, - }[self] - - def entries_path(self): - return f"imagenet21kp_{self.value}.txt" - - -def _get_tarball_path(class_id: str) -> str: - return f"{class_id}.tar" - - -def _make_mmap_tarball(tarballs_root: str, mmap_cache_size: int): - @lru_cache(maxsize=mmap_cache_size) - def _mmap_tarball(class_id: str) -> mmap: - tarball_path = _get_tarball_path(class_id) - tarball_full_path = os.path.join(tarballs_root, tarball_path) - with open(tarball_full_path) as f: - return mmap(fileno=f.fileno(), length=0, access=ACCESS_READ) - - return _mmap_tarball - - -class ImageNet22k(ExtendedVisionDataset): - _GZIPPED_INDICES: Set[int] = { - 841_545, - 1_304_131, - 2_437_921, - 2_672_079, - 2_795_676, - 2_969_786, - 6_902_965, - 6_903_550, - 6_903_628, - 7_432_557, - 7_432_589, - 7_813_809, - 8_329_633, - 10_296_990, - 10_417_652, - 10_492_265, - 10_598_078, - 10_782_398, - 10_902_612, - 11_203_736, - 11_342_890, - 11_397_596, - 11_589_762, - 11_705_103, - 12_936_875, - 13_289_782, - } - Labels = _Labels - - def __init__( - self, - *, - root: str, - extra: str, - transforms: Optional[Callable] = None, - transform: Optional[Callable] = None, - target_transform: Optional[Callable] = None, - mmap_cache_size: int = _DEFAULT_MMAP_CACHE_SIZE, - ) -> None: - super().__init__(root, transforms, transform, target_transform) - self._extra_root = extra - - entries_path = self._get_entries_path(root) - self._entries = self._load_extra(entries_path) - - class_ids_path = self._get_class_ids_path(root) - self._class_ids = self._load_extra(class_ids_path) - - self._gzipped_indices = ImageNet22k._GZIPPED_INDICES - self._mmap_tarball = _make_mmap_tarball(self._tarballs_root, mmap_cache_size) - - def _get_entries_path(self, root: Optional[str] = None) -> str: - return "entries.npy" - - def _get_class_ids_path(self, root: Optional[str] = None) -> str: - return "class-ids.npy" - - def _find_class_ids(self, path: str) -> List[str]: - class_ids = [] - - with os.scandir(path) as entries: - for entry in entries: - root, ext = os.path.splitext(entry.name) - if ext != ".tar": - continue - class_ids.append(root) - - return sorted(class_ids) - - def _load_entries_class_ids(self, root: Optional[str] = None) -> Tuple[List[_Entry], List[str]]: - root = self.get_root(root) - entries: List[_Entry] = [] - class_ids = self._find_class_ids(root) - - for class_index, class_id in enumerate(class_ids): - path = os.path.join(root, "blocks", f"{class_id}.log") - class_entries = [] - - try: - with open(path) as f: - for line in f: - line = line.rstrip() - block, filename = line.split(":") - block_offset = int(block[6:]) - filename = filename[1:] - - maybe_filename = None - if filename != "** Block of NULs **": - maybe_filename = filename - _, ext = os.path.splitext(filename) - # assert ext == ".JPEG" - - class_entry = _ClassEntry(block_offset, maybe_filename) - class_entries.append(class_entry) - except OSError as e: - raise RuntimeError(f'can not read blocks file "{path}"') from e - - assert class_entries[-1].maybe_filename is None - - for class_entry1, class_entry2 in zip(class_entries, class_entries[1:]): - assert class_entry1.block_offset <= class_entry2.block_offset - start_offset = 512 * class_entry1.block_offset - end_offset = 512 * class_entry2.block_offset - assert class_entry1.maybe_filename is not None - filename = class_entry1.maybe_filename - entry = _Entry(class_index, start_offset, end_offset, filename) - # Skip invalid image files (PIL throws UnidentifiedImageError) - if filename == "n06470073_47249.JPEG": - continue - entries.append(entry) - - return entries, class_ids - - def _load_extra(self, extra_path: str) -> np.ndarray: - extra_root = self._extra_root - extra_full_path = os.path.join(extra_root, extra_path) - return np.load(extra_full_path, mmap_mode="r") - - def _save_extra(self, extra_array: np.ndarray, extra_path: str) -> None: - extra_root = self._extra_root - extra_full_path = os.path.join(extra_root, extra_path) - os.makedirs(extra_root, exist_ok=True) - np.save(extra_full_path, extra_array) - - @property - def _tarballs_root(self) -> str: - return self.root - - def find_class_id(self, class_index: int) -> str: - return str(self._class_ids[class_index]) - - def get_image_data(self, index: int) -> bytes: - entry = self._entries[index] - class_id = entry["class_id"] - class_mmap = self._mmap_tarball(class_id) - - start_offset, end_offset = entry["start_offset"], entry["end_offset"] - try: - mapped_data = class_mmap[start_offset:end_offset] - data = mapped_data[512:] # Skip entry header block - - if len(data) >= 2 and tuple(data[:2]) == (0x1F, 0x8B): - assert index in self._gzipped_indices, f"unexpected gzip header for sample {index}" - with GzipFile(fileobj=BytesIO(data)) as g: - data = g.read() - except Exception as e: - raise RuntimeError(f"can not retrieve image data for sample {index} " f'from "{class_id}" tarball') from e - - return data - - def get_target(self, index: int) -> Any: - return int(self._entries[index]["class_index"]) - - def get_targets(self) -> np.ndarray: - return self._entries["class_index"] - - def get_class_id(self, index: int) -> str: - return str(self._entries[index]["class_id"]) - - def get_class_ids(self) -> np.ndarray: - return self._entries["class_id"] - - def __getitem__(self, index: int) -> Tuple[Any, Any]: - with warnings.catch_warnings(): - warnings.simplefilter("ignore") - return super().__getitem__(index) - - def __len__(self) -> int: - return len(self._entries) - - def _dump_entries(self, *args, **kwargs) -> None: - entries, class_ids = self._load_entries_class_ids(*args, **kwargs) - - max_class_id_length, max_filename_length, max_class_index = -1, -1, -1 - for entry in entries: - class_id = class_ids[entry.class_index] - max_class_index = max(entry.class_index, max_class_index) - max_class_id_length = max(len(class_id), max_class_id_length) - max_filename_length = max(len(entry.filename), max_filename_length) - - dtype = np.dtype( - [ - ("class_index", " None: - entries_path = self._get_entries_path(*args, **kwargs) - entries_array = self._load_extra(entries_path) - - max_class_id_length, max_class_index = -1, -1 - for entry in entries_array: - class_index, class_id = entry["class_index"], entry["class_id"] - max_class_index = max(int(class_index), max_class_index) - max_class_id_length = max(len(str(class_id)), max_class_id_length) - - class_ids_array = np.empty(max_class_index + 1, dtype=f"U{max_class_id_length}") - for entry in entries_array: - class_index, class_id = entry["class_index"], entry["class_id"] - class_ids_array[class_index] = class_id - class_ids_path = self._get_class_ids_path(*args, **kwargs) - self._save_extra(class_ids_array, class_ids_path) - - def _dump_extra(self, *args, **kwargs) -> None: - self._dump_entries(*args, *kwargs) - self._dump_class_ids(*args, *kwargs) - - def dump_extra(self, root: Optional[str] = None) -> None: - return self._dump_extra(root) diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/data/datasets/nyu.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/data/datasets/nyu.py deleted file mode 100644 index 33b58986571637a71264c49b3389978f3e5879a4..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/data/datasets/nyu.py +++ /dev/null @@ -1,81 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import os -from enum import Enum -from typing import Any, Callable, Optional, Union - -from PIL import Image - -from .decoders import Decoder, DenseTargetDecoder, ImageDataDecoder -from .extended import ExtendedVisionDataset - - -class _Split(Enum): - TRAIN = "train" - VAL = "val" - TEST = "test" - - @property - def data_fname(self) -> str: - _DATA_FNAMES = { - _Split.TRAIN: "nyu_train.txt", - _Split.VAL: "nyu_test.txt", - _Split.TEST: "nyu_test.txt", - } - return _DATA_FNAMES[self] - - -class NYU(ExtendedVisionDataset): - Split = Union[_Split] - Labels = Union[Image.Image] - - def __init__( - self, - *, - split: "NYU.Split", - root: Optional[str] = None, - transforms: Optional[Callable] = None, - transform: Optional[Callable] = None, - target_transform: Optional[Callable] = None, - image_decoder: Decoder = ImageDataDecoder, - target_decoder: Decoder = DenseTargetDecoder, - ) -> None: - super().__init__( - root=root, - transforms=transforms, - transform=transform, - target_transform=target_transform, - image_decoder=image_decoder, - target_decoder=target_decoder, - ) - self.image_paths = [] - self.target_paths = [] - with open(os.path.join(root, split.data_fname)) as f: - lines = f.readlines() - lines = sorted(lines) - for line in lines: - image_relpath, depth_relpath, _ = line.split() - image_relpath = image_relpath.strip("/") - depth_relpath = depth_relpath.strip("/") - self.image_paths.append(image_relpath) - self.target_paths.append(depth_relpath) - - def get_image_data(self, index: int) -> bytes: - image_relpath = self.image_paths[index] - image_full_path = os.path.join(self.root, image_relpath) - with open(image_full_path, mode="rb") as f: - image_data = f.read() - return image_data - - def get_target(self, index: int) -> Any: - target_relpath = self.target_paths[index] - target_full_path = os.path.join(self.root, target_relpath) - with open(target_full_path, mode="rb") as f: - target_data = f.read() - return target_data - - def __len__(self) -> int: - return len(self.image_paths) diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/data/loaders.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/data/loaders.py deleted file mode 100644 index 6a55ad6cfdd77023ca8663886462761b8f589009..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/data/loaders.py +++ /dev/null @@ -1,242 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import logging -from enum import Enum -from typing import Any, Callable, List, Optional, TypeVar - -import torch -from torch.utils.data import Sampler - -from .datasets import ADE20K, CocoCaptions, ImageNet, ImageNet22k, NYU -from .samplers import EpochSampler, InfiniteSampler, ShardedInfiniteSampler - -logger = logging.getLogger("dinov3") - - -class SamplerType(Enum): - DISTRIBUTED = 0 - EPOCH = 1 - INFINITE = 2 - SHARDED_INFINITE = 3 - SHARDED_INFINITE_NEW = 4 - - -def _make_bool_str(b: bool) -> str: - return "yes" if b else "no" - - -def _make_sample_transform( - image_transform: Optional[Callable] = None, - target_transform: Optional[Callable] = None, -): - def transform(sample): - image, target = sample - if image_transform is not None: - image = image_transform(image) - if target_transform is not None: - target = target_transform(target) - return image, target - - return transform - - -def _parse_dataset_str(dataset_str: str): - tokens = dataset_str.split(":") - - name = tokens[0] - kwargs = {} - - for token in tokens[1:]: - key, value = token.split("=") - assert key in ("root", "extra", "split") - kwargs[key] = value - - if name == "ImageNet": - class_ = ImageNet - if "split" in kwargs: - kwargs["split"] = ImageNet.Split[kwargs["split"]] - elif name == "ImageNet22k": - class_ = ImageNet22k - elif name == "ADE20K": - class_ = ADE20K - if "split" in kwargs: - kwargs["split"] = ADE20K.Split[kwargs["split"]] - elif name == "CocoCaptions": - class_ = CocoCaptions - if "split" in kwargs: - kwargs["split"] = CocoCaptions.Split[kwargs["split"]] - elif name == "NYU": - class_ = NYU - if "split" in kwargs: - kwargs["split"] = NYU.Split[kwargs["split"]] - else: - raise ValueError(f'Unsupported dataset "{name}"') - - return class_, kwargs - - -def make_dataset( - *, - dataset_str: str, - transform: Optional[Callable] = None, - target_transform: Optional[Callable] = None, - transforms: Optional[Callable] = None, -): - """ - Creates a dataset with the specified parameters. - - Args: - dataset_str: A dataset string description (e.g. ImageNet:split=TRAIN). - transform: A transform to apply to images. - target_transform: A transform to apply to targets. - transforms: A transform to apply to both images and targets. - - Returns: - The created dataset. - """ - logger.info(f'using dataset: "{dataset_str}"') - - class_, kwargs = _parse_dataset_str(dataset_str) - dataset = class_(transform=transform, target_transform=target_transform, transforms=transforms, **kwargs) - - logger.info(f"# of dataset samples: {len(dataset):,d}") - - # Aggregated datasets do not expose (yet) these attributes, so add them. - if not hasattr(dataset, "transform"): - dataset.transform = transform - if not hasattr(dataset, "target_transform"): - dataset.target_transform = target_transform - if not hasattr(dataset, "transforms"): - dataset.transforms = transforms - - return dataset - - -def _make_sampler( - *, - dataset, - type: Optional[SamplerType] = None, - shuffle: bool = False, - seed: int = 0, - size: int = -1, - advance: int = 0, -) -> Optional[Sampler]: - sample_count = len(dataset) - - if type == SamplerType.INFINITE: - logger.info("sampler: infinite") - if size > 0: - raise ValueError("sampler size > 0 is invalid") - return InfiniteSampler( - sample_count=sample_count, - shuffle=shuffle, - seed=seed, - advance=advance, - ) - elif type in (SamplerType.SHARDED_INFINITE, SamplerType.SHARDED_INFINITE_NEW): - logger.info("sampler: sharded infinite") - if size > 0: - raise ValueError("sampler size > 0 is invalid") - use_new_shuffle_tensor_slice = type == SamplerType.SHARDED_INFINITE_NEW - return ShardedInfiniteSampler( - sample_count=sample_count, - shuffle=shuffle, - seed=seed, - advance=advance, - use_new_shuffle_tensor_slice=use_new_shuffle_tensor_slice, - ) - elif type == SamplerType.EPOCH: - logger.info("sampler: epoch") - if advance > 0: - raise NotImplementedError("sampler advance > 0 is not supported") - size = size if size > 0 else sample_count - logger.info(f"# of samples / epoch: {size:,d}") - return EpochSampler( - size=size, - sample_count=sample_count, - shuffle=shuffle, - seed=seed, - ) - elif type == SamplerType.DISTRIBUTED: - logger.info("sampler: distributed") - if size > 0: - raise ValueError("sampler size > 0 is invalid") - if advance > 0: - raise ValueError("sampler advance > 0 is invalid") - return torch.utils.data.DistributedSampler( - dataset=dataset, - shuffle=shuffle, - seed=seed, - drop_last=False, - ) - - logger.info("sampler: none") - return None - - -T = TypeVar("T") - - -def make_data_loader( - *, - dataset, - batch_size: int, - num_workers: int, - shuffle: bool = True, - seed: int = 0, - sampler_type: Optional[SamplerType] = SamplerType.INFINITE, - sampler_size: int = -1, - sampler_advance: int = 0, - drop_last: bool = True, - persistent_workers: bool = False, - collate_fn: Optional[Callable[[List[T]], Any]] = None, - worker_init_fn: Optional[Callable[[List[T]], Any]] = None, -): - """ - Creates a data loader with the specified parameters. - - Args: - dataset: A dataset (third party, LaViDa or WebDataset). - batch_size: The size of batches to generate. - num_workers: The number of workers to use. - shuffle: Whether to shuffle samples. - seed: The random seed to use. - sampler_type: Which sampler to use: EPOCH, INFINITE, SHARDED_INFINITE, SHARDED_INFINITE_NEW, DISTRIBUTED or None. - sampler_size: The number of images per epoch (when applicable) or -1 for the entire dataset. - sampler_advance: How many samples to skip (when applicable). - drop_last: Whether the last non-full batch of data should be dropped. - persistent_workers: maintain the workers Dataset instances alive after a dataset has been consumed once. - collate_fn: Function that performs batch collation - worker_init_fn: Optional init function for each dataloader worker. - """ - - sampler = _make_sampler( - dataset=dataset, - type=sampler_type, - shuffle=shuffle, - seed=seed, - size=sampler_size, - advance=sampler_advance, - ) - - logger.info("using PyTorch data loader") - data_loader = torch.utils.data.DataLoader( - dataset, - sampler=sampler, - batch_size=batch_size, - num_workers=num_workers, - pin_memory=True, - drop_last=drop_last, - persistent_workers=persistent_workers, - collate_fn=collate_fn, - worker_init_fn=worker_init_fn, - ) - - try: - logger.info(f"# of batches: {len(data_loader):,d}") - except TypeError: # data loader has no length - logger.info("infinite data loader") - return data_loader diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/data/masking.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/data/masking.py deleted file mode 100644 index 691c31142c985dc9e71c5923fcaa60e199f21e83..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/data/masking.py +++ /dev/null @@ -1,94 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import math -import random - -import numpy as np - - -class MaskingGenerator: - def __init__( - self, - input_size, - num_masking_patches=None, - min_num_patches=4, - max_num_patches=None, - min_aspect=0.3, - max_aspect=None, - ): - if not isinstance(input_size, tuple): - input_size = (input_size,) * 2 - self.height, self.width = input_size - - self.num_patches = self.height * self.width - self.num_masking_patches = num_masking_patches - - self.min_num_patches = min_num_patches - self.max_num_patches = num_masking_patches if max_num_patches is None else max_num_patches - - max_aspect = max_aspect or 1 / min_aspect - self.log_aspect_ratio = (math.log(min_aspect), math.log(max_aspect)) - - def __repr__(self): - repr_str = "Generator(%d, %d -> [%d ~ %d], max = %d, %.3f ~ %.3f)" % ( - self.height, - self.width, - self.min_num_patches, - self.max_num_patches, - self.num_masking_patches, - self.log_aspect_ratio[0], - self.log_aspect_ratio[1], - ) - return repr_str - - def get_shape(self): - return self.height, self.width - - def _mask(self, mask, max_mask_patches): - delta = 0 - for _ in range(10): - target_area = random.uniform(self.min_num_patches, max_mask_patches) - aspect_ratio = math.exp(random.uniform(*self.log_aspect_ratio)) - h = int(round(math.sqrt(target_area * aspect_ratio))) - w = int(round(math.sqrt(target_area / aspect_ratio))) - if w < self.width and h < self.height: - top = random.randint(0, self.height - h) - left = random.randint(0, self.width - w) - - num_masked = mask[top : top + h, left : left + w].sum() - # Overlap - if 0 < h * w - num_masked <= max_mask_patches: - for i in range(top, top + h): - for j in range(left, left + w): - if mask[i, j] == 0: - mask[i, j] = 1 - delta += 1 - - if delta > 0: - break - return delta - - def __call__(self, num_masking_patches=0): - mask = np.zeros(shape=self.get_shape(), dtype=bool) - mask_count = 0 - while mask_count < num_masking_patches: - max_mask_patches = num_masking_patches - mask_count - max_mask_patches = min(max_mask_patches, self.max_num_patches) - - delta = self._mask(mask, max_mask_patches) - if delta == 0: - break - else: - mask_count += delta - - return self.complete_mask_randomly(mask, num_masking_patches) - - def complete_mask_randomly(self, mask, num_masking_patches): - shape = mask.shape - m2 = mask.flatten() - to_add = np.random.choice(np.where(~m2)[0], size=num_masking_patches - m2.sum(), replace=False) - m2[to_add] = True - return m2.reshape(shape) diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/data/meta_loaders.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/data/meta_loaders.py deleted file mode 100644 index b82faa3be1605050b14089c208b827c7d1e52f14..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/data/meta_loaders.py +++ /dev/null @@ -1,78 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import logging -from typing import Any, Iterable, Iterator, List, Tuple, TypeVar - -import numpy as np - -logger = logging.getLogger("dinov3") -Loader = Iterable[List[Any]] -T = TypeVar("T") - - -class CombinedDataLoader: - """ - Combines data loaders using the provided sampling ratios - """ - - GLOBAL_HOMOGENEOUS = 0 - LOCAL_HOMOGENEOUS = 1 - - def __init__( - self, - loaders_with_ratios: Iterable[Tuple[Loader, float]], - batch_size: int, - combining_mode: int = 1, - seed: int = 65537, - name: str = None, - logging_period: int = 100, - ): - if combining_mode not in [self.GLOBAL_HOMOGENEOUS, self.LOCAL_HOMOGENEOUS]: - raise ValueError(f"Unsupported value of combining_mode ({combining_mode})") - loaders, ratios = zip(*loaders_with_ratios) - assert np.all([loader.batch_size == batch_size for loader in loaders]), ( - f"All individual loaders must have the same batch size to the combined data loader for combining_mode={combining_mode}" - ) - self.loaders = loaders - self.ratios = ratios - self.batch_size = batch_size - self.combining_mode = combining_mode - self.initial_seed = seed - self.name = name if name is not None else "" - self.logging_period = logging_period - if combining_mode == self.GLOBAL_HOMOGENEOUS: - logger.info(f"Initialize CDL {self.name} with seed={seed}") - self.seed = seed - self.rng = np.random.default_rng(seed=seed) - else: - logger.info(f"Initialize CDL {self.name} with random seed") - self.seed = 0 - self.rng = np.random.default_rng() - self.loader_count = np.zeros(len(self.loaders)) - - def homogeneous_iterator(self) -> Iterator[List[Any]]: - iteration = 0 - iters = [iter(loader) for loader in self.loaders] - while True: - iteration += 1 - try: - idx = self.rng.choice(len(self.loaders), p=self.ratios) - self.loader_count[idx] += 1 - if iteration % self.logging_period == 0: - logger.info(f"Empirical ratios: CDL {self.name} {self.loader_count / self.loader_count.sum()}") - yield next(iters[idx]) - except StopIteration: - break - - def heterogeneous_iterator(self) -> Iterator[List[Any]]: - pass - - def __iter__(self) -> Iterator[List[Any]]: - if self.combining_mode in [self.GLOBAL_HOMOGENEOUS, self.LOCAL_HOMOGENEOUS]: - logger.info(f"Using homogeneous iterator for CDL {self.name}") - return self.homogeneous_iterator() - else: - raise ValueError(f"Unsupported value of combining_mode ({self.combining_mode})") diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/data/samplers.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/data/samplers.py deleted file mode 100644 index b58145ba547909d9fcbeeff0ada3438f351f3bf9..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/data/samplers.py +++ /dev/null @@ -1,229 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import itertools -import warnings -from typing import Any, Optional - -import numpy as np -import torch -from torch.utils.data.sampler import Sampler - -from dinov3.distributed import get_rank, get_world_size - - -class EpochSampler(Sampler): - def __init__( - self, - *, - size: int, - sample_count: int, - shuffle: bool = False, - seed: int = 0, - start: Optional[int] = None, - step: Optional[int] = None, - ): - self._size = size - self._sample_count = sample_count - self._shuffle = shuffle - self._seed = seed - self._start = get_rank() if start is None else start - self._step = get_world_size() if step is None else step - self._epoch = 0 - - def __iter__(self): - count = (self._size + self._sample_count - 1) // self._sample_count - tiled_indices = np.tile(np.arange(self._sample_count), count) - if self._shuffle: - seed = self._seed * self._epoch if self._seed != 0 else self._epoch - rng = np.random.default_rng(seed) - iterable = rng.choice(tiled_indices, self._size, replace=False) - else: - iterable = tiled_indices[: self._size] - - yield from itertools.islice(iterable, self._start, None, self._step) - - def __len__(self): - return (self._size - self._start + self._step - 1) // self._step - - def set_epoch(self, epoch): - self._epoch = epoch - - -def _get_numpy_dtype(size: int) -> Any: - return np.int32 if size <= 2**31 else np.int64 - - -def _get_torch_dtype(size: int) -> Any: - return torch.int32 if size <= 2**31 else torch.int64 - - -def _generate_randperm_indices(*, size: int, generator: torch.Generator): - """Generate the indices of a random permutation.""" - dtype = _get_torch_dtype(size) - # This is actually matching PyTorch's CPU implementation, see: https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/TensorFactories.cpp#L900-L921 - perm = torch.arange(size, dtype=dtype) - for i in range(size): - j = torch.randint(i, size, size=(1,), generator=generator).item() - - # Always swap even if no-op - value = perm[j].item() - perm[j] = perm[i].item() - perm[i] = value - yield value - - -class InfiniteSampler(Sampler): - def __init__( - self, - *, - sample_count: int, - shuffle: bool = False, - seed: int = 0, - start: Optional[int] = None, - step: Optional[int] = None, - advance: int = 0, - ): - self._sample_count = sample_count - self._seed = seed - self._shuffle = shuffle - self._start = get_rank() if start is None else start - self._step = get_world_size() if step is None else step - self._advance = advance - - def __iter__(self): - if self._shuffle: - iterator = self._shuffled_iterator() - else: - iterator = self._iterator() - - yield from itertools.islice(iterator, self._advance, None) - - def _iterator(self): - assert not self._shuffle - - while True: - iterable = range(self._sample_count) - yield from itertools.islice(iterable, self._start, None, self._step) - - def _shuffled_iterator(self): - assert self._shuffle - - # Instantiate a generator here (rather than in the ctor) to keep the class - # picklable (requirement of mp.spawn) - generator = torch.Generator().manual_seed(self._seed) - - while True: - iterable = _generate_randperm_indices(size=self._sample_count, generator=generator) - yield from itertools.islice(iterable, self._start, None, self._step) - - -# The following function is somewhat equivalent to _new_shuffle_tensor_slice below, -# but avoids a full in-place random permutation generation. -def _shuffle_tensor_slice( - *, tensor: torch.Tensor, start: int = 0, step: int = 1, generator: torch.Generator -) -> np.ndarray: - stop = len(tensor) - count = stop // step - drop_count = stop - step * count - if drop_count: - warnings.warn(f"# of dropped samples: {drop_count}", stacklevel=1) - - dtype = _get_numpy_dtype(stop) - result = np.empty(count, dtype=dtype) - - for i in range(count): - j = torch.randint(0, i + 1, size=(1,), generator=generator).item() if i > 0 else 0 - - result[i] = result[j] - result[j] = tensor[start + i * step].item() - - return result - - -def _new_shuffle_tensor_slice( - *, tensor: torch.Tensor, start: int = 0, step: int = 1, generator: torch.Generator -) -> np.ndarray: - stop = len(tensor) - count = stop // step - dtype = torch.int64 # Needed for using randperm result as indices - count = stop // step - drop_count = stop - step * count - if drop_count: - warnings.warn(f"# of dropped samples: {drop_count}", stacklevel=1) - indices = torch.randperm(count, dtype=dtype, generator=generator) - return tensor[start::step][indices].numpy() - - -def _make_seed(seed: int, start: int, iter_count: int) -> int: - # NOTE: Tried a few variants (including iter_count << 32), this one worked best. - return seed + start + (iter_count << 24) - - -class ShardedInfiniteSampler(Sampler): - def __init__( - self, - *, - sample_count: int, - shuffle: bool = False, - seed: int = 0, - start: Optional[int] = None, - step: Optional[int] = None, - advance: int = 0, - use_new_shuffle_tensor_slice: bool = False, - ): - self._sample_count = sample_count - self._seed = seed - self._shuffle = shuffle - self._start = get_rank() if start is None else start - self._step = get_world_size() if step is None else step - self._advance = advance - self._iter_count = 0 - self._shuffle_tensor_slice_fn = ( - _new_shuffle_tensor_slice if use_new_shuffle_tensor_slice else _shuffle_tensor_slice - ) - - def __iter__(self): - iter_count = self._advance // self._sample_count - if iter_count > 0: - self._advance -= iter_count * self._sample_count - self._iter_count += iter_count - - if self._shuffle: - iterator = self._shuffled_iterator() - else: - iterator = self._iterator() - - yield from itertools.islice(iterator, self._advance, None) - - def _iterator(self): - assert not self._shuffle - - while True: - iterable = range(self._sample_count) - yield from itertools.islice(iterable, self._start, None, self._step) - - def _shuffled_iterator(self): - assert self._shuffle - - # Instantiate a generator here (rather than in the ctor) to be keep the class - # picklable (requirement of mp.spawn) - generator = torch.Generator() - - # Always shuffle everything first - generator.manual_seed(self._seed) - dtype = _get_torch_dtype(self._sample_count) - perm = torch.randperm(self._sample_count, dtype=dtype, generator=generator) - - while True: - # Re-seed on each iteration to allow skipping whole permutations - seed = _make_seed(self._seed, self._start, self._iter_count) - generator.manual_seed(seed) - - iterable = self._shuffle_tensor_slice_fn( - tensor=perm, start=self._start, step=self._step, generator=generator - ) - yield from iterable - self._iter_count += 1 diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/data/transforms.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/data/transforms.py deleted file mode 100644 index 3104c827b5144a4bd87b7d997a191595d703e107..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/data/transforms.py +++ /dev/null @@ -1,165 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import logging -from typing import Sequence - -import torch -from torchvision.transforms import v2 - -logger = logging.getLogger("dinov3") - - -def make_interpolation_mode(mode_str: str) -> v2.InterpolationMode: - return {mode.value: mode for mode in v2.InterpolationMode}[mode_str] - - -class GaussianBlur(v2.RandomApply): - """ - Apply Gaussian Blur to the PIL image. - """ - - def __init__(self, *, p: float = 0.5, radius_min: float = 0.1, radius_max: float = 2.0): - # NOTE: torchvision is applying 1 - probability to return the original image - keep_p = 1 - p - transform = v2.GaussianBlur(kernel_size=9, sigma=(radius_min, radius_max)) - super().__init__(transforms=[transform], p=keep_p) - - -# Use timm's names -IMAGENET_DEFAULT_MEAN = (0.485, 0.456, 0.406) -IMAGENET_DEFAULT_STD = (0.229, 0.224, 0.225) - -CROP_DEFAULT_SIZE = 224 -RESIZE_DEFAULT_SIZE = int(256 * CROP_DEFAULT_SIZE / 224) - - -def make_normalize_transform( - mean: Sequence[float] = IMAGENET_DEFAULT_MEAN, - std: Sequence[float] = IMAGENET_DEFAULT_STD, -) -> v2.Normalize: - return v2.Normalize(mean=mean, std=std) - - -def make_base_transform( - mean: Sequence[float] = IMAGENET_DEFAULT_MEAN, - std: Sequence[float] = IMAGENET_DEFAULT_STD, -) -> v2.Normalize: - return v2.Compose( - [ - v2.ToDtype(torch.float32, scale=True), - make_normalize_transform(mean=mean, std=std), - ] - ) - - -# This roughly matches torchvision's preset for classification training: -# https://github.com/pytorch/vision/blob/main/references/classification/presets.py#L6-L44 -def make_classification_train_transform( - *, - crop_size: int = CROP_DEFAULT_SIZE, - interpolation=v2.InterpolationMode.BICUBIC, - hflip_prob: float = 0.5, - mean: Sequence[float] = IMAGENET_DEFAULT_MEAN, - std: Sequence[float] = IMAGENET_DEFAULT_STD, -): - transforms_list = [v2.ToImage(), v2.RandomResizedCrop(crop_size, interpolation=interpolation)] - if hflip_prob > 0.0: - transforms_list.append(v2.RandomHorizontalFlip(hflip_prob)) - transforms_list.append(make_base_transform(mean, std)) - transform = v2.Compose(transforms_list) - logger.info(f"Built classification train transform\n{transform}") - return transform - - -def make_resize_transform( - *, - resize_size: int, - resize_square: bool = False, - resize_large_side: bool = False, # Set the larger side to resize_size instead of the smaller - interpolation: v2.InterpolationMode = v2.InterpolationMode.BICUBIC, -): - assert not (resize_square and resize_large_side), "These two options can not be set together" - if resize_square: - logger.info("resizing image as a square") - size = (resize_size, resize_size) - transform = v2.Resize(size=size, interpolation=interpolation) - return transform - elif resize_large_side: - logger.info("resizing based on large side") - transform = v2.Resize(size=None, max_size=resize_size, interpolation=interpolation) - return transform - else: - transform = v2.Resize(resize_size, interpolation=interpolation) - return transform - - -# Derived from make_classification_eval_transform() with more control over resize and crop -def make_eval_transform( - *, - resize_size: int = RESIZE_DEFAULT_SIZE, - crop_size: int = CROP_DEFAULT_SIZE, - resize_square: bool = False, - resize_large_side: bool = False, # Set the larger side to resize_size instead of the smaller - interpolation: v2.InterpolationMode = v2.InterpolationMode.BICUBIC, - mean: Sequence[float] = IMAGENET_DEFAULT_MEAN, - std: Sequence[float] = IMAGENET_DEFAULT_STD, -) -> v2.Compose: - transforms_list = [v2.ToImage()] - resize_transform = make_resize_transform( - resize_size=resize_size, - resize_square=resize_square, - resize_large_side=resize_large_side, - interpolation=interpolation, - ) - transforms_list.append(resize_transform) - if crop_size: - transforms_list.append(v2.CenterCrop(crop_size)) - transforms_list.append(make_base_transform(mean, std)) - transform = v2.Compose(transforms_list) - logger.info(f"Built eval transform\n{transform}") - return transform - - -# This matches (roughly) torchvision's preset for classification evaluation: -# https://github.com/pytorch/vision/blob/main/references/classification/presets.py#L47-L69 -def make_classification_eval_transform( - *, - resize_size: int = RESIZE_DEFAULT_SIZE, - crop_size: int = CROP_DEFAULT_SIZE, - interpolation=v2.InterpolationMode.BICUBIC, - mean: Sequence[float] = IMAGENET_DEFAULT_MEAN, - std: Sequence[float] = IMAGENET_DEFAULT_STD, -) -> v2.Compose: - return make_eval_transform( - resize_size=resize_size, - crop_size=crop_size, - interpolation=interpolation, - mean=mean, - std=std, - resize_square=False, - resize_large_side=False, - ) - - -def voc2007_classification_target_transform(label, n_categories=20): - one_hot = torch.zeros(n_categories, dtype=int) - for instance in label.instances: - one_hot[instance.category_id] = True - return one_hot - - -def imaterialist_classification_target_transform(label, n_categories=294): - one_hot = torch.zeros(n_categories, dtype=int) - one_hot[label.attributes] = True - return one_hot - - -def get_target_transform(dataset_str): - if "VOC2007" in dataset_str: - return voc2007_classification_target_transform - elif "IMaterialist" in dataset_str: - return imaterialist_classification_target_transform - return None diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/distributed/__init__.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/distributed/__init__.py deleted file mode 100644 index 3c6e4f69488f492cc13d5584b1ddc17d7b3a063a..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/distributed/__init__.py +++ /dev/null @@ -1,24 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -# isort: skip_file -from .torch_distributed_wrapper import ( - disable_distributed as disable, - enable_distributed as enable, - get_default_process_group, - get_process_subgroup, - get_rank, - get_subgroup_rank, - get_subgroup_size, - get_world_size, - is_distributed_enabled as is_enabled, - is_main_process, - is_subgroup_main_process, - new_subgroups, - save_in_main_process, - TorchDistributedEnvironment, -) - -from .torch_distributed_primitives import gather_all_tensors, reduce_dict diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/distributed/__pycache__/__init__.cpython-310.pyc b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/distributed/__pycache__/__init__.cpython-310.pyc deleted file mode 100644 index c14cd1b2ba6a315de76b28c32f0844f58c4dc11a..0000000000000000000000000000000000000000 Binary files a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/distributed/__pycache__/__init__.cpython-310.pyc and /dev/null differ diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/distributed/__pycache__/torch_distributed_primitives.cpython-310.pyc b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/distributed/__pycache__/torch_distributed_primitives.cpython-310.pyc deleted file mode 100644 index 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b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/distributed/torch_distributed_primitives.py deleted file mode 100644 index 2cac0438ebcca4b0a496894be638641ea5a28471..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/distributed/torch_distributed_primitives.py +++ /dev/null @@ -1,100 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -from typing import Any, Dict, List, Optional - -import torch -import torch.distributed as dist -from torch.nn import functional as F - -from .torch_distributed_wrapper import get_default_process_group, get_world_size - - -def reduce_dict(input_dict: Dict[Any, torch.Tensor], average: bool = True) -> Dict[Any, torch.Tensor]: - """ - Reduce the values in the dictionary from all processes so that all processes - have the averaged results. Returns a dictionary with the same fields as - the input dictionary, after reduction. - - Args: - input_dict (dict): all the values will be reduced - average (bool): whether to do average or sum - """ - world_size = get_world_size() - if world_size <= 1: - return input_dict - with torch.no_grad(): - names = [] - values = [] - # Sort the keys so that they are consistent across processes - for k in sorted(input_dict.keys()): - names.append(k) - values.append(input_dict[k]) - stacked_values = torch.stack(values, dim=0) - dist.all_reduce(stacked_values) - if average: - stacked_values /= world_size - reduced_dict = {k: v for k, v in zip(names, stacked_values)} - return reduced_dict - - -def _simple_gather_all_tensors(result: torch.Tensor, group: Any, world_size: int) -> List[torch.Tensor]: - gathered_result = [torch.zeros_like(result) for _ in range(world_size)] - dist.all_gather(gathered_result, result, group) - return gathered_result - - -def gather_all_tensors(result: torch.Tensor, group: Optional[Any] = None) -> List[torch.Tensor]: - """ - Copied from https://github.com/Lightning-AI/torchmetrics/blob/master/src/torchmetrics/utilities/distributed.py - Gather all tensors from several ddp processes onto a list that is broadcasted to all processes. - - Works on tensors that have the same number of dimensions, but where each dimension may differ. In this case - tensors are padded, gathered and then trimmed to secure equal workload for all processes. - - Args: - result: the value to sync - group: the process group to gather results from. Defaults to all processes (world) - - Return: - list with size equal to the process group where element i corresponds to result tensor from process i - """ - if group is None: - group = get_default_process_group() - - # convert tensors to contiguous format - result = result.contiguous() - - world_size = get_world_size() - dist.barrier(group=group) - - # if the tensor is scalar, things are easy - if result.ndim == 0: - return _simple_gather_all_tensors(result, group, world_size) - - # 1. Gather sizes of all tensors - local_size = torch.tensor(result.shape, device=result.device) - local_sizes = [torch.zeros_like(local_size) for _ in range(world_size)] - dist.all_gather(local_sizes, local_size, group=group) - max_size = torch.stack(local_sizes).max(dim=0).values - all_sizes_equal = all(all(ls == max_size) for ls in local_sizes) - - # 2. If shapes are all the same, then do a simple gather: - if all_sizes_equal: - return _simple_gather_all_tensors(result, group, world_size) - - # 3. If not, we need to pad each local tensor to maximum size, gather and then truncate - pad_dims = [] - pad_by = (max_size - local_size).detach().cpu() - for val in reversed(pad_by): - pad_dims.append(0) - pad_dims.append(val.item()) - result_padded = F.pad(result, pad_dims) - gathered_result = [torch.zeros_like(result_padded) for _ in range(world_size)] - dist.all_gather(gathered_result, result_padded, group) - for idx, item_size in enumerate(local_sizes): - slice_param = [slice(dim_size) for dim_size in item_size] - gathered_result[idx] = gathered_result[idx][slice_param] - return gathered_result diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/distributed/torch_distributed_wrapper.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/distributed/torch_distributed_wrapper.py deleted file mode 100644 index 36fbe1b7c8dac03c37a042964d74af51ac3c3e7a..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/distributed/torch_distributed_wrapper.py +++ /dev/null @@ -1,351 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import logging -import os -import random -import socket -import subprocess -from datetime import timedelta -from enum import Enum -from typing import List, Sequence - -import torch -import torch.distributed as dist - -logger = logging.getLogger("dinov3") - -_DEFAULT_PROCESS_GROUP = None -_PROCESS_SUBGROUP = None -_BUILTIN_PRINT = None - - -def is_distributed_enabled() -> bool: - """ - Returns: - True if distributed training is enabled. - """ - return dist.is_available() and dist.is_initialized() - - -def get_rank(group=None) -> int: - """ - Returns: - The rank of the current process within the specified process group. - """ - if not is_distributed_enabled(): - return 0 - return dist.get_rank(group=group) - - -def get_world_size(group=None) -> int: - """ - Returns: - The number of processes in the specified process group. - """ - if not is_distributed_enabled(): - return 1 - return dist.get_world_size(group=group) - - -def is_main_process(group=None) -> bool: - """ - Returns: - True if the current process is the main one in the specified process group. - """ - return get_rank(group) == 0 - - -def save_in_main_process(*args, **kwargs) -> None: - """Utility function to save only from the main process.""" - group = kwargs.pop("group", None) - if not is_main_process(group): - return - torch.save(*args, **kwargs) - - -def _restrict_print_to_main_process() -> None: - """This function disables printing when not in the main process.""" - import builtins as __builtin__ - - global _BUILTIN_PRINT - _BUILTIN_PRINT = __builtin__.print - - def print(*args, **kwargs): - force = kwargs.pop("force", False) - if is_main_process() or force: - _BUILTIN_PRINT(*args, **kwargs) - - __builtin__.print = print - - -def _get_master_port(seed: int = 0) -> int: - MIN_MASTER_PORT, MAX_MASTER_PORT = (20_000, 60_000) - - master_port_str = os.environ.get("MASTER_PORT") - if master_port_str is None: - rng = random.Random(seed) - return rng.randint(MIN_MASTER_PORT, MAX_MASTER_PORT) - - return int(master_port_str) - - -def _get_available_port() -> int: - with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: - # A "" host address means INADDR_ANY i.e. binding to all interfaces. - # Note this is not compatible with IPv6. - s.bind(("", 0)) - port = s.getsockname()[1] - return port - - -def _parse_slurm_node_list(s: str) -> List[str]: - return subprocess.check_output(["scontrol", "show", "hostnames", s], text=True).splitlines() - - -class JobType(Enum): - TORCHELASTIC = "TorchElastic" - SLURM = "Slurm" - MANUAL = "manual" - - -class TorchDistributedEnvironment: - """ - Helper class to get (and set) distributed job information from the - environment. Identifies and supports (in this order): - - TorchElastic, - - Slurm, - - Manual launch (single-node). - """ - - def __init__(self): - if "TORCHELASTIC_RUN_ID" in os.environ: - # TorchElastic job created with torchrun - self.job_id = os.environ["TORCHELASTIC_RUN_ID"] - self.job_type = JobType.TORCHELASTIC - - self.master_addr = os.environ["MASTER_ADDR"] - self.master_port = int(os.environ["MASTER_PORT"]) - self.rank = int(os.environ["RANK"]) - self.world_size = int(os.environ["WORLD_SIZE"]) - self.local_rank = int(os.environ["LOCAL_RANK"]) - self.local_world_size = int(os.environ["LOCAL_WORLD_SIZE"]) - elif "SLURM_JOB_ID" in os.environ: - # Slurm job created with sbatch, submitit, etc... - self.job_id = int(os.environ["SLURM_JOB_ID"]) - self.job_type = JobType.SLURM - - node_count = int(os.environ["SLURM_JOB_NUM_NODES"]) - nodes = _parse_slurm_node_list(os.environ["SLURM_JOB_NODELIST"]) - assert len(nodes) == node_count - - self.master_addr = nodes[0] - self.master_port = _get_master_port(seed=self.job_id) - self.rank = int(os.environ["SLURM_PROCID"]) - self.world_size = int(os.environ["SLURM_NTASKS"]) - self.local_rank = int(os.environ["SLURM_LOCALID"]) - self.local_world_size = self.world_size // node_count - else: - # Single node and single job launched manually - self.job_id = None - self.job_type = JobType.MANUAL - - self.master_addr = "127.0.0.1" - self.master_port = _get_available_port() - self.rank = 0 - self.world_size = 1 - self.local_rank = 0 - self.local_world_size = 1 - - assert self.rank < self.world_size - assert self.local_rank < self.local_world_size - - def export( - self, - *, - overwrite: bool, - nccl_async_error_handling: bool = False, - ) -> "TorchDistributedEnvironment": - # See the "Environment variable initialization" section from - # https://pytorch.org/docs/stable/distributed.html for the complete list of - # environment variables required for the env:// initialization method. - env_vars = { - "MASTER_ADDR": self.master_addr, - "MASTER_PORT": str(self.master_port), - "RANK": str(self.rank), - "WORLD_SIZE": str(self.world_size), - "LOCAL_RANK": str(self.local_rank), - "LOCAL_WORLD_SIZE": str(self.local_world_size), - } - if nccl_async_error_handling: - env_vars.update( - { - "TORCH_NCCL_ASYNC_ERROR_HANDLING": "1", # "TORCH_" prefix added in PyTorch 2.2 - } - ) - - if not overwrite: - for k, v in env_vars.items(): - # Only check for difference with preset environment variables - if k not in os.environ: - continue - if os.environ[k] == v: - continue - raise RuntimeError(f"Cannot export environment variables as {k} is already set") - - os.environ.update(env_vars) - return self - - @property - def is_main_process(self) -> bool: - return self.rank == 0 - - def __str__(self): - return ( - f"{self.job_type.value} job " - + (f"({self.job_id}) " if self.job_id else "") - + f"using {self.master_addr}:{self.master_port} " # noqa: E231 - f"(rank={self.rank}, world size={self.world_size})" - ) - - def __repr__(self): - return ( - f"{self.__class__.__name__}(" - f"master_addr={self.master_addr}," # noqa: E231 - f"master_port={self.master_port}," # noqa: E231 - f"rank={self.rank}," # noqa: E231 - f"world_size={self.world_size}," # noqa: E231 - f"local_rank={self.local_rank}," # noqa: E231 - f"local_world_size={self.local_world_size}" - ")" - ) - - -def enable_distributed( - *, - set_cuda_current_device: bool = True, - overwrite: bool = False, - nccl_async_error_handling: bool = False, - restrict_print_to_main_process: bool = True, - timeout: timedelta | None = None, -): - """Enable distributed mode. - - Args: - set_cuda_current_device: If True, call torch.cuda.set_device() to set the - current PyTorch CUDA device to the one matching the local rank. - overwrite: If True, overwrites already set variables. Else fails. - nccl_async_error_handling: Enables NCCL asynchronous error handling. As a - side effect, this enables timing out PyTorch distributed operations - after a default 30 minutes delay). - restrict_print_to_main_process: If True, the print function of non-main processes - (ie rank>0) is disabled. Use print(..., force=True) to print anyway. - If False, nothing is changed and all processes can print as usual. - timeout: Timeout for operations executed against the process group. - Default value is 10 minutes for NCCL and 30 minutes for other backends. - """ - global _DEFAULT_PROCESS_GROUP - - if _DEFAULT_PROCESS_GROUP is not None: - raise RuntimeError("Distributed mode has already been enabled") - - torch_env = TorchDistributedEnvironment() - logger.info(f"PyTorch distributed environment: {torch_env}") - torch_env.export( - overwrite=overwrite, - nccl_async_error_handling=nccl_async_error_handling, - ) - - if set_cuda_current_device: - torch.cuda.set_device(torch_env.local_rank) - - dist.init_process_group(backend="nccl", timeout=timeout) - dist.barrier() - - if restrict_print_to_main_process: - _restrict_print_to_main_process() - - # Finalize setup - _DEFAULT_PROCESS_GROUP = torch.distributed.group.WORLD - - -def get_default_process_group(): - return _DEFAULT_PROCESS_GROUP - - -def disable_distributed() -> None: - global _BUILTIN_PRINT - if _BUILTIN_PRINT is not None: - import builtins as __builtin__ - - __builtin__.print = _BUILTIN_PRINT - - global _PROCESS_SUBGROUP - # checking here because get_process_subgroup can return _DEFAULT_PROCESS_GROUP - if _PROCESS_SUBGROUP is not None: - torch.distributed.destroy_process_group(_PROCESS_SUBGROUP) - _PROCESS_SUBGROUP = None - - global _DEFAULT_PROCESS_GROUP - if _DEFAULT_PROCESS_GROUP is not None: # not initialized - torch.distributed.destroy_process_group(_DEFAULT_PROCESS_GROUP) - _DEFAULT_PROCESS_GROUP = None - - -def new_subgroups(all_subgroup_ranks: Sequence[Sequence[int]]): - """Create new process subgroups according to the provided specification. - - Args: - all_subgroup_ranks: a sequence of rank sequences (first rank, ..., last rank), - one for each process subgroup. Example: ((0, 1), (2, 3), (4, 5, 6, 7)). - - Note: - This is similar to the (non-documented) new_subgroups_by_enumeration(). - This should be called once (and not sequentially) to create all subgroups. - """ - all_ranks = tuple(rank for subgroup_ranks in all_subgroup_ranks for rank in subgroup_ranks) - rank = get_rank() - assert len(all_ranks) == len(set(all_ranks)) - assert rank in all_ranks - - global _PROCESS_SUBGROUP - assert _PROCESS_SUBGROUP is None - - for subgroup_ranks in all_subgroup_ranks: - subgroup = torch.distributed.new_group(subgroup_ranks) - if rank in subgroup_ranks: - _PROCESS_SUBGROUP = subgroup - - -def get_process_subgroup(): - """ - Returns: - The process subgroup of this rank (or None). - """ - return _PROCESS_SUBGROUP or _DEFAULT_PROCESS_GROUP - - -def get_subgroup_rank() -> int: - """ - Returns: - The rank of the current process within its process subgroup. - """ - return get_rank(group=get_process_subgroup()) - - -def get_subgroup_size() -> int: - """ - Returns: - The number of processes in the process subgroup - """ - return get_world_size(group=get_process_subgroup()) - - -def is_subgroup_main_process() -> bool: - """ - Returns: - True if the current process is the main one within its process subgroup. - """ - return get_rank(group=get_process_subgroup()) == 0 diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/env/__init__.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/env/__init__.py deleted file mode 100644 index 15278fd217438cd25fc12ba9eda40cc38182b0d8..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/env/__init__.py +++ /dev/null @@ -1,33 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import contextlib -import logging -import os -import tempfile -from typing import Optional - -import submitit.helpers - -logger = logging.getLogger("dinov3") - - -@contextlib.contextmanager -def clean_env(): - try: - # Hide torch.compile() variables from the launched evals - extra_names = ("TRITON_CACHE_DIR", "TORCHINDUCTOR_CACHE_DIR") - ctx = submitit.helpers.clean_env(extra_names=extra_names) - except TypeError as e: - logger.warning("Update submitit to the latest main branch\n%s", e) - ctx = submitit.helpers.clean_env() - with ctx: - yield - - -def set_triton_cache_dir(cache_dir: Optional[str] = None) -> None: - if cache_dir is None: - cache_dir = tempfile.mkdtemp() - os.environ["TRITON_CACHE_DIR"] = cache_dir diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/accumulators.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/accumulators.py deleted file mode 100644 index 092cc7b671bfc4baa9482c0a911de104bc063ada..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/accumulators.py +++ /dev/null @@ -1,77 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import logging -from collections import defaultdict -from typing import Dict, List, Optional - -import torch -from torch import Tensor - -from dinov3.distributed import gather_all_tensors # Gathers tensors of different sizes - -logger = logging.getLogger("dinov3") - - -def _cat_and_gather_tensor_list(tensor_list: List[Tensor]) -> Tensor: - local_cat = torch.cat(tensor_list) - return torch.cat(gather_all_tensors(local_cat)) - - -class Accumulator: - def __init__(self) -> None: - pass - - def update(self, preds: Tensor, target: Tensor, index: Tensor) -> None: - raise NotImplementedError - - def accumulate(self) -> Optional[Dict[str, Tensor]]: - raise NotImplementedError - - -class NoOpAccumulator(Accumulator): - def __init__(self) -> None: - pass - - def update(self, preds: Tensor, target: Tensor, index: Tensor) -> None: - pass - - def accumulate(self) -> None: - return None - - -class ResultsAccumulator(Accumulator): - """ - Accumulate predictions and targets across processes - """ - - def __init__(self) -> None: - self._local_values: Dict[str, List[Tensor]] = defaultdict(list) - self._gathered_values: Dict[str, Tensor] = {} - self._gathered = False - - def update(self, preds: Tensor, target: Tensor, index: Tensor) -> None: - assert len(preds) == len(target) == len(index) - assert not self._gathered, "Tensors have already been gathered in this helper" - self._local_values["preds"].append(preds) - self._local_values["target"].append(target) - self._local_values["index"].append(index) - self._gathered = False - - def _gather_tensors(self): - for k, tensor_list in self._local_values.items(): - self._gathered_values[k] = _cat_and_gather_tensor_list(tensor_list) - self._gathered = True - - def accumulate(self) -> Dict[str, Tensor]: - if not self._gathered: - self._gather_tensors() - preds, target, index = [self._gathered_values[k] for k in ["preds", "target", "index"]] - assert len(preds) == len(target) == len(index) and index.min() == 0 - preds_ordered = torch.zeros((index.max() + 1, *preds.shape[1:]), dtype=preds.dtype, device=preds.device) - preds_ordered[index] = preds - target_ordered = torch.zeros((index.max() + 1, *target.shape[1:]), dtype=target.dtype, device=target.device) - target_ordered[index] = target - return {"preds": preds_ordered, "target": target_ordered} diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/data.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/data.py deleted file mode 100644 index 6c71628cffcf472faf9ef486cf361d3823b29be6..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/data.py +++ /dev/null @@ -1,256 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import logging -from functools import lru_cache -from typing import Any, Callable, Optional - -import numpy as np -import torch -from torch.utils.data import Subset -from torchvision.datasets.vision import StandardTransform - -from dinov3.eval.utils import extract_features - -logger = logging.getLogger("dinov3") - - -class SubsetEx(Subset): - def _get_actual_index(self, index: int) -> int: - return self.indices[index] - - def get_target(self, index: int) -> Any: - actual_index = self._get_actual_index(index) - return self.dataset.get_target(actual_index) - - @property - def transforms(self): - return self.dataset.transforms - - -def get_target_transform(dataset) -> Optional[Callable]: - if hasattr(dataset, "transforms"): - if isinstance(dataset.transforms, StandardTransform): - return dataset.transforms.target_transform - raise ValueError("Dataset has a non-standard .transforms property") - if hasattr(dataset, "target_transform"): - return dataset.target_transform - return None - - -@lru_cache(maxsize=1) -def get_labels(dataset) -> torch.Tensor: - """ - Get the labels of a classification dataset, as a Tensor, using the `get_targets` method - if it is present or loading the labels one by one with `get_target`, if it exists. - If the dataset has a target transform, iterate over the whole dataset to get the - transformed labels for each element, then stack them as a torch tensor. - """ - logger.info("Getting dataset labels ...") - if hasattr(dataset, "get_targets") or hasattr(dataset, "get_target"): - if hasattr(dataset, "get_targets"): # Returns a np.array - labels = dataset.get_targets() - elif hasattr(dataset, "get_target"): - labels = [dataset.get_target(i) for i in range(len(dataset))] - target_transform = get_target_transform(dataset) - if target_transform is not None: - labels = [target_transform(label) for label in labels] - else: - # Target transform is applied in this case - labels = [dataset[i][1] for i in range(len(dataset))] - return torch.stack([torch.tensor(label, dtype=int) for label in labels]) - - -def get_num_classes(dataset) -> int: - """ - Get the labels of a dataset and compute the number of classes - """ - labels = get_labels(dataset) - if len(labels.shape) > 1: - return int(labels.shape[1]) - return int(labels.max() + 1) - - -def create_class_indices_mapping(labels: torch.Tensor) -> dict[int, torch.Tensor]: - """ - Efficiently creates a mapping between the labels and tensors containing - the indices of all the dataset elements that share this label. - In the case of multiple labels, it is not guaranteed that there - will be exactly the specified percentage of labels. - """ - if len(labels.shape) > 1: # labels are a one-hot encoding - assert len(labels.shape) == 2 - sorted_labels, indices = torch.nonzero(labels.T, as_tuple=True) - else: - sorted_labels, indices = torch.sort(labels, stable=True) - unique_labels, counts = torch.unique_consecutive(sorted_labels, return_counts=True) - mapping = dict(zip(unique_labels.tolist(), torch.split(indices, counts.tolist()))) - return mapping - - -def _shuffle_dataset(dataset: torch.Tensor, seed: int = 0): - """ - Shuffling a dataset by subsetting it with a random permutation of its indices - """ - random_generator = torch.Generator() - random_generator.manual_seed(seed) - random_indices = torch.randperm(len(dataset), generator=random_generator) - return SubsetEx(dataset, random_indices) - - -def _subset_dataset_per_class( - class_indices_mapping: dict[int, torch.Tensor], - n_or_percent_per_class: int | float, - dataset_size: int, - seed: int = 0, - is_percent: bool = False, -) -> torch.Tensor: - """ - Helper function to select a percentage of a dataset, equally distributed across classes, - or to take the same number of elements from each class of the dataset. - Returns a boolean mask tensor being True at indices of selected elements - """ - - random_generator = torch.Generator() - random_generator.manual_seed(seed) - - final_indices_bool = torch.zeros(dataset_size, dtype=bool) - for class_indices in class_indices_mapping.values(): - # Select at least one element - n_for_class = max(int(len(class_indices) * n_or_percent_per_class), 1) if is_percent else n_or_percent_per_class - assert isinstance(n_for_class, int) - filtered_index = torch.randperm(len(class_indices), generator=random_generator)[:n_for_class] - final_indices_bool[class_indices[filtered_index]] = True - return final_indices_bool - - -def _multilabel_rebalance_subset( - class_indices_mapping: dict[int, torch.Tensor], - n_or_percent_per_class: int | float, - labels: torch.Tensor, - indices_bool: torch.Tensor, - dataset_size: int, - seed: int = 0, -) -> torch.Tensor: - """ - Helper function to refine a subset of a multi-label dataset (indices_bool) - to better match a target percentage of labels. - Returns a boolean mask tensor being True at indices of selected elements. - """ - - # Compute the number of selected labels in indices_bool - num_total_labels = labels.sum() - num_wanted_labels = int(num_total_labels * n_or_percent_per_class) - num_selected_labels = (labels[indices_bool] > 0).sum() - logger.info(f" {num_selected_labels} labels instead of {num_wanted_labels}") - - # Compute a new percentage and new set selecting less images, therefore less labels, to match approximatelly the exact percentage of labels selected - n_or_percent_per_class = n_or_percent_per_class / (num_selected_labels / num_wanted_labels) - final_indices_bool = _subset_dataset_per_class( - class_indices_mapping, n_or_percent_per_class, dataset_size, seed, True - ) - - # Compute the number of labels finally used - num_selected_labels = (labels[final_indices_bool] > 0).sum() - logger.info(f" {num_selected_labels} labels instead of {num_wanted_labels}") - - return final_indices_bool - - -def split_train_val_datasets(train_dataset, split_percentage: float = 0.1, shuffle_train: bool = True): - """ - Splitting a percent of the train dataset to choose hyperparameters, taking the same percentage for each class. - If `shuffle` is False, taking the first elements of each class as the validaton set. - """ - assert 0 < split_percentage < 1 - logger.info(f"Selecting {int(split_percentage * 100)}% of the train dataset as the validation set") - if shuffle_train: - logger.info("Shuffling train dataset before splitting in train and validation sets") - train_dataset = _shuffle_dataset(train_dataset) - train_labels = get_labels(train_dataset) - class_indices_mapping = create_class_indices_mapping(train_labels) - val_mask = torch.zeros(len(train_labels), dtype=bool) - for class_indices in class_indices_mapping.values(): - # If there is only one element, it goes in the train set - n_for_val = max(1, int(split_percentage * len(class_indices))) if len(class_indices) > 1 else 0 - val_mask[class_indices[:n_for_val]] = True - - val_dataset = SubsetEx(train_dataset, val_mask.nonzero().flatten()) - train_dataset = SubsetEx(train_dataset, (~val_mask).nonzero().flatten()) - return train_dataset, val_dataset - - -def create_train_dataset_dict( - train_dataset, - few_shot_eval: bool = False, - few_shot_k_or_percent: float | None = None, - few_shot_n_tries: int = 1, -) -> dict[int, dict[int, Any]]: - """ - Randomly split a dataset for few-shot evaluation, with `few_shot_k_or_percent` being - n elements or x% of a class. Produces a dict, which keys are number of random "tries" - and values are the dataset subset for this "try". - - Format is {"nth-try": dataset} - """ - if few_shot_eval is False: - assert few_shot_k_or_percent is None - assert few_shot_n_tries == 1 - return {0: train_dataset} - - assert few_shot_k_or_percent is not None - train_labels = get_labels(train_dataset) - class_indices_mapping = create_class_indices_mapping(train_labels) - train_dataset_dict: dict[int, Any] = {} - is_percent = few_shot_k_or_percent < 1 - if not is_percent: - few_shot_k_or_percent = int(few_shot_k_or_percent) - - for t in range(few_shot_n_tries): - t_subset_bool = _subset_dataset_per_class( - class_indices_mapping=class_indices_mapping, - n_or_percent_per_class=few_shot_k_or_percent, - dataset_size=len(train_labels), - is_percent=is_percent, - seed=t, - ) - if len(train_labels.shape) > 1 and is_percent: - t_subset_bool = _multilabel_rebalance_subset( - class_indices_mapping=class_indices_mapping, - n_or_percent_per_class=few_shot_k_or_percent, - dataset_size=len(train_labels), - labels=train_labels, - indices_bool=t_subset_bool, - seed=t, - ) - train_dataset_dict[t] = SubsetEx(train_dataset, t_subset_bool.nonzero().flatten()) - return train_dataset_dict - - -def extract_features_for_dataset_dict( - model, dataset_dict: dict[int, dict[int, Any]], batch_size: int, num_workers: int, gather_on_cpu=False -) -> dict[int, dict[str, torch.Tensor]]: - """ - Extract features for each subset of dataset in the context of few-shot evaluations - """ - few_shot_data_dict: dict[int, dict[str, torch.Tensor]] = {} - for try_n, dataset in dataset_dict.items(): - features, labels = extract_features(model, dataset, batch_size, num_workers, gather_on_cpu=gather_on_cpu) - few_shot_data_dict[try_n] = {"train_features": features, "train_labels": labels} - return few_shot_data_dict - - -def pad_multilabel_and_collate(batch, pad_value=-1): - """ - This method pads and collates a batch of (image, (index, target)) tuples, coming from - DatasetWithEnumeratedTargets, with targets that are list of potentially varying sizes. - The targets are padded to the length of the longest target list in the batch. - """ - maxlen = max(len(targets) for _, (_, targets) in batch) - padded_batch = [ - (image, (index, np.pad(targets, (0, maxlen - len(targets)), constant_values=pad_value))) - for image, (index, targets) in batch - ] - return torch.utils.data.default_collate(padded_batch) diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/depth/checkpoint_utils.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/depth/checkpoint_utils.py deleted file mode 100644 index 03ec60ef6b1bbaf6b7d836f268e315b70aa25098..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/depth/checkpoint_utils.py +++ /dev/null @@ -1,53 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import logging -import os - -import torch -from torch.optim.optimizer import Optimizer - -logger = logging.getLogger("dinov3") - - -def unwrap_ddp_state_dict(model_state_dict): - is_ddp = all([k.startswith("module.") for k in model_state_dict.keys()]) - if is_ddp: - model_state_dict = {k.split("module.", 1)[-1]: v for (k, v) in model_state_dict.items()} - return model_state_dict - - -def load_checkpoint(checkpoint_path): - checkpoint = torch.load(checkpoint_path, map_location="cpu") - state_dicts = {} - iteration = None - if "iteration" in checkpoint.keys(): - iteration = checkpoint["iteration"] - state_dicts["model"] = unwrap_ddp_state_dict(checkpoint["model"]) - if "optimizer" in checkpoint.keys(): - state_dicts["optimizer"] = checkpoint["optimizer"] - return state_dicts, iteration - - -def find_latest_checkpoint(path): - if not os.path.exists(path): - return None - list_checkpoints = sorted([filepath for filepath in os.listdir(path) if filepath.endswith("pth")]) - if os.path.exists(os.path.join(path, "model_final.pth")): - return os.path.join(path, "model_final.pth") - elif len(list_checkpoints) >= 1: - model_latest_iteration_path = list_checkpoints[-1] - return os.path.join(path, model_latest_iteration_path) - else: - logger.info("Could not find checkpoint to resume from, starting from scratch") - - -def save_checkpoint(path: str, iteration: int, model: torch.nn.Module, optimizer: Optimizer): - chkpt = { - "model": unwrap_ddp_state_dict(model.state_dict()), - "optimizer": optimizer.state_dict(), - "iteration": iteration, - } - torch.save(chkpt, os.path.join(path, f"model_{iteration:08d}.pth")) diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/depth/config.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/depth/config.py deleted file mode 100644 index 59f9f36f8def70ce76aa19d9adf2f4c3702f1aa5..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/depth/config.py +++ /dev/null @@ -1,160 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -from dataclasses import dataclass, field -from enum import Enum -from typing import Any - -from omegaconf import MISSING - -import torch - -from dinov3.eval.setup import ModelConfig - - -from dinov3.eval.depth.loss import LossType -from dinov3.eval.depth.models import DecoderConfig -from dinov3.eval.depth.transforms import make_depth_train_transforms, make_depth_eval_transforms -from dinov3.data.transforms import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD - - -class Dtype(Enum): - FLOAT32 = "float32" - BFLOAT16 = "bfloat16" - - @property - def autocast_dtype(self): - return { - Dtype.BFLOAT16: torch.bfloat16, - Dtype.FLOAT32: torch.float, - }[self] - - -class ResultExtension(Enum): - JPG = "jpg" - PNG = "png" - PTH = "pth" - - -@dataclass -class ResultConfig: - save_results: bool = False - extension: ResultExtension = ResultExtension.JPG - save_resolution: int | None = ( - None # if set, the output result image is resized to have its smallest size set to save_resolution - ) - overlay_alpha: float = 1.0 # if alpha == 1, masks are not overlaid on the original image - save_separate_files: bool = False # set to true to save individual files (image, prediction, gt) - - -@dataclass -class DatasetsConfig: - root: str = MISSING - train: str = "" - val: str = "" - test: str = "" - - -@dataclass -class OptimizerConfig: - lr: float = 1e-4 - beta1: float = 0.9 - beta2: float = 0.999 - weight_decay: float = 0.01 - gradient_clip: float = 35.0 - - -@dataclass -class SchedulerConfig: - type: str = "WarmupOneCycleLR" - total_iter: int = 38_400 # Total number of iterations for training - constructor_kwargs: dict[str, Any] = field(default_factory=dict) - - -@dataclass -class TrainTransformConfig: - img_size: Any = None - random_crop: tuple[int, int] | None = None - brightness_range: tuple[float, float] = (0.9, 1.1) - rotation_angle: float = 2.5 # max rotation angle - fixed_crop: str = "FULL" - eval_mask: str = "FULL" - - -@dataclass -class EvalTransformConfig: - img_size: Any = None - fixed_crop: str = "FULL" - eval_mask: str = "FULL" - - -@dataclass -class TransformConfig: - train: TrainTransformConfig | None = None - eval: EvalTransformConfig = field(default_factory=EvalTransformConfig) - mean: tuple[float, float, float] = IMAGENET_DEFAULT_MEAN - std: tuple[float, float, float] = IMAGENET_DEFAULT_STD - normalization_constant: float = 1000.0 - - -@dataclass -class EvalConfig: - ignored_value: float = 0.0 # If depth pixels have this value in the dataset, they will be ignored - align_least_squares: bool = False # Choose whether to align predictions to ground truth during testing - min_depth: float = 0.001 # Minimum depth to be evaluated - max_depth: float = 10.0 # Maximum depth to be evaluated - use_tta: bool = True # apply test-time augmentation at evaluation time - eval_interval: int = 1600 # number of iterations between evaluations - - -@dataclass -class DepthConfig: - model: ModelConfig | None = None - bs: int = 2 - n_gpus: int = 8 - num_workers: int = 2 - seed: int = 321 - scheduler: SchedulerConfig = field(default_factory=SchedulerConfig) - optimizer: OptimizerConfig = field(default_factory=OptimizerConfig) - datasets: DatasetsConfig = field(default_factory=DatasetsConfig) - decoder_head: DecoderConfig = field(default_factory=DecoderConfig) - model_dtype: Dtype | None = None - losses: dict[LossType, float] | None = None # For example {SIGLOSS: 1.0, GRADIENT_LOG_LOSS: 0.0} - transforms: TransformConfig = field(default_factory=TransformConfig) - eval: EvalConfig = field(default_factory=EvalConfig) - metrics: list[str] = field(default_factory=lambda: ["rmse", "abs_rel", "a1"]) - result_config: ResultConfig = field(default_factory=ResultConfig) - load_from: str | None = None # path to .pt checkpoint to resume training from - output_dir: str = "" - - -def make_depth_train_transforms_from_config(config: DepthConfig): - assert config.datasets.train is not None - assert config.transforms.train is not None - transforms = make_depth_train_transforms( - img_size=config.transforms.train.img_size, - normalization_constant=config.transforms.normalization_constant, - random_crop_size=config.transforms.train.random_crop, - fixed_crop=config.transforms.train.fixed_crop, - brightness_range=config.transforms.train.brightness_range, - rotation_angle=config.transforms.train.rotation_angle, - mean=config.transforms.mean, - std=config.transforms.std, - ) - return transforms - - -def make_depth_eval_transforms_from_config(config: DepthConfig, split: str = "val"): - assert split in ["val", "test"] - transforms = make_depth_eval_transforms( - img_size=config.transforms.eval.img_size, - normalization_constant=config.transforms.normalization_constant, - fixed_crop=config.transforms.eval.fixed_crop, - tta=config.eval.use_tta if split == "test" else False, - mean=config.transforms.mean, - std=config.transforms.std, - ) - - return transforms diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/depth/configs/config-nyu-synthmix-dpt-inference.yaml b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/depth/configs/config-nyu-synthmix-dpt-inference.yaml deleted file mode 100644 index ff5ca8622abfda9c85fc0ce8a4d7544db20cc50f..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/depth/configs/config-nyu-synthmix-dpt-inference.yaml +++ /dev/null @@ -1,12 +0,0 @@ -datasets: - test: 'NYU:split=VAL' -transforms: - eval: - img_size: 768 - fixed_crop: 'FULL' - eval_mask: 'NYU_EIGEN' -eval: - min_depth: 0.001 - max_depth: 10.0 - use_tta: true - align_least_squares: true diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/depth/configs/config-nyu.yaml b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/depth/configs/config-nyu.yaml deleted file mode 100644 index f1a89e8d02a445cc1f8bafef2554a23599b4ab00..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/depth/configs/config-nyu.yaml +++ /dev/null @@ -1,46 +0,0 @@ -bs: 2 -n_gpus: 8 -seed: 321 -scheduler: - total_iter: 38400 - type: 'WarmupOneCycleLR' - constructor_kwargs: - final_div_factor: 1000.0 - warmup_iters: 12800 - base_momentum: 0.85 - max_momentum: 0.95 -optimizer: - lr : 3e-4 - beta1: 0.9 - beta2: 0.99 - weight_decay: 1e-4 - gradient_clip: 35 -datasets: - train: 'NYU:split=TRAIN' - val: 'NYU:split=VAL' - test: 'NYU:split=VAL' -decoder_head: - min_depth: 0.001 - max_depth: 10.0 - use_backbone_norm: True - use_batchnorm: True - backbone_out_layers: LAST - type: linear - use_cls_token: False - n_output_channels: 256 -losses: - SIGLOSS: 1.0 -transforms: - train: - random_crop: [416, 544] - brightness_range: [0.75, 1.25] - fixed_crop: 'NYU' - eval_mask: 'FULL' - eval: - fixed_crop: 'FULL' - eval_mask: 'NYU_EIGEN' -eval: - min_depth: 0.001 - max_depth: 10.0 - use_tta: true - align_least_squares: false diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/depth/data.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/depth/data.py deleted file mode 100644 index 93953635c97c660afc3f3196e19e620ca73c47ef..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/depth/data.py +++ /dev/null @@ -1,115 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import logging -import random -from functools import partial -from typing import Any - -import numpy as np -import torch - -from dinov3.data import make_dataset, make_data_loader, DatasetWithEnumeratedTargets, SamplerType -import dinov3.distributed as distributed - - -logger = logging.getLogger("dinov3") - - -def worker_init_fn(worker_id, num_workers, rank, seed): - """Worker init func for dataloader. - The seed of each worker equals to num_worker * rank + worker_id + user_seed - Args: - worker_id (int): Worker id. - num_workers (int): Number of workers. - rank (int): The rank of current process. - seed (int): The random seed to use. - """ - worker_seed = num_workers * rank + worker_id + seed - np.random.seed(worker_seed) - random.seed(worker_seed) - torch.manual_seed(worker_seed) - - -def build_dataloader( - transforms: Any, - dataset_str: str, - device: int, - split: str = "train", - batch_size: int = 1, - n_gpus: int = 1, - num_workers: int = 2, - seed: int = 0, - use_init_fn=False, -): - """ - Build a dataloader from lavida descriptor strings. - One can specify either a list of descriptors or a single one. - When a list is used, the resulting dataset is - a concatenation of all the listed datasets. - - transforms: transforms for the dataset - dataset_str (str): a dataset descriptor, e.g. 'NYU:split=TRAIN' - device (int): id for the GPU rank - split (str): dataset split (choice: ['train', 'val', 'test']) - batch_size (int): batch size - n_gpus (int): number of ranks to use for distributed sampler - num_workers (int): number of workers for the dataloader - seed (int): random seed - use_init_fn (bool): if True, initializes workers with worker_init_fn - """ - assert split in ["train", "val", "test"] - is_train = split == "train" - ds = make_dataset(dataset_str=dataset_str, transforms=transforms) - logger.info(f"Dataset {split}:\n{ds}") - - if not is_train: - assert batch_size == 1, "Evaluation should only be done at batch size 1!" - ds = DatasetWithEnumeratedTargets(ds, pad_dataset=True, num_replicas=n_gpus) - - if use_init_fn and is_train: - init_fn = partial(worker_init_fn, num_workers=num_workers, rank=device, seed=seed + device) - else: - init_fn = None - dataloader = make_data_loader( - dataset=ds, - batch_size=batch_size, - sampler_type=SamplerType.DISTRIBUTED if distributed.is_enabled() else None, - drop_last=is_train, - shuffle=is_train, - persistent_workers=(not is_train), - worker_init_fn=init_fn, - seed=seed, - num_workers=num_workers, - ) - - if is_train: - return InfiniteDataloader(dataloader) - - return dataloader - - -class InfiniteDataloader: - def __init__(self, dataloader: torch.utils.data.DataLoader): - self.dataloader = dataloader - self.data_iterator = iter(dataloader) - self.sampler = dataloader.sampler - if not hasattr(self.sampler, "epoch"): - self.sampler.epoch = 0 # type: ignore - - def __iter__(self): - return self - - def __len__(self) -> int: - return len(self.dataloader) - - def __next__(self): - try: - data = next(self.data_iterator) - except StopIteration: - self.sampler.epoch += 1 - self.data_iterator = iter(self.dataloader) - data = next(self.data_iterator) - return data diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/depth/datasets/__init__.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/depth/datasets/__init__.py deleted file mode 100644 index ea8cef2afa438924642360f035e7319a1f1ee2b2..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/depth/datasets/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/depth/datasets/datasets_utils.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/depth/datasets/datasets_utils.py deleted file mode 100644 index 0c50884997785dd3bb21e5fe5eeac70c170b5a02..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/depth/datasets/datasets_utils.py +++ /dev/null @@ -1,42 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -from enum import Enum - -import torch - - -class _EvalCropType(Enum): - NYU_EIGEN = "NYU_EIGEN" - FULL = "FULL" - - -def make_valid_mask(input, eval_crop: _EvalCropType = _EvalCropType.FULL, ignored_value: float = 0.0): - """Following Adabins, Do grag_crop or eigen_crop for testing - - Args: - input: input tensor in BxCxHxW format - eval_crop (_EvalCropType): evaluation crop used for evaluation - ignored_value (float): value from input to be ignored during evaluation - """ - B, _, h, w = input.shape - eval_mask = torch.zeros(input.shape, device=input.device) - if eval_crop == _EvalCropType.NYU_EIGEN: - y1, y2, x1, x2 = 45, 471, 41, 601 - orig_h, orig_w = 480, 640 - y1_new = int((y1 / orig_h) * h) - y2_new = int((y2 / orig_h) * h) - x1_new = int((x1 / orig_w) * w) - x2_new = int((x2 / orig_w) * w) - eval_mask[:, :, y1_new:y2_new, x1_new:x2_new] = 1 - else: - eval_mask.fill_(1) - - # make mask from ignored values - ignored_value_mask = torch.ones((B, 1, h, w), device=eval_mask.device) - ignored_value_mask[(input == ignored_value).all(dim=1, keepdims=True)] = 0 - - eval_mask = eval_mask * ignored_value_mask - return eval_mask.bool() diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/depth/eval.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/depth/eval.py deleted file mode 100644 index 11f8e5e7e7ea67510c5d601735913d9a9921836f..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/depth/eval.py +++ /dev/null @@ -1,217 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import logging - -from typing import Any -import torch -import torch.utils -import torch.utils.data - -import dinov3.distributed as distributed -from dinov3.logging import MetricLogger - - -from dinov3.eval.depth.config import ( - DepthConfig, - ResultConfig, - make_depth_eval_transforms_from_config, -) -from dinov3.eval.depth.data import build_dataloader -from dinov3.eval.depth.datasets.datasets_utils import _EvalCropType, make_valid_mask -from dinov3.eval.depth.metrics import calculate_depth_metrics, _DepthMetric, DEPTH_METRICS -from dinov3.eval.depth.transforms import Aug, LeftRightFlipAug -from dinov3.eval.depth.utils import align_depth_least_square -from dinov3.eval.depth.visualization_utils import depth_tensor_to_colorized_pil, save_predictions - - -logger = logging.getLogger("dinov3") - - -def inverse_tta_hook(transforms: Aug): - return lambda module, inputs, outputs: transforms.inverse(outputs) - - -def evaluate_depther_with_config( - config: DepthConfig, - depther: torch.nn.Module, - device: Any, - reduce_results: bool = True, -): - # 1- define dataset - transforms = make_depth_eval_transforms_from_config(config, split="test") - dataloader = build_dataloader( - dataset_str=config.datasets.test + f":root={config.datasets.root}", - transforms=transforms, - device=device, - split="test", - batch_size=1, - n_gpus=distributed.get_world_size(), - ) - metrics = [metric for metric in DEPTH_METRICS if metric.name in config.metrics] - - return evaluate_depther_with_dataloader( - dataloader, - depther, - device=device, - metrics=metrics, - eval_range=(config.eval.min_depth, config.eval.max_depth), - result_config=config.result_config, - ignored_value=config.eval.ignored_value, - eval_mask_type=config.transforms.eval.eval_mask, - save_dir=config.output_dir, - reduce_results=reduce_results, - align_least_squares=config.eval.align_least_squares, - use_tta=config.eval.use_tta, - ) - - -@torch.no_grad() -def evaluate_depther_with_dataloader( - dataloader: torch.utils.data.DataLoader, - depther: torch.nn.Module, - device: Any, - metrics: list[_DepthMetric], - eval_range: tuple[float, float], - result_config: ResultConfig, - save_dir="", - ignored_value: float = 0.0, - eval_mask_type: str = "NYU_EIGEN", - reduce_results: bool = True, - align_least_squares: bool = False, - use_tta: bool = False, -): - """ - Evaluate a dense estimation model with a dataloader - - Inputs: - - dataloader: a torch.utils.data.DataLoader - - depther: depth estimator to evaluate - - device: the (CUDA) device to evaluate on - - metrics: metrics to report during evaluation - - eval_range (float, float): depth evaluation range - - result_config (ResultConfig): contains parameters for results saving - - save_dir (str): saving directory for results (metrics and predictions) - - ignored_value (float): value to ignore from the ground truth - - eval_mask_type (str): evaluation mask. See _EvalCropType Enum for choices - - reduce_results (bool): if True, results are averaged across all samples (default=True) - - align_least_squares (bool): if True, aligns prediction in scale and shift with GT using least squares error minimization - - use_tta (bool): if True, uses left-right flipping test time augmentation (default False). - """ - - metric_names = [metric.name for metric in metrics] - all_metric_values_dict: dict[str, Any] = {metric: [] for metric in metric_names} - all_metric_values_dict["indices"] = [] - final_metric_values_dict = {} - - n_gpus = distributed.get_world_size() - - # build a metric_logger for validation - header = "Validation: " - metric_logger = MetricLogger(delimiter=" ") - all_losses: dict[str, list[float]] = {} - if use_tta: - hook = depther.register_forward_hook(inverse_tta_hook(LeftRightFlipAug(flip=True))) - else: - hook = None - - for batch_img, target in metric_logger.log_every(dataloader, 10, header=header): - index, gt_map = target - # batchify augmentations together - assert batch_img[0].shape[0] == 1 - batch_img = torch.cat(batch_img, dim=0).to(device) - gt_map = torch.cat(gt_map, dim=0).to(device) - - preds = depther(batch_img) - # Skip padded indices AFTER prediction, so that each rank can process the - # Same number of forwards, necessary for a sharded backbone - if index < 0: - continue - - # in case tta inflated the batch - B, C, _, _ = preds.shape - gt_map = gt_map[:B] - - # run post processing on ground truth and prediction - gt_map = torch.where( - torch.logical_or(gt_map >= eval_range[1], gt_map <= eval_range[0]), - ignored_value, - gt_map, - ) - - valid_mask = make_valid_mask( - gt_map, - eval_crop=_EvalCropType(eval_mask_type), - ignored_value=ignored_value, - ) - - # resize -if necessary- prediction to match size of gt - if gt_map.shape[-2:] != preds.shape[-2:]: - preds = torch.nn.functional.interpolate( - input=preds, - size=gt_map.shape[2:], - mode="bilinear", - align_corners=False, - ) - if align_least_squares: - preds = torch.stack([align_depth_least_square(gt_map, p, valid_mask)[0] for p in preds]) - preds = preds.to(device) - - if result_config.save_results: - assert preds.shape[0] == 1, "Cannot save results for more than one decoder" - save_predictions( - img=batch_img[: preds.shape[0]], - pred=preds[0], - gt=gt_map, - save_index=index, - result_config=result_config, - save_dir=save_dir, - pred_tensor_to_pil_fn=depth_tensor_to_colorized_pil, - ) - - preds = preds.clamp(min=eval_range[0], max=eval_range[1]) - - batch_metric_values = calculate_depth_metrics( - gt_map, - preds, - valid_mask, - list_metrics=metrics, - ) # NamedTuple with metrics as names - for metric in metric_names: - value = getattr(batch_metric_values, metric, None) - if value is not None: - all_metric_values_dict[metric].append(value) - all_metric_values_dict["indices"].append(index) - all_indices = torch.tensor(all_metric_values_dict["indices"], device=device) - if n_gpus > 1: - list_all_indices = distributed.gather_all_tensors(all_indices) - all_indices = torch.cat(list_all_indices, dim=0).cpu().to(torch.int32) - - out_results_dict = {} - - all_metric_values = torch.tensor( - [values_per_metric for values_per_metric in all_metric_values_dict.values()], - device=device, - ) - if n_gpus > 1: - all_metric_values = torch.cat(distributed.gather_all_tensors(all_metric_values), dim=1) - - final_metric_values = all_metric_values.nanmean(1) - final_metric_values_dict = dict(zip(metric_names, final_metric_values.cpu().numpy())) - - if reduce_results: - out_results_dict = {k: float(v) for (k, v) in final_metric_values_dict.items()} - else: - out_results_dict = { - metric_name: value for (metric_name, value) in zip(metric_names, all_metric_values.cpu().numpy().tolist()) - } - logger.info( - "Final scores: " + " ".join([f"{name}: {meter:.3f}" for name, meter in final_metric_values_dict.items()]) - ) - - if hook is not None: - hook.remove() - - return out_results_dict, all_losses, all_indices diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/depth/loss.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/depth/loss.py deleted file mode 100644 index 301eb8693307ecade0f832317bf5cc1bf37a2a97..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/depth/loss.py +++ /dev/null @@ -1,164 +0,0 @@ -from enum import Enum -from functools import partial - -import torch -from torch import nn - - -class LossType(Enum): - SIGLOSS = "sigloss" - GRADIENT_LOSS = "gradient_loss" - GRADIENT_LOG_LOSS = "gradient_log_loss" - L1 = "l1" - - def module(self, *args, **kwargs): - return { - LossType.SIGLOSS: partial( - SigLoss, - warm_up=True, - warm_iter=100, - ), # default parameters for the custom loss (BW compatibility) - LossType.GRADIENT_LOG_LOSS: GradientLogLoss, - LossType.GRADIENT_LOSS: GradientLoss, - LossType.L1: L1Loss, - }[self](*args, **kwargs) - - -class GradientLoss(nn.Module): - def __init__(self): - super().__init__() - self.eps = 0.001 - - def forward(self, input, target, valid_mask=None): - input_downscaled = [input] + [input[..., :: 2 * i, :: 2 * i] for i in range(1, 4)] - target_downscaled = [target] + [target[..., :: 2 * i, :: 2 * i] for i in range(1, 4)] - if valid_mask is not None: - mask_downscaled = [valid_mask] + [valid_mask[..., :: 2 * i, :: 2 * i] for i in range(1, 4)] - else: - mask_downscaled = [torch.ones_like(target, dtype=bool) for target in target_downscaled] - - gradient_loss = 0 - for input, target, mask in zip(input_downscaled, target_downscaled, mask_downscaled): - N = torch.sum(mask) - d_diff = torch.mul(input - target, mask) - - v_gradient = torch.abs(d_diff[..., 0:-2, :] - d_diff[..., 2:, :]) - v_mask = torch.mul(mask[..., 0:-2, :], mask[..., 2:, :]) - v_gradient = torch.mul(v_gradient, v_mask) - - h_gradient = torch.abs(d_diff[..., :, 0:-2] - d_diff[..., :, 2:]) - h_mask = torch.mul(mask[..., :, 0:-2], mask[..., :, 2:]) - h_gradient = torch.mul(h_gradient, h_mask) - gradient_loss += (torch.sum(h_gradient) + torch.sum(v_gradient)) / N - - return gradient_loss - - -class GradientLogLoss(nn.Module): - def __init__(self): - super().__init__() - self.eps = 0.001 - - def forward(self, input, target, valid_mask=None): - input_downscaled = [input] + [input[..., :: 2 * i, :: 2 * i] for i in range(1, 4)] - target_downscaled = [target] + [target[..., :: 2 * i, :: 2 * i] for i in range(1, 4)] - if valid_mask is not None: - mask_downscaled = [valid_mask] + [valid_mask[..., :: 2 * i, :: 2 * i] for i in range(1, 4)] - else: - mask_downscaled = [torch.ones_like(target, dtype=bool) for target in target_downscaled] - - gradient_loss = 0 - for input, target, mask in zip(input_downscaled, target_downscaled, mask_downscaled): - N = torch.sum(mask) - input_log = torch.log(input + self.eps) - target_log = torch.log(target + self.eps) - log_d_diff = input_log - target_log - - log_d_diff = torch.mul(log_d_diff, mask) - - v_gradient = torch.abs(log_d_diff[..., 0:-2, :] - log_d_diff[..., 2:, :]) - v_mask = torch.mul(mask[..., 0:-2, :], mask[..., 2:, :]) - v_gradient = torch.mul(v_gradient, v_mask) - - h_gradient = torch.abs(log_d_diff[..., :, 0:-2] - log_d_diff[..., :, 2:]) - h_mask = torch.mul(mask[..., :, 0:-2], mask[..., :, 2:]) - h_gradient = torch.mul(h_gradient, h_mask) - gradient_loss += (torch.sum(h_gradient) + torch.sum(v_gradient)) / N - - return gradient_loss - - -class L1Loss(nn.Module): - def __init__(self): - super().__init__() - - def forward(self, input, target, valid_mask=None): - loss = nn.functional.l1_loss(input, target, reduce=False) - mask = valid_mask if (valid_mask is not None) else torch.ones_like(input, dtype=bool) - loss = loss * mask - return loss.sum() / (mask.sum() + 1e-7) - - -class SigLoss(nn.Module): - """Sigloss - - Adapted from Binsformer who adapted from AdaBins - https://github.com/zhyever/Monocular-Depth-Estimation-Toolbox/blob/7c0c89c8db07631fec1737f3087e4f1f540ccd53/depth/models/losses/sigloss.py#L8 - """ - - def __init__(self, warm_up=True, warm_iter=100): - super(SigLoss, self).__init__() - self.loss_name = "SigLoss" - self.eps = 0.001 # avoid grad explode - self.warm_up = warm_up - self.warm_iter = warm_iter - self.warm_up_counter = 0 - - def sigloss(self, input, target, valid_mask=None): - if valid_mask is None: - valid_mask = torch.ones_like(target, dtype=bool) - input = input[valid_mask] - target = target[valid_mask] - - g = torch.log(input + self.eps) - torch.log(target + self.eps) - Dg = 0.15 * torch.pow(torch.mean(g), 2) - if self.warm_up and self.warm_up_counter < self.warm_iter: - self.warm_up_counter += 1 - else: - Dg += torch.var(g) - if Dg <= 0: - return torch.abs(Dg) - return torch.sqrt(Dg) - - def forward(self, depth_pred, depth_gt, valid_mask=None): - """Forward function.""" - - return self.sigloss(depth_pred, depth_gt, valid_mask) - - -class MultiLoss(nn.Module): - """ - losses adapted from https://www.cs.cornell.edu/projects/megadepth/ - - Args: - dict_losses: (dict[LossType, float, Any]) a dict of losses in the format {LossType_1: Weight_1, ..., LossType_N: Weight_N}. - """ - - def __init__( - self, - dict_losses: dict[LossType, float], - ): - super(MultiLoss, self).__init__() - self.dict_losses = nn.ModuleDict({loss_type.name: loss_type.module() for loss_type in dict_losses.keys()}) - self.dict_weights = {loss_type.name: weight for (loss_type, weight) in dict_losses.items()} - self.eps = 0.001 # avoid grad explode - - def forward(self, depth_pred, depth_gt, valid_mask=None): - """Forward function.""" - - loss_depth = 0 - for loss_name in self.dict_losses.keys(): - weight = self.dict_weights[loss_name] - loss = self.dict_losses[loss_name](depth_pred, depth_gt, valid_mask) - loss_depth += weight * loss - return loss_depth diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/depth/metrics.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/depth/metrics.py deleted file mode 100644 index 1a4104c4b0829af1237e6c4733b77befb86fd1dc..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/depth/metrics.py +++ /dev/null @@ -1,91 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import math -from collections import namedtuple -from dataclasses import dataclass - -import torch - - -@dataclass(frozen=True) -class _DepthMetric: - name: str - is_lower_better: bool - - @property - def worst_value(self) -> float: - return math.inf if self.is_lower_better else -math.inf - - def is_better(self, value1, value2) -> bool: - sign = 1 if self.is_lower_better else -1 - return sign * value1 < sign * value2 - - -DEPTH_METRICS = ( - _DepthMetric(name="a1", is_lower_better=False), - _DepthMetric(name="a2", is_lower_better=False), - _DepthMetric(name="a3", is_lower_better=False), - _DepthMetric(name="abs_rel", is_lower_better=True), - _DepthMetric(name="rmse", is_lower_better=True), - _DepthMetric(name="log_10", is_lower_better=True), - _DepthMetric(name="rmse_log", is_lower_better=True), - _DepthMetric(name="silog", is_lower_better=True), - _DepthMetric(name="sq_rel", is_lower_better=True), - _DepthMetric(name="mae", is_lower_better=True), -) - -DEPTH_METRICS_NAME = [metric.name for metric in DEPTH_METRICS] - -_DepthMetricValues = namedtuple("DepthMetricValues", [metric.name for metric in DEPTH_METRICS]) # type: ignore - - -def calculate_depth_metrics( - gt: torch.Tensor, - pred: torch.Tensor, - valid_mask: torch.Tensor | None = None, - list_metrics: list[_DepthMetric] = list(DEPTH_METRICS), -): - if gt.shape[0] == 0: - return [torch.nan] * len(DEPTH_METRICS) - - if valid_mask is not None: - valid_mask = torch.logical_and(valid_mask, gt > 0) - - gt = gt[valid_mask] - pred = pred[valid_mask] - - metrics_dict = {} - - metric_names = [metric.name for metric in list_metrics] - - thresh = torch.maximum((gt / pred), (pred / gt)) - metrics_dict["a1"] = (thresh < 1.25).float().mean() if "a1" in metric_names else torch.nan - metrics_dict["a2"] = (thresh < 1.25**2).float().mean() if "a2" in metric_names else torch.nan - metrics_dict["a3"] = (thresh < 1.25**3).float().mean() if "a3" in metric_names else torch.nan - - error = gt - pred - sq_error = error**2 - metrics_dict["mae"] = torch.mean(torch.abs(error)) if "mae" in metric_names else torch.nan - metrics_dict["abs_rel"] = torch.mean(torch.abs(error) / gt) if "abs_rel" in metric_names else torch.nan - metrics_dict["sq_rel"] = torch.mean(sq_error / gt) if "sq_rel" in metric_names else torch.nan - - metrics_dict["rmse"] = torch.sqrt(sq_error.mean()) if "rmse" in metric_names else torch.nan - - error_log = torch.log(gt) - torch.log(pred) - sq_error_log = error_log**2 - metrics_dict["rmse_log"] = torch.sqrt(sq_error_log.mean()) if "rmse_log" in metric_names else torch.nan - if "silog" in metric_names: - silog = torch.sqrt(torch.mean(sq_error_log) - torch.mean(error_log) ** 2) * 100 - if torch.isnan(silog): - silog = torch.tensor(0) - metrics_dict["silog"] = silog - else: - metrics_dict["silog"] = torch.nan - metrics_dict["log_10"] = ( - (torch.abs(torch.log10(gt) - torch.log10(pred))).mean() if "log_10" in metric_names else math.inf - ) - - return _DepthMetricValues(**metrics_dict) diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/depth/models/__init__.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/depth/models/__init__.py deleted file mode 100644 index 2a74cb7cd3d6f957117c9173fe38777c9c05cf2f..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/depth/models/__init__.py +++ /dev/null @@ -1,266 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. -from dataclasses import dataclass, field -from functools import partial -from typing import Any - -import torch -from dinov3.eval.depth.checkpoint_utils import load_checkpoint - -from .dpt_head import DPTHead -from .linear_head import LinearHead -from .encoder import BackboneLayersSet, DinoVisionTransformerWrapper, PatchSizeAdaptationStrategy - - -@dataclass -class DecoderConfig: - min_depth: float = 0.001 - max_depth: float = 80 - bins_strategy: str = "linear" # (choice: ["linear", "log"]) distribution of bins across the range - norm_strategy: str = ( - "linear" # (choice: ["linear", "softmax", "sigmoid"]) activation used before normalization of depth-bin logits - ) - head_kwargs: dict[str, Any] = field(default_factory=dict) - backbone_out_layers: Any = ( - BackboneLayersSet.FOUR_EVEN_INTERVALS # One of BackboneLayersSet(Enum) or a list of indices e.g. [0, 1, 2, 3] - ) - adapt_to_patch_size: PatchSizeAdaptationStrategy = PatchSizeAdaptationStrategy.CENTER_PADDING - use_backbone_norm: bool = True - # decoder - type: str = "linear" # choices: linear or dpt - n_output_channels: int = 256 - use_batchnorm: bool = False - use_cls_token: bool = False - - -class FeaturesToDepth(torch.nn.Module): - def __init__( - self, - min_depth=0.001, - max_depth=80, - bins_strategy="linear", - norm_strategy="linear", - ): - """ - Module which converts a feature maps into a depth map - - Args: - min_depth (float): minimum depth, used to calibrate the depth range - max_depth (float): maximum depth, used to calibrate the depth range - bins_strategy (str): Choices are 'linear' or 'log', for Uniform or Scale Invariant distributions for depth bins. - See AdaBins [1] for more details. - norm_strategy (str): Choices are 'linear', 'softmax' or 'sigmoid', for the conversion of features to depth logits - scale_up (bool): If true, and only if regression by classification is not used, the result is multiplied by max_depth - - - Example: - x = depth_model(input_image) # N C H W - - If pure regression (C == 1), depth is obtained by scaling and/or shifting x - - If C > 1, bins are used: - Depth is obtained as a weighted sum of depth bins, where weights are predicted logits. (see AdaBins [1] for more details) - - [1] AdaBins: https://github.com/shariqfarooq123/AdaBins - """ - super().__init__() - self.min_depth = min_depth - self.max_depth = max_depth - assert bins_strategy in ["linear", "log"], "Support bins_strategy: linear, log" - assert norm_strategy in ["linear", "softmax", "sigmoid"], "Support norm_strategy: linear, softmax, sigmoid" - - self.bins_strategy = bins_strategy - self.norm_strategy = norm_strategy - - def forward(self, x): - n_bins = x.shape[1] # N n_bins H W - if n_bins > 1: - if self.bins_strategy == "linear": - bins = torch.linspace(self.min_depth, self.max_depth, n_bins, device=x.device) - elif self.bins_strategy == "log": - bins = torch.linspace( - torch.log(torch.tensor(self.min_depth)), - torch.log(torch.tensor(self.max_depth)), - n_bins, - device=x.device, - ) - bins = torch.exp(bins) - - # following Adabins, default linear - if self.norm_strategy == "linear": - logit = torch.relu(x) - eps = 0.1 - logit = logit + eps - logit = logit / logit.sum(dim=1, keepdim=True) - elif self.norm_strategy == "softmax": - logit = torch.softmax(x, dim=1) - elif self.norm_strategy == "sigmoid": - logit = torch.sigmoid(x) - logit = logit / logit.sum(dim=1, keepdim=True) - - output = torch.einsum("ikmn,k->imn", [logit, bins]).unsqueeze(dim=1) - else: - # standard regression - output = torch.relu(x) + self.min_depth - return output - - -def make_head( - embed_dims: int | list[int], - n_output_channels: int, - use_batchnorm: bool = False, - use_cls_token: bool = False, - head_type: str = "linear", - **kwargs, -) -> torch.nn.Module: - if isinstance(embed_dims, int): - embed_dims = [embed_dims] - decoder: torch.nn.Module - if head_type == "linear": - decoder = LinearHead( - in_channels=embed_dims, - n_output_channels=n_output_channels, - use_batchnorm=use_batchnorm, - use_cls_token=use_cls_token, - ) - elif head_type == "dpt": - decoder = DPTHead( - in_channels=embed_dims, - n_output_channels=n_output_channels, - readout_type="project" if use_cls_token else "ignore", - use_batchnorm=use_batchnorm, - **kwargs, - ) - else: - raise NotImplementedError("only linear and DPT head supported") - return decoder - - -class EncoderDecoder(torch.nn.Module): - def __init__( - self, - encoder: torch.nn.Module, - decoder: torch.nn.Module, - ): - super().__init__() - self.encoder = encoder - self.decoder = decoder - - def forward(self, x): - x = self.encoder(x) - x = self.decoder(x) - return x - - -class Depther(torch.nn.Module): - def __init__( - self, - encoder: torch.nn.Module, - decoder: torch.nn.Module, - min_depth: float, - max_depth: float, - bins_strategy: str = "linear", - norm_strategy: str = "linear", - autocast_dtype: torch.dtype = torch.float32, - ): - super().__init__() - self.encoder = encoder - self.decoder = decoder - - self.features_to_depth = FeaturesToDepth( - min_depth=min_depth, - max_depth=max_depth, - bins_strategy=bins_strategy, - norm_strategy=norm_strategy, - ) - if torch.cuda.is_available(): - self.autocast_ctx = partial(torch.autocast, device_type="cuda", dtype=autocast_dtype, enabled=True) - self.encoder.cuda() - self.decoder.cuda() - else: - self.autocast_ctx = partial(torch.autocast, device_type="cpu", enabled=True) - - def forward(self, x): - with self.autocast_ctx(): - x = self.encoder(x) - x = self.decoder(x) - x = self.features_to_depth(x) - return x - - -def build_depther( - backbone: torch.nn.Module, - backbone_out_layers: tuple[int, ...] | BackboneLayersSet, - n_output_channels: int, - use_backbone_norm: bool = False, - use_batchnorm: bool = False, - use_cls_token: bool = False, - adapt_to_patch_size: PatchSizeAdaptationStrategy = PatchSizeAdaptationStrategy.CENTER_PADDING, - head_type: str = "dpt", - autocast_dtype: torch.dtype = torch.float32, - # depth args - min_depth: float = 0.001, - max_depth: float = 10.0, - bins_strategy: str = "linear", - norm_strategy: str = "linear", - **kwargs, -): - encoder = DinoVisionTransformerWrapper( - backbone_model=backbone, - backbone_out_layers=backbone_out_layers, - use_backbone_norm=use_backbone_norm, - adapt_to_patch_size=adapt_to_patch_size, - ) - - decoder = make_head( - encoder.embed_dims, - n_output_channels=n_output_channels, - use_batchnorm=use_batchnorm, - use_cls_token=use_cls_token, - head_type=head_type, - **kwargs, - ) - - depther = Depther( - encoder=encoder, - decoder=decoder, - min_depth=min_depth, - max_depth=max_depth, - bins_strategy=bins_strategy, - norm_strategy=norm_strategy, - autocast_dtype=autocast_dtype, - ) - depther.eval() - return depther - - -def make_depther_from_config( - backbone, - config: DecoderConfig, - checkpoint_path: str | None = None, - autocast_dtype: torch.dtype = torch.float32, -) -> Depther: - depther = build_depther( - backbone, - backbone_out_layers=config.backbone_out_layers, - n_output_channels=config.n_output_channels, - use_backbone_norm=config.use_backbone_norm, - use_batchnorm=config.use_batchnorm, - use_cls_token=config.use_cls_token, - adapt_to_patch_size=config.adapt_to_patch_size, - head_type=config.type, - autocast_dtype=autocast_dtype, - min_depth=config.min_depth, - max_depth=config.max_depth, - bins_strategy=config.bins_strategy, - norm_strategy=config.norm_strategy, - **config.head_kwargs, - ) - - # resume from checkpoint - if checkpoint_path is not None: - state_dicts, _ = load_checkpoint(checkpoint_path) - # load head state dict, the backbone has its pretrained_weights - depther.decoder.load_state_dict(state_dicts["model"], strict=True) - - return depther diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/depth/models/__pycache__/__init__.cpython-310.pyc b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/depth/models/__pycache__/__init__.cpython-310.pyc deleted file mode 100644 index 5743c6b426cad00220596af246dd73234af0790d..0000000000000000000000000000000000000000 Binary files a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/depth/models/__pycache__/__init__.cpython-310.pyc and /dev/null differ diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/depth/models/__pycache__/dpt_head.cpython-310.pyc b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/depth/models/__pycache__/dpt_head.cpython-310.pyc deleted file mode 100644 index b1d9b8cef53798cd586543ed15f7937b993f74f0..0000000000000000000000000000000000000000 Binary files a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/depth/models/__pycache__/dpt_head.cpython-310.pyc and /dev/null differ diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/depth/models/__pycache__/embed.cpython-310.pyc b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/depth/models/__pycache__/embed.cpython-310.pyc deleted file mode 100644 index 018b70931da81e211fcf487a0384398ef3fafa51..0000000000000000000000000000000000000000 Binary files a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/depth/models/__pycache__/embed.cpython-310.pyc and /dev/null differ diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/depth/models/__pycache__/encoder.cpython-310.pyc b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/depth/models/__pycache__/encoder.cpython-310.pyc deleted file mode 100644 index 4ea335627d6d24f5e003a744a384ffba120ac2b8..0000000000000000000000000000000000000000 Binary files a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/depth/models/__pycache__/encoder.cpython-310.pyc and /dev/null differ diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/depth/models/__pycache__/linear_head.cpython-310.pyc b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/depth/models/__pycache__/linear_head.cpython-310.pyc deleted file mode 100644 index 7ec68f6401b6a7253805b626644a73f14cf524db..0000000000000000000000000000000000000000 Binary files a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/depth/models/__pycache__/linear_head.cpython-310.pyc and /dev/null differ diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/depth/models/dpt_head.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/depth/models/dpt_head.py deleted file mode 100644 index 9b6be42db6e7937b94b51aa5050c0e333a8a5dbd..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/depth/models/dpt_head.py +++ /dev/null @@ -1,532 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import torch -from torch import nn - - -def kaiming_init( - module: nn.Module, - a: float = 0, - mode: str = "fan_out", - nonlinearity: str = "relu", - bias: float = 0, - distribution: str = "normal", -) -> None: - assert distribution in ["uniform", "normal"] - if hasattr(module, "weight") and module.weight is not None: - if distribution == "uniform": - nn.init.kaiming_uniform_(module.weight, a=a, mode=mode, nonlinearity=nonlinearity) - else: - nn.init.kaiming_normal_(module.weight, a=a, mode=mode, nonlinearity=nonlinearity) - if hasattr(module, "bias") and module.bias is not None: - nn.init.constant_(module.bias, bias) - - -def constant_init(module, val, bias=0): - if hasattr(module, "weight") and module.weight is not None: - nn.init.constant_(module.weight, val) - if hasattr(module, "bias") and module.bias is not None: - nn.init.constant_(module.bias, bias) - - -class ConvModule(nn.Module): - """A conv block that bundles conv/norm/activation layers. - This block simplifies the usage of convolution layers, which are commonly - used with a norm layer (e.g., BatchNorm) and activation layer (e.g., ReLU). - It is based upon three build methods: `build_conv_layer()`, - `build_norm_layer()` and `build_activation_layer()`. - Besides, we add some additional features in this module. - 1. Automatically set `bias` of the conv layer. - 2. Spectral norm is supported. - 3. More padding modes are supported. Before PyTorch 1.5, nn.Conv2d only - supports zero and circular padding, and we add "reflect" padding mode. - Args: - in_channels (int): Number of channels in the input feature map. - Same as that in ``nn._ConvNd``. - out_channels (int): Number of channels produced by the convolution. - Same as that in ``nn._ConvNd``. - kernel_size (int | tuple[int]): Size of the convolving kernel. - Same as that in ``nn._ConvNd``. - stride (int | tuple[int]): Stride of the convolution. - Same as that in ``nn._ConvNd``. - padding (int | tuple[int]): Zero-padding added to both sides of - the input. Same as that in ``nn._ConvNd``. - dilation (int | tuple[int]): Spacing between kernel elements. - Same as that in ``nn._ConvNd``. - groups (int): Number of blocked connections from input channels to - output channels. Same as that in ``nn._ConvNd``. - bias (bool | str): If specified as `auto`, it will be decided by the - norm_cfg. Bias will be set as True if `norm_cfg` is None, otherwise - False. Default: "auto". - conv_cfg (dict): Config dict for convolution layer. Default: None, - which means using conv2d. - norm_cfg (dict): Config dict for normalization layer. Default: None. - act_cfg (dict): Config dict for activation layer. - Default: dict(type='ReLU'). - inplace (bool): Whether to use inplace mode for activation. - Default: True. - with_spectral_norm (bool): Whether use spectral norm in conv module. - Default: False. - padding_mode (str): If the `padding_mode` has not been supported by - current `Conv2d` in PyTorch, we will use our own padding layer - instead. Currently, we support ['zeros', 'circular'] with official - implementation and ['reflect'] with our own implementation. - Default: 'zeros'. - order (tuple[str]): The order of conv/norm/activation layers. It is a - sequence of "conv", "norm" and "act". Common examples are - ("conv", "norm", "act") and ("act", "conv", "norm"). - Default: ('conv', 'norm', 'act'). - """ - - _abbr_ = "conv_block" - - def __init__( - self, - in_channels, - out_channels, - kernel_size, - stride=1, - padding=0, - dilation=1, - groups=1, - bias="auto", - conv_cfg=None, - norm_cfg=None, - act_cfg=dict(type="ReLU"), - inplace=True, - with_spectral_norm=False, - padding_mode="zeros", - order=("conv", "norm", "act"), - ): - super().__init__() - assert conv_cfg is None or isinstance(conv_cfg, dict) - assert norm_cfg is None or isinstance(norm_cfg, dict) - assert act_cfg is None or isinstance(act_cfg, dict) - official_padding_mode = ["zeros", "circular"] - self.conv_cfg = conv_cfg - self.norm_cfg = norm_cfg - self.act_cfg = act_cfg - self.inplace = inplace - self.with_spectral_norm = with_spectral_norm - self.with_explicit_padding = padding_mode not in official_padding_mode - self.order = order - assert isinstance(self.order, tuple) and len(self.order) == 3 - assert set(order) == {"conv", "norm", "act"} - - self.with_norm = norm_cfg is not None - self.with_activation = act_cfg is not None - # if the conv layer is before a norm layer, bias is unnecessary. - if bias == "auto": - bias = not self.with_norm - self.with_bias = bias - - # if self.with_explicit_padding: - # pad_cfg = dict(type=padding_mode) - # self.padding_layer = build_padding_layer(pad_cfg, padding) - # to do Camille put back - - # reset padding to 0 for conv module - conv_padding = 0 if self.with_explicit_padding else padding - # build convolution layer - self.conv = nn.Conv2d( # build_conv_layer(#conv_cfg, - in_channels, - out_channels, - kernel_size, - stride=stride, - padding=conv_padding, - dilation=dilation, - groups=groups, - bias=bias, - ) - # export the attributes of self.conv to a higher level for convenience - self.in_channels = self.conv.in_channels - self.out_channels = self.conv.out_channels - self.kernel_size = self.conv.kernel_size - self.stride = self.conv.stride - self.padding = padding - self.dilation = self.conv.dilation - self.transposed = self.conv.transposed - self.output_padding = self.conv.output_padding - self.groups = self.conv.groups - - if self.with_spectral_norm: - self.conv = nn.utils.spectral_norm(self.conv) - - # # build normalization layers - if self.with_norm: - # norm layer is after conv layer - if order.index("norm") > order.index("conv"): - norm_channels = out_channels - else: - norm_channels = in_channels - # self.norm_name, norm = build_norm_layer( - # norm_cfg, norm_channels) # type: ignore - self.add_module("bn", torch.nn.SyncBatchNorm(norm_channels)) - # if self.with_bias: - # if isinstance(norm, (_BatchNorm, _InstanceNorm)): - # warnings.warn( - # 'Unnecessary conv bias before batch/instance norm') - self.norm_name = "bn" - else: - self.norm_name = None # type: ignore - - # build activation layer - if self.with_activation: - act_cfg_ = act_cfg.copy() # type: ignore - # nn.Tanh has no 'inplace' argument - if act_cfg_["type"] not in ["Tanh", "PReLU", "Sigmoid", "HSigmoid", "Swish", "GELU"]: - act_cfg_.setdefault("inplace", inplace) - self.activate = nn.ReLU() # build_activation_layer(act_cfg_) - - # Use msra init by default - self.init_weights() - - @property - def norm(self): - if self.norm_name: - return getattr(self, self.norm_name) - else: - return None - - def init_weights(self): - # 1. It is mainly for customized conv layers with their own - # initialization manners by calling their own ``init_weights()``, - # and we do not want ConvModule to override the initialization. - # 2. For customized conv layers without their own initialization - # manners (that is, they don't have their own ``init_weights()``) - # and PyTorch's conv layers, they will be initialized by - # this method with default ``kaiming_init``. - # Note: For PyTorch's conv layers, they will be overwritten by our - # initialization implementation using default ``kaiming_init``. - if not hasattr(self.conv, "init_weights"): - if self.with_activation and self.act_cfg["type"] == "LeakyReLU": - nonlinearity = "leaky_relu" - a = self.act_cfg.get("negative_slope", 0.01) - else: - nonlinearity = "relu" - a = 0 - kaiming_init(self.conv, a=a, nonlinearity=nonlinearity) - if self.with_norm: - constant_init(self.norm_name, 1, bias=0) - - def forward(self, x: torch.Tensor, activate: bool = True, norm: bool = True, debug: bool = False) -> torch.Tensor: - for layer in self.order: - if debug: - breakpoint() - if layer == "conv": - if self.with_explicit_padding: - x = self.padding_layer(x) - x = self.conv(x) - elif layer == "norm" and norm and self.with_norm: - x = self.norm(x) - elif layer == "act" and activate and self.with_activation: - x = self.activate(x) - return x - - -class Interpolate(nn.Module): - def __init__(self, scale_factor, mode, align_corners=False): - super(Interpolate, self).__init__() - self.interp = nn.functional.interpolate - self.scale_factor = scale_factor - self.mode = mode - self.align_corners = align_corners - - def forward(self, x): - x = self.interp(x, scale_factor=self.scale_factor, mode=self.mode, align_corners=self.align_corners) - return x - - -class UpConvHead(nn.Module): - """ - A 3 layer Convolutional head with intermediate upsampling - - Args: - - features (int): number of input channels - - n_output_channels (int, default=256): number of output channels - - n_hidden_channels (int, default=32): number of channels in hidden layer - - The operations are - [ - Conv3x3(features, features // 2), - 2x-Upsampling, - Conv3x3(features // 2, hidden_channels), - ReLU, - Conv1x1(hidden_channels, n_output_channels), - ] - """ - - def __init__(self, features, n_output_channels, n_hidden_channels=32): - super(UpConvHead, self).__init__() - self.n_output_channels = n_output_channels - self.head = nn.Sequential( - nn.Conv2d(features, features // 2, kernel_size=3, stride=1, padding=1), - Interpolate(scale_factor=2, mode="bilinear", align_corners=True), - nn.Conv2d(features // 2, n_hidden_channels, kernel_size=3, stride=1, padding=1), - nn.ReLU(), - nn.Conv2d(n_hidden_channels, n_output_channels, kernel_size=1, stride=1, padding=0), - ) - - def forward(self, x): - x = self.head(x) - return x - - -class ReassembleBlocks(nn.Module): - """ViTPostProcessBlock, process cls_token in ViT backbone output and - rearrange the feature vector to feature map. - Args: - in_channels (List): ViT feature channels. - Default: [1024, 1024, 1024, 1024]. - out_channels (List): output channels of each stage. - Default: [128, 256, 512, 1024]. - readout_type (str): Type of readout operation. Default: 'ignore'. - init_cfg (dict, optional): Initialization config dict. Default: None. - """ - - def __init__( - self, - in_channels=[1024, 1024, 1024, 1024], - out_channels=[128, 256, 512, 1024], - readout_type="project", - use_batchnorm=False, - ): - super(ReassembleBlocks, self).__init__() - - assert readout_type in ["ignore", "add", "project"] - self.readout_type = readout_type - - self.projects = nn.ModuleList( - [ - ConvModule( - in_channels=in_channels[channel_index], - out_channels=out_channel, - kernel_size=1, - act_cfg=None, - ) - for channel_index, out_channel in enumerate(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 self.readout_type == "project": - self.readout_projects = nn.ModuleList() - for i in range(len(self.projects)): - self.readout_projects.append(nn.Sequential(nn.Linear(2 * in_channels[i], in_channels[i]), nn.GELU())) - - self.batchnorm_layers = nn.ModuleList( - [nn.SyncBatchNorm(channel) if use_batchnorm else nn.Identity(channel) for channel in in_channels] - ) - - def forward(self, inputs): - assert isinstance(inputs, list) - out = [] - for i, x in enumerate(inputs): - assert len(x) == 2 - x, cls_token = x[0], x[1] - feature_shape = x.shape - if self.readout_type == "project": - x = x.flatten(2).permute((0, 2, 1)) - readout = cls_token.unsqueeze(1).expand_as(x) - x = self.readout_projects[i](torch.cat((x, readout), -1)) - x = x.permute(0, 2, 1).reshape(feature_shape) - elif self.readout_type == "add": - x = x.flatten(2) + cls_token.unsqueeze(-1) - x = x.reshape(feature_shape) - else: - pass - x = self.batchnorm_layers[i](x) - x = self.projects[i](x) - x = self.resize_layers[i](x) - out.append(x) - return out - - -class PreActResidualConvUnit(nn.Module): - """ResidualConvUnit, pre-activate residual unit. - Args: - in_channels (int): number of channels in the input feature map. - act_cfg (dict): dictionary to construct and config activation layer. - norm_cfg (dict): dictionary to construct and config norm layer. - stride (int): stride of the first block. Default: 1 - dilation (int): dilation rate for convs layers. Default: 1. - init_cfg (dict, optional): Initialization config dict. Default: None. - """ - - def __init__(self, in_channels, act_cfg, norm_cfg, stride=1, dilation=1, init_cfg=None): - super(PreActResidualConvUnit, self).__init__() # init_cfg) - self.conv1 = ConvModule( - in_channels, - in_channels, - 3, - stride=stride, - padding=dilation, - dilation=dilation, - norm_cfg=norm_cfg, - act_cfg=act_cfg, - bias=False, - order=("act", "conv", "norm"), - ) - self.conv2 = ConvModule( - in_channels, - in_channels, - 3, - padding=1, - norm_cfg=norm_cfg, - act_cfg=act_cfg, - bias=False, - order=("act", "conv", "norm"), - ) - - def forward(self, inputs): - inputs_ = inputs.clone() - x = self.conv1(inputs) - x = self.conv2(x) - return x + inputs_ - - -class FeatureFusionBlock(nn.Module): - """FeatureFusionBlock, merge feature map from different stages. - Args: - in_channels (int): Input channels. - act_cfg (dict): The activation config for ResidualConvUnit. - norm_cfg (dict): Config dict for normalization layer. - expand (bool): Whether expand the channels in post process block. - Default: False. - align_corners (bool): align_corner setting for bilinear upsample. - Default: True. - init_cfg (dict, optional): Initialization config dict. Default: None. - """ - - def __init__(self, in_channels, act_cfg, norm_cfg, expand=False, align_corners=True, init_cfg=None): - super(FeatureFusionBlock, self).__init__() # init_cfg) - self.in_channels = in_channels - self.expand = expand - self.align_corners = align_corners - self.out_channels = in_channels - if self.expand: - self.out_channels = in_channels // 2 - self.project = ConvModule(self.in_channels, self.out_channels, kernel_size=1, act_cfg=None, bias=True) - self.res_conv_unit1 = PreActResidualConvUnit(in_channels=self.in_channels, act_cfg=act_cfg, norm_cfg=norm_cfg) - self.res_conv_unit2 = PreActResidualConvUnit(in_channels=self.in_channels, act_cfg=act_cfg, norm_cfg=norm_cfg) - - def forward(self, *inputs): - x = inputs[0] - - if len(inputs) == 2: - if x.shape != inputs[1].shape: - res = torch.nn.functional.interpolate( - inputs[1], - size=(x.shape[2], x.shape[3]), - mode="bilinear", - align_corners=False, - ) - else: - res = inputs[1] - x = x + self.res_conv_unit1(res) - x = self.res_conv_unit2(x) # ok - - x = torch.nn.functional.interpolate(x, scale_factor=2, mode="bilinear", align_corners=self.align_corners) - # ok - - x = self.project(x) # ok - return x - - -class DPTHead(nn.Module): - """Vision Transformers for Dense Prediction. - This head is implemented of `DPT `_. - Args: - in_channels (List): The input dimensions of the ViT backbone. - Default: [1024, 1024, 1024, 1024]. - channels (int): Channels after modules, before the task-specific module - (`conv_depth`). Default: 256. - post_process_channels (List): Out channels of post process conv - layers. Default: [96, 192, 384, 768]. - readout_type (str): Type of readout operation. Default: 'ignore'. - expand_channels (bool): Whether expand the channels in post process - block. Default: False. - """ - - def __init__( - self, - in_channels=(1024, 1024, 1024, 1024), - channels=256, - post_process_channels=[128, 256, 512, 1024], - readout_type="project", - expand_channels=False, - n_output_channels=256, - use_batchnorm=False, # TODO - **kwargs, - ): - super(DPTHead, self).__init__(**kwargs) - self.channels = channels - self.n_output_channels = n_output_channels - self.in_channels = in_channels - self.expand_channels = expand_channels - self.norm_cfg = None # TODO CHECK THIS - self.reassemble_blocks = ReassembleBlocks( - in_channels=in_channels, - out_channels=post_process_channels, - readout_type=readout_type, - use_batchnorm=use_batchnorm, - ) - - self.post_process_channels = [ - channel * (2**i) if expand_channels else channel for i, channel in enumerate(post_process_channels) - ] - self.convs = nn.ModuleList() - for channel in self.post_process_channels: - self.convs.append(ConvModule(channel, self.channels, kernel_size=3, padding=1, act_cfg=None, bias=False)) - self.fusion_blocks = nn.ModuleList() - self.act_cfg = {"type": "ReLU"} - for _ in range(len(self.convs)): - self.fusion_blocks.append(FeatureFusionBlock(self.channels, self.act_cfg, self.norm_cfg)) - self.fusion_blocks[0].res_conv_unit1 = None - self.project = ConvModule(self.channels, self.channels, kernel_size=3, padding=1, norm_cfg=self.norm_cfg) - self.num_fusion_blocks = len(self.fusion_blocks) - self.num_reassemble_blocks = len(self.reassemble_blocks.resize_layers) - self.num_post_process_channels = len(self.post_process_channels) - assert self.num_fusion_blocks == self.num_reassemble_blocks - assert self.num_reassemble_blocks == self.num_post_process_channels - self.conv_depth = UpConvHead(self.channels, self.n_output_channels) - - def forward_features(self, inputs): - assert len(inputs) == self.num_reassemble_blocks, ( - f"Expected {self.num_reassemble_blocks} inputs, got {len(inputs)}." - ) - x = [inp for inp in inputs] - - x = self.reassemble_blocks(x) - x = [self.convs[i](feature) for i, feature in enumerate(x)] - out = self.fusion_blocks[0](x[-1]) - - for i in range(1, len(self.fusion_blocks)): - out = self.fusion_blocks[i](out, x[-(i + 1)]) - - out = self.project(out) - return out - - def forward(self, inputs): - out = self.forward_features(inputs) - return self.conv_depth(out) - - def predict(self, inputs, rescale_to=(512, 512)): - out = self.forward_features(inputs) - return self.conv_depth.predict(out, rescale_to=rescale_to) diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/depth/models/embed.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/depth/models/embed.py deleted file mode 100644 index 7bb5d275cd0ef20cec8c3d23c84e0e1dd07f466d..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/depth/models/embed.py +++ /dev/null @@ -1,52 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import itertools -import math - -import torch - - -class CenterPadding(torch.nn.Module): - def __init__(self, multiple: int): - super().__init__() - self.multiple = multiple - - def _get_pad(self, size): - new_size = math.ceil(size / self.multiple) * self.multiple - pad_size = new_size - size - pad_size_left = pad_size // 2 - pad_size_right = pad_size - pad_size_left - return pad_size_left, pad_size_right - - # @torch.inference_mode() - def forward(self, x): - # expected shapes are ... x H x W, usually B x C x H x W - pads = list(itertools.chain.from_iterable(self._get_pad(m) for m in x.shape[:-3:-1])) - output = torch.nn.functional.pad(x, pads) - return output - - def __extra_repr__(self) -> str: - return f"multiple={self.multiple}" - - -class StretchToMultiple(torch.nn.Module): - def __init__(self, multiple: int): - super().__init__() - self.multiple = multiple - - def forward(self, x): - # expected shapes are ... x H x W, usually B x C x H x W - *shape, C, H, W = x.shape - new_H = math.ceil(H / self.multiple) * self.multiple - new_W = math.ceil(W / self.multiple) * self.multiple - if new_H != H or new_W != W: - x = x.reshape(-1, C, H, W) - x = torch.nn.functional.interpolate(x, size=(new_H, new_W), mode="bilinear") - x = x.reshape(*shape, C, new_H, new_W) - return x - - def __extra_repr__(self) -> str: - return f"multiple={self.multiple}" diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/depth/models/encoder.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/depth/models/encoder.py deleted file mode 100644 index a09cd9ac0e0c07ae17e834f9b01560b026913667..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/depth/models/encoder.py +++ /dev/null @@ -1,117 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import logging -from enum import Enum - -from dinov3.eval.depth.models.embed import CenterPadding, StretchToMultiple -from torch import Tensor, nn - -logger = logging.getLogger("dinov3") - - -class BackboneLayersSet(Enum): - # Set of intermediate layers to take from the backbone - LAST = "LAST" # extracting only the last layer - FOUR_LAST = "FOUR_LAST" # extracting the last 4 layers - FOUR_EVEN_INTERVALS = "FOUR_EVEN_INTERVALS" # extracting outputs every 1/4 of the total number of blocks - - -def _get_backbone_out_indices( - model: nn.Module, - backbone_out_layers: list[int] | tuple[int, ...] | BackboneLayersSet = BackboneLayersSet.FOUR_EVEN_INTERVALS, -): - """ - Get indices for output layers of the ViT backbone. For now there are 3 options available: - BackboneLayersSet.LAST : only extract the last layer, used in segmentation tasks with a bn head. - BackboneLayersSet.FOUR_LAST : extract the last 4 layers, used in segmentation (multiscale setting) - BackboneLayersSet.FOUR_EVEN_INTERVALS : extract outputs every 1/4 of the total number of blocks - Reference outputs in 'FOUR_EVEN_INTERVALS' mode : - ViT/S (12 blocks): [2, 5, 8, 11] - ViT/B (12 blocks): [2, 5, 8, 11] - ViT/L (24 blocks): [5, 11, 17, 23] (correct), [4, 11, 17, 23] (incorrect) - ViT/g (40 blocks): [9, 19, 29, 39] - """ - n_blocks = getattr(model, "n_blocks", 1) - out_indices: list[int] - if isinstance(backbone_out_layers, (tuple, list)): - out_indices = list(backbone_out_layers) - elif backbone_out_layers == BackboneLayersSet.LAST: - out_indices = [n_blocks - 1] - elif backbone_out_layers == BackboneLayersSet.FOUR_LAST: - out_indices = [i for i in range(n_blocks - 4, n_blocks)] - elif backbone_out_layers == BackboneLayersSet.FOUR_EVEN_INTERVALS: - # XXX: Force (incorrect) out indices for backward-compatibility (ViT/L only) - if n_blocks == 24: - out_indices = [4, 11, 17, 23] - else: - out_indices = [i * (n_blocks // 4) - 1 for i in range(1, 5)] - assert all([out_index < n_blocks for out_index in out_indices]) - return out_indices - - -class PatchSizeAdaptationStrategy(Enum): - CENTER_PADDING = "center_padding" - STRETCH = "stretch" - NO_ADAPTATION = "never" - - -class DinoVisionTransformerWrapper(nn.Module): - """Vision Transformer.""" - - def __init__( - self, - backbone_model: nn.Module, - backbone_out_layers: str | tuple[int, ...] | BackboneLayersSet, - use_backbone_norm: bool = False, - adapt_to_patch_size: PatchSizeAdaptationStrategy = PatchSizeAdaptationStrategy.CENTER_PADDING, - ): - super().__init__() - - self.final_norm = use_backbone_norm - self.backbone = backbone_model - if isinstance(backbone_out_layers, str): - backbone_out_layers = BackboneLayersSet(backbone_out_layers) - self.backbone_out_indices = _get_backbone_out_indices(self.backbone, backbone_out_layers=backbone_out_layers) - - # If the backbone does not define embed_dims, use [embed_dim] * n_blocks - try: - embed_dims: list[int] = getattr(self.backbone, "embed_dims") - except AttributeError: - embed_dim: int = getattr(self.backbone, "embed_dim") - n_blocks: int = getattr(self.backbone, "n_blocks") - logger.warning(f"Backbone does not define embed_dims, using {[embed_dim] * n_blocks} instead") - embed_dims = [embed_dim] * n_blocks - self.embed_dims = [embed_dims[idx] for idx in self.backbone_out_indices] - - # How to adapt input images to the patch size of the model? - try: - input_pad_size = getattr(self.backbone, "input_pad_size") - except AttributeError: - patch_size = getattr(self.backbone, "patch_size") - logger.warning(f"Backbone does not define input_pad_size, using {patch_size=} instead") - input_pad_size = patch_size - self.patch_size_adapter: nn.Module = nn.Identity() - if adapt_to_patch_size is PatchSizeAdaptationStrategy.CENTER_PADDING: - self.patch_size_adapter = CenterPadding(input_pad_size) - elif adapt_to_patch_size is PatchSizeAdaptationStrategy.STRETCH: - self.patch_size_adapter = StretchToMultiple(input_pad_size) - - # Freeze backbone - self.backbone.requires_grad_(False) - - def forward( - self, - x: Tensor, # [B, rgb, H, W] - ) -> list[tuple[Tensor, Tensor]]: - x = self.patch_size_adapter(x) - outputs = self.backbone.get_intermediate_layers( # type: ignore - x, - n=self.backbone_out_indices, - reshape=True, - return_class_token=True, - norm=self.final_norm, - ) # List of (patch feats [B, C, h, w], class token [B, C]) - return outputs diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/depth/models/linear_head.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/depth/models/linear_head.py deleted file mode 100644 index 76b592392a0e134b277fbe7d0cef68f2ba035355..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/depth/models/linear_head.py +++ /dev/null @@ -1,98 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import torch -import torch.nn as nn -import torch.nn.functional as F - - -class LinearHead(nn.Module): - """Linear layer .""" - - def __init__( - self, - in_channels, - n_output_channels, - input_transform="resize", - align_corners=False, - use_batchnorm=False, - use_cls_token=True, - ): - super().__init__() - self.in_channels = in_channels - self.channels = sum(in_channels) - if use_cls_token: - self.channels *= 2 # concatenate CLS to patch tokens - self.input_transform = input_transform - self.align_corners = align_corners - self.n_output_channels = n_output_channels - self.use_cls_token = use_cls_token - - # batchnorm - self.batchnorm_layer = nn.SyncBatchNorm(self.channels) if use_batchnorm else nn.Identity(self.channels) - - # linear head - self.conv_depth = nn.Conv2d(self.channels, self.n_output_channels, kernel_size=1, padding=0, stride=1) - nn.init.normal_(self.conv_depth.weight, mean=0, std=0.01) - nn.init.constant_(self.conv_depth.bias, 0) - - def _transform_inputs(self, inputs): - """Transform inputs for decoder. - Args: - inputs (list[Tensor]): List of multi-level img features. - Returns: - Tensor: The transformed inputs - """ - if "resize" in self.input_transform: - inputs = [ - torch.nn.functional.interpolate( - input=x, - size=[s for s in inputs[0].shape[2:]], - mode="bilinear", - align_corners=self.align_corners, - ) - for x in inputs - ] - inputs = torch.cat(inputs, dim=1) - return inputs - - def _forward_feature(self, inputs): - """Forward function for feature maps before classifying each pixel with - ``self.cls_seg`` fc. - Args: - inputs (list[Tensor]): List of multi-level img features. - Returns: - feats (Tensor): A tensor of shape (batch_size, self.channels, - H, W) which is feature map for last layer of decoder head. - """ - # accept lists (for cls token) - inputs = list(inputs) - for i, x in enumerate(inputs): - if self.use_cls_token: - assert len(x) == 2, "Missing class tokens" - x, cls_token = x[0], x[1] - if len(x.shape) == 2: - x = x[:, :, None, None] - cls_token = cls_token[:, :, None, None].expand_as(x) - inputs[i] = torch.cat((x, cls_token), 1) - else: - x = x[0] - if len(x.shape) == 2: - x = x[:, :, None, None] - inputs[i] = x - x = self._transform_inputs(inputs) - return x - - def forward(self, inputs): - """Forward function.""" - output = self._forward_feature(inputs) - output = self.batchnorm_layer(output) - output = self.conv_depth(output) - return output - - def predict(self, x, rescale_to=(512, 512)): - x = self.forward(x) - x = F.interpolate(input=x, size=rescale_to, mode="bilinear") - return x diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/depth/run.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/depth/run.py deleted file mode 100644 index 60ad6e20dfc2ab21aeb2c90276d2f770001794c5..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/depth/run.py +++ /dev/null @@ -1,142 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import json -import logging -import os -import sys -from typing import Any, Dict - -import torch -from omegaconf import OmegaConf - -import dinov3.distributed as distributed -from dinov3.eval.depth.checkpoint_utils import find_latest_checkpoint -from dinov3.eval.depth.config import DepthConfig -from dinov3.eval.depth.eval import evaluate_depther_with_config -from dinov3.eval.depth.models import make_depther_from_config -from dinov3.eval.depth.train import train_model_with_backbone - -from dinov3.eval.helpers import args_dict_to_dataclass, cli_parser, write_results -from dinov3.eval.setup import load_model_and_context -from dinov3.run.init import job_context -from dinov3.hub.depthers import _get_depther_config, dinov3_vit7b16_dd - -RESULTS_FILENAME = "results-depth.csv" -MAIN_METRICS = [".*_abs_rel", ".*_a1", ".*_rmse"] - - -logger = logging.getLogger("dinov3") - - -def _add_dataset_prefix_to_results(results_dict: Dict[str, float], dataset_name: str): - final_dict = {dataset_name + "_" + k: v for k, v in results_dict.items()} - return final_dict - - -def eval_depther_with_model(*, depther: torch.nn.Module, config: DepthConfig): - if config.load_from is None: - config.load_from = find_latest_checkpoint(config.output_dir) - - logger.info(f"Using config: \n {OmegaConf.to_yaml(config)}") - results_dict, _, _ = evaluate_depther_with_config( - config=config, - depther=depther, - device=distributed.get_rank(), - reduce_results=False, - ) - test_config_name = config.datasets.test.split(":", 1)[0] - test_save_dir = os.path.join(config.output_dir, test_config_name) - # reduce results - if distributed.is_main_process(): - if not os.path.exists(test_save_dir): - os.makedirs(test_save_dir) - with open(os.path.join(test_save_dir, "results.json"), "w") as f: - json.dump(results_dict, f, indent=4) - for metric, values in results_dict.items(): - results_dict[metric] = float(torch.Tensor(values).nanmean()) # result can be NaN if ground truth is all masked - summary = " \n====== Summary ======\n" - summary += ( - f"{test_config_name:<10} " - + " ".join([f"{metric}: {value:.3f}" for metric, value in results_dict.items()]) - + "\n" - ) - results_dict = _add_dataset_prefix_to_results(results_dict, test_config_name) - summary += "=====================" - logger.info(summary) - return results_dict - - -def benchmark_launcher(eval_args: dict[str, Any]) -> dict[str, Any]: - """Initialization of distributed and logging are preconditions for this method""" - if "config" in eval_args: - base_config_path = eval_args.pop("config") - output_dir = eval_args["output_dir"] - base_config = OmegaConf.load(base_config_path) - structured_config = OmegaConf.structured(DepthConfig) - depth_config: DepthConfig = OmegaConf.to_object( # type: ignore - OmegaConf.merge( - structured_config, - base_config, - OmegaConf.create(eval_args), - ) - ) - else: - depth_config, output_dir = args_dict_to_dataclass( - eval_args=eval_args, config_dataclass=DepthConfig, save_config=False - ) - OmegaConf.save(config=depth_config, f=os.path.join(output_dir, "depth_config.yaml")) - - config_autocast_dtype = depth_config.model_dtype.autocast_dtype if depth_config.model_dtype is not None else None - if depth_config.load_from == "dinov3_vit7b16_dd": - with torch.device("cuda" if torch.cuda.is_available() else "cpu"): - autocast_dtype = config_autocast_dtype or torch.float32 - # override config parameters with those of the pretrained depther - depther_config = _get_depther_config("dinov3_vit7b16") - depth_config.decoder_head = OmegaConf.to_object( # type: ignore - OmegaConf.merge( - depth_config.decoder_head, - depther_config, - ) - ) - - depther = dinov3_vit7b16_dd( - pretrained=True, - autocast_dtype=autocast_dtype, - ) - else: - with torch.device("cuda" if torch.cuda.is_available() else "cpu"): - assert depth_config.model is not None - model, model_context = load_model_and_context(depth_config.model, output_dir=output_dir) - autocast_dtype = config_autocast_dtype or model_context["autocast_dtype"] - - if depth_config.load_from: - depther = make_depther_from_config( - backbone=model, - config=depth_config.decoder_head, - checkpoint_path=depth_config.load_from, - autocast_dtype=autocast_dtype, - ) - logger.info(f"Depth config:\n {OmegaConf.to_yaml(depth_config)}") - else: - # train backbone - depther = train_model_with_backbone(depth_config, model, autocast_dtype) - - results_dict = eval_depther_with_model(depther=depther, config=depth_config) - write_results(results_dict, output_dir, RESULTS_FILENAME) - return results_dict - - -def main(argv=None): - if argv is None: - argv = sys.argv[1:] - eval_args = cli_parser(argv) - with job_context(output_dir=eval_args["output_dir"]): - benchmark_launcher(eval_args=eval_args) - return 0 - - -if __name__ == "__main__": - main() diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/depth/schedulers.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/depth/schedulers.py deleted file mode 100644 index 394f841634ad19aeddc997f3ee8966ceb54b014e..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/depth/schedulers.py +++ /dev/null @@ -1,261 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -from inspect import signature -import math -from typing import Any, Literal - -import torch -from packaging.version import Version -from torch.optim import lr_scheduler as torch_schedulers -from torch.optim.optimizer import Optimizer - - -TORCH_VERSION = Version(torch.__version__) - - -def annealing_cos(start, end, pct): - "Cosine anneal from `start` to `end` as pct goes from 0.0 to 1.0." - cos_out = math.cos(math.pi * pct) + 1 - return end + (start - end) / 2.0 * cos_out - - -def annealing_linear(start, end, pct): - "Linearly anneal from `start` to `end` as pct goes from 0.0 to 1.0." - return (end - start) * pct + start - - -class WarmupOneCycleLR(torch_schedulers.LRScheduler): - def __init__( - self, - optimizer: Optimizer, - total_steps: int = 0, - warmup_iters: int = 0, - warmup_ratio: float = 0.0, # XXX: warmup ratio to deprecate, previously defined in mmcv segmentation code - pct_start: float = 0.295, - max_lr: float | list[float] | None = None, - anneal_strategy: Literal["cos", "linear"] = "cos", - base_momentum: float | tuple[float, ...] = 0.85, - max_momentum: float | tuple[float, ...] = 0.95, - div_factor: float = 25.0, - final_div_factor: float = 1000.0, - use_beta1: bool = True, - update_momentum: bool = True, - last_epoch: int = -1, - ): - """ - A variant of OneCycleLR with a warmup on top which potentially - replaces the first phase of the original OneCycleLR. - """ - self.warmup_iters = warmup_iters - self.warmup_ratio = warmup_ratio - self.min_point = float(pct_start * total_steps) - self.base_momentum = base_momentum - self.max_momentum = max_momentum - self.total_steps = total_steps - self.use_beta1 = use_beta1 - self.anneal_strategy = anneal_strategy - self.final_div_factor = final_div_factor - self.update_momentum = update_momentum - assert self.anneal_strategy in [ - "cos", - "linear", - ], f"Only cosine and linear-annealing strategy supported, got {self.anneal_strategy}" - assert total_steps > 0 - - if isinstance(max_lr, float): - max_lr = [max_lr] # type: ignore - # Initialize learning rate variables and momentum - for idx, group in enumerate(optimizer.param_groups): - if "initial_lr" not in group: - assert last_epoch == -1 - try: # this avoids getting an error when there are multiple lrs AND weight decay values - ml = max_lr[idx] # type: ignore - except IndexError: - ml = group["lr"] - assert isinstance(ml, float) # makes sure that the variable is well updated - group["initial_lr"] = ml / div_factor - group["max_lr"] = ml - group["min_lr"] = group["initial_lr"] / final_div_factor - # initialize learning rate - group["lr"] = ml / final_div_factor if self.warmup_iters > 0 else group["initial_lr"] - if self.use_beta1: - group["betas"] = (self.max_momentum, *group["betas"][1:]) - elif self.update_momentum: - group["momentum"] = self.max_momentum - - super().__init__(optimizer, last_epoch) - - def _anneal_func(self, *args, **kwargs): - if self.anneal_strategy == "cos": - return annealing_cos(*args, **kwargs) - elif self.anneal_strategy == "linear": - return annealing_linear(*args, **kwargs) - - def _compute_lr_momentum(self, optimizer_param_group): - # torch.optim.lr_scheduler.LRScheduler does an initial - # step that sets self._step_count = 1 - step_num = (self._step_count - 1) if self.last_epoch != -1 else 0 - momentum = 0 - if step_num < self.warmup_iters: - if self.warmup_ratio: # XXX (remove in the next BW-compatibility breaking cleanup) - k = (1 - step_num / self.warmup_iters) * (1 - self.warmup_ratio) - warmup_lr = optimizer_param_group["max_lr"] * (1 - k) - thelr = warmup_lr * (1 - step_num / self.total_steps) - else: - gmax = ( - optimizer_param_group["max_lr"] * (1 + math.cos(math.pi * step_num / float(self.total_steps))) / 2 - ) - thelr = optimizer_param_group["max_lr"] / self.final_div_factor + gmax * step_num / float( - self.warmup_iters - ) - else: - pct = (step_num - self.warmup_iters) / float(self.total_steps - self.warmup_iters) - step_num_to_use = step_num - momentum = self._anneal_func( - self.base_momentum, - self.max_momentum, - pct, - ) - if self.anneal_strategy == "cos": - step_num_to_use += 1 - thelr = self._anneal_func( - optimizer_param_group["max_lr"], - optimizer_param_group["min_lr"], - step_num_to_use / float(self.total_steps), - ) - return thelr, momentum - - def get_lr(self): - """Compute the learning rate of each parameter group.""" - if TORCH_VERSION >= Version("2.4.0"): - torch_schedulers._warn_get_lr_called_within_step(self) - - lrs = [] - step_num = self.last_epoch - - if step_num > self.total_steps: - raise ValueError( - f"Tried to step {step_num} times. The specified number of total steps is {self.total_steps}" # noqa: UP032 - ) - - for group in self.optimizer.param_groups: - computed_lr, computed_momentum = self._compute_lr_momentum(group) - lrs.append(computed_lr) # type: ignore[possibly-undefined] - if self.use_beta1: - group["betas"] = (computed_momentum, *group["betas"][1:]) # type: ignore[possibly-undefined] - elif self.update_momentum: - group["momentum"] = computed_momentum # type: ignore[possibly-undefined] - - return lrs - - -class WarmupMultiStepLR(torch_schedulers.LRScheduler): - def __init__( - self, - optimizer: Optimizer, - total_steps: int = 0, - milestones: list[float] = [0.5, 0.9, 1.0], - gamma: float = 0.1, - warmup_iters: int = 0, - max_lr: float | list[float] | None = None, - last_epoch: int = -1, - ): - """ - A variant of MultiStepLR with a warmup on top which potentially - replaces the first phase of the original OneCycleLR. - Instead of using epochs to define the milestones, this scheduler uses number of iterations - as it is the case when training dense heads. Two main parameters are: - - milestones (list of floats, between 0-1): indicates the % of iterations after which - the step schedule will be applied. - - gamma (float): factor to multiply the lr by, at each milestone - """ - self.milestones = milestones - self.milestone_index = 0 - self.gamma = gamma - self.warmup_iters = warmup_iters - self.total_steps = total_steps - assert total_steps > 0 - - max_lr_list = [max_lr] if isinstance(max_lr, float) else max_lr - # Initialize learning rate variables and momentum - for idx, group in enumerate(optimizer.param_groups): - if "initial_lr" not in group: - assert last_epoch == -1 - max_lr = max_lr_list[idx] if max_lr_list else group["lr"] - group["initial_lr"] = max_lr - group["max_lr"] = max_lr - super().__init__(optimizer, last_epoch) - - def _compute_lr(self, optimizer_param_group): - if self.warmup_iters > 0 and self._step_count < self.warmup_iters: - thelr = optimizer_param_group["max_lr"] * (self._step_count / self.warmup_iters) - else: - if self._step_count >= self.total_steps * self.milestones[self.milestone_index]: - self.milestone_index += 1 - thelr = optimizer_param_group["max_lr"] * (self.gamma**self.milestone_index) - return thelr - - def get_lr(self): - """Compute the learning rate of each parameter group.""" - torch_schedulers._warn_get_lr_called_within_step(self) - - lrs = [] - step_num = self.last_epoch - - if step_num > self.total_steps: - raise ValueError( - f"Tried to step {step_num} times. The specified number of total steps is {self.total_steps}" # noqa: UP032 - ) - for group in self.optimizer.param_groups: - computed_lr = self._compute_lr(group) - lrs.append(computed_lr) # type: ignore[possibly-undefined] - - return lrs - - -def build_scheduler( - scheduler_type: str, - optimizer: Optimizer, - lr: float, - total_iter: int, - constructor_kwargs: dict[str, Any], -): - _kwargs: dict[str, Any] = {} - _kwargs.update(**constructor_kwargs) - constructor_fn = SCHEDULERS_DICT[scheduler_type] - accepted_kwargs = signature(constructor_fn).parameters.keys() - keywords = list(constructor_kwargs.keys()) - for key in keywords: - if key not in accepted_kwargs: - # ignore arguments that are not part of kwargs - _kwargs.pop(key) - if scheduler_type in ["OneCycleLR", "WarmupOneCycleLR", "WarmupMultiStepLR"]: - _kwargs.update( - dict( - max_lr=lr, - total_steps=total_iter, - ) - ) - elif scheduler_type in [ - "ConstantLR", - "LinearLR", - "PolynomialLR", - ]: - constructor_kwargs.update(dict(total_iters=total_iter)) - - return constructor_fn(optimizer, **_kwargs) - - -SCHEDULERS_DICT = { - "ConstantLR": torch_schedulers.ConstantLR, - "LinearLR": torch_schedulers.LinearLR, - "MultiStepLR": torch_schedulers.MultiStepLR, - "PolynomialLR": torch_schedulers.PolynomialLR, - "StepLR": torch_schedulers.StepLR, - "OneCycleLR": torch_schedulers.OneCycleLR, - "WarmupOneCycleLR": WarmupOneCycleLR, - "WarmupMultiStepLR": WarmupMultiStepLR, -} diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/depth/train.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/depth/train.py deleted file mode 100644 index 2f44095ef9b0b1f8af2c7e6ece00eec73c6f1dee..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/depth/train.py +++ /dev/null @@ -1,292 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import logging -import os -from typing import Any, Callable - -import torch -import torch.distributed as dist -from omegaconf import OmegaConf -from torch.nn.parallel import DistributedDataParallel as DDP -from torch.optim.optimizer import Optimizer -from torch.optim.lr_scheduler import LRScheduler - -import dinov3.distributed as distributed -from dinov3.eval.depth.data import build_dataloader -from dinov3.eval.depth.config import ( - DepthConfig, - ResultConfig, - make_depth_eval_transforms_from_config, - make_depth_train_transforms_from_config, -) - -from dinov3.eval.depth.checkpoint_utils import find_latest_checkpoint, load_checkpoint, save_checkpoint -from dinov3.eval.depth.datasets.datasets_utils import _EvalCropType, make_valid_mask -from dinov3.eval.depth.loss import MultiLoss -from dinov3.eval.depth.models import Depther, make_depther_from_config -from dinov3.eval.depth.metrics import DEPTH_METRICS -from dinov3.eval.depth.schedulers import build_scheduler -from dinov3.eval.depth.eval import evaluate_depther_with_dataloader - -from dinov3.eval.depth.utils import setup_model_ddp -from dinov3.logging import MetricLogger, SmoothedValue -from dinov3.utils import fix_random_seeds - -logger = logging.getLogger("dinov3") - - -class IterBasedTrainer: - def __init__( - self, - config: DepthConfig, - depther: Depther, - train_dataloader: Any, - val_dataloader: Any, - criterion: Callable, - metrics: list[str], - optimizer: Optimizer, - scheduler: LRScheduler, - ): - self._train_dataset_epoch = 0 # a counter for how many times all images in the dataset were seen - self.rank = distributed.get_rank() - - self.depther = depther - self.train_dataloader = train_dataloader - self.val_dataloader = val_dataloader - self.train_dataloader.sampler.set_epoch(self._train_dataset_epoch) - torch.backends.cudnn.benchmark = True - self.optimizer = optimizer - self.scheduler = scheduler - self.criterion = criterion - - self.global_step = 0 - self.total_iter = config.scheduler.total_iter - self._halt_trainer = False - - # filter out metrics that won't be tracked - self.metrics = [metric for metric in DEPTH_METRICS if metric.name in metrics] - self.config = config - - def train_on_batch(self, batch): - assert not self._halt_trainer - - device = self.rank - batch_img, depth_gt = batch - batch_img = batch_img.to(device) - depth_gt = depth_gt.to(device) - - valid_mask = make_valid_mask( - depth_gt, - eval_crop=_EvalCropType(self.config.transforms.train.eval_mask), - ignored_value=self.config.eval.ignored_value, - ).bool() - # mask out pixels outside of valid region - depth_gt[~valid_mask] = self.config.eval.ignored_value - - self.optimizer.zero_grad(set_to_none=True) - - # c) forward pass - pred = self.depther(batch_img) - # d) resize -if necessary- prediction to match size of gt - - if depth_gt.shape != pred.shape: - pred = torch.nn.functional.interpolate( - input=pred, - size=depth_gt.shape[2:], - mode="bilinear", - align_corners=False, - ) - loss = self.criterion(pred, depth_gt, valid_mask) - - # e) optimization - loss.backward() - torch.nn.utils.clip_grad_norm_( - [p for p in self.depther.parameters() if p.requires_grad], - self.config.optimizer.gradient_clip, - ) - self.optimizer.step() - self.scheduler.step() - - self.global_step += 1 - if self.global_step >= self.total_iter: - self._halt_trainer = False - - # update epoch for dataset - if self.global_step % len(self.train_dataloader): - self._train_dataset_epoch += 1 - self.train_dataloader.sampler.set_epoch(self._train_dataset_epoch) - - return loss - - def validate(self): - """ - Runs evaluation on the validation set - - Returns True if the selected target metric is better than the previous best one - """ - depther = self.depther - # unwrap DDP - if isinstance(depther, DDP): - depther = depther.module - - self.depther.eval() - new_metric_values_dict, _, _ = evaluate_depther_with_dataloader( - dataloader=self.val_dataloader, - depther=self.depther, - device=self.rank, - metrics=self.metrics, - eval_range=(self.config.eval.min_depth, self.config.eval.max_depth), - result_config=ResultConfig(save_results=False), - save_dir=self.config.output_dir, - ignored_value=self.config.eval.ignored_value, - eval_mask_type=self.config.transforms.eval.eval_mask, - align_least_squares=self.config.eval.align_least_squares, - use_tta=False, - ) - - # put model back to train mode after validation - self.depther.decoder.train() - - -def run_epochs(config: DepthConfig, backbone: torch.nn.Module, autocast_dtype: torch.dtype): - n_gpus = distributed.get_world_size() - - # 1- define decoder(s) and optimizer - optim_param_groups = [] - depther = make_depther_from_config( - backbone, - config.decoder_head, - autocast_dtype=autocast_dtype, - ) - depther.train() - if torch.cuda.is_available(): - depther = depther.cuda() - optim_param_groups.append( - { - "params": depther.decoder.parameters(), - "lr": config.optimizer.lr, - "betas": (config.optimizer.beta1, config.optimizer.beta2), - "weight_decay": config.optimizer.weight_decay, - } - ) - depther.decoder = setup_model_ddp(depther.decoder, device=distributed.get_rank()) - optimizer = torch.optim.AdamW(optim_param_groups) - - # 2- define scheduler - scheduler = build_scheduler( - config.scheduler.type, - optimizer=optimizer, - lr=config.optimizer.lr, - total_iter=config.scheduler.total_iter, - constructor_kwargs=config.scheduler.constructor_kwargs, - ) - - # 3- define transforms and dataloaders - train_transforms = make_depth_train_transforms_from_config(config) - val_transforms = make_depth_eval_transforms_from_config(config, split="val") - train_dataloader = build_dataloader( - transforms=train_transforms, - dataset_str=getattr(config.datasets, "train") + f":root={config.datasets.root}", - device=torch.cuda.current_device(), - split="train", - batch_size=config.bs, - n_gpus=n_gpus, - num_workers=n_gpus, - use_init_fn=True, - ) - - val_dataloader = build_dataloader( - transforms=val_transforms, - dataset_str=getattr(config.datasets, "val") + f":root={config.datasets.root}", - device=torch.cuda.current_device(), - split="val", - batch_size=1, - n_gpus=n_gpus, - num_workers=n_gpus, - ) - - # 4- define criterion - assert config.losses is not None, "No loss defined for training" - criterion = MultiLoss(dict_losses=config.losses) - - trainer = IterBasedTrainer( - config, - depther=depther, - train_dataloader=train_dataloader, - val_dataloader=val_dataloader, - criterion=criterion, - metrics=config.metrics, - optimizer=optimizer, - scheduler=scheduler, - ) - - if config.load_from is None: - config.load_from = find_latest_checkpoint(config.output_dir) # returns "" if the path does not exist - - if config.load_from is not None: - logger.info(f"RESUMING CHECKPOINT from {config.load_from}") - chkpt, iteration = load_checkpoint(config.load_from) - depther.decoder.load_state_dict(chkpt["model"]) - optimizer.load_state_dict(chkpt["optimizer"]) - trainer.global_step = iteration or float("inf") # type: ignore - - metric_logger = MetricLogger(delimiter=" ") - metric_logger.add_meter("loss", SmoothedValue(window_size=4, fmt="{value:.3f}")) - logger.info(f"Built trainer with start: {trainer.global_step} | total_iter {trainer.total_iter}") - - for batch in metric_logger.log_every( - trainer.train_dataloader, - 50, - header="Train: ", - start_iteration=trainer.global_step, - n_iterations=trainer.total_iter, - ): - if trainer.global_step >= trainer.total_iter: - break - - loss = trainer.train_on_batch(batch) - metric_logger.update(loss=loss) - - if trainer.global_step % config.eval.eval_interval == 0: - dist.barrier() - trainer.validate() - if distributed.is_main_process(): - save_checkpoint( - config.output_dir, - iteration=trainer.global_step, - model=trainer.depther.decoder, - optimizer=trainer.optimizer, - ) - metric_logger.synchronize_between_processes() - - # one last validation only if the number of total iterations is NOT divisible by eval interval: - if trainer.total_iter % config.eval.eval_interval: - trainer.validate() - metric_logger.synchronize_between_processes() - - logger.info("done!") - if distributed.is_main_process(): - save_checkpoint( - config.output_dir, - iteration=trainer.global_step, - model=trainer.depther.decoder, - optimizer=trainer.optimizer, - ) - - # load selected model checkpoint and return the model - dist.barrier() - depther.eval() - return depther - - -def train_model_with_backbone(config: DepthConfig, backbone: torch.nn.Module, autocast_dtype: torch.dtype): - fix_random_seeds(config.seed + distributed.get_rank()) - - depth_file_path = os.path.join(config.output_dir, "depth_config.yaml") - OmegaConf.save(config=config, f=depth_file_path) - logger.info(f"Config:\n{OmegaConf.to_yaml(config)}") - trained_model = run_epochs(config=config, backbone=backbone, autocast_dtype=autocast_dtype) - return trained_model diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/depth/transforms.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/depth/transforms.py deleted file mode 100644 index da27e32fce978982005a4af23e51cd7f0f48b8e5..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/depth/transforms.py +++ /dev/null @@ -1,382 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -from enum import Enum -from typing import Callable - -import numpy as np -import torch -import torchvision.transforms as T -from torchvision.transforms import v2 -from torchvision import tv_tensors - - -import torchvision.transforms.functional as TF -from PIL import Image - -from dinov3.data.transforms import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD - - -class _FixedCropType(Enum): - NYU = "NYU" - FULL = "FULL" - - -class Aug: - def __call__(self, x): - raise NotImplementedError - - def inverse(self, x): - raise NotImplementedError("This function has no inverse!") - - -class ColorAug(torch.nn.Module): - """Color augmentation used in depth estimation - - Args: - prob (float, optional): The color augmentation probability. Default: None. - gamma_range(tuple[float, float], optional): Gamma range for augmentation. Default: (0.9, 1.1). - brightness_range(tuple[float, float], optional): Brightness range for augmentation. Default: (0.9, 1.1). - color_range(tuple[float, float], optional): Color range for augmentation. Default: (0.9, 1.1). - """ - - def __init__(self, prob=None, gamma_range=(0.9, 1.1), brightness_range=(0.9, 1.1), color_range=(0.9, 1.1)): - super().__init__() - self.prob = prob - self.gamma_range = gamma_range - self.brightness_range = brightness_range - self.color_range = color_range - if prob is not None: - assert prob >= 0 and prob <= 1 - - def __call__(self, img): - """Call function to apply color augmentation. - - Args: - img: Data to transform. - - Returns: - img: Randomly colored data. - """ - aug = True if np.random.rand() < self.prob else False - if aug: - image = img.permute((1, 2, 0)) * 255 # 256, 256, 3 - - # gamma augmentation - gamma = np.random.uniform(min(*self.gamma_range), max(*self.gamma_range)) - image_aug = image**gamma - - # brightness augmentation - brightness = np.random.uniform(min(*self.brightness_range), max(*self.brightness_range)) - image_aug = image_aug * brightness - - # color augmentation - colors = np.random.uniform(min(*self.color_range), max(*self.color_range), size=3) - white = np.ones((image.shape[0], image.shape[1]), dtype=np.float32) - color_image = np.stack([white * colors[i] for i in range(3)], axis=2) - image_aug *= color_image - image_aug = np.clip(image_aug, 0, 255) - image_aug = image_aug / 255 - - return image_aug.permute((2, 0, 1)) - return img - - -class ColorAugV2(torch.nn.Module): - """Color augmentation used in depth estimation - - Args: - prob (float, optional): The color augmentation probability. Default: None. - gamma_range(tuple[float, float], optional): Gamma range for augmentation. Default: (0.9, 1.1). - brightness_range(tuple[float, float], optional): Brightness range for augmentation. Default: (0.9, 1.1). - color_range(tuple[float, float], optional): Color range for augmentation. Default: (0.9, 1.1). - """ - - def __init__(self, prob=None, gamma_range=(0.9, 1.1), brightness_range=(0.9, 1.1), color_range=(0.9, 1.1)): - super().__init__() - self.prob = prob - self.img_transform = ColorAug( - prob=prob, gamma_range=gamma_range, brightness_range=brightness_range, color_range=color_range - ) - - def __call__(self, img, label): - return self.img_transform(img), label - - def __repr__(self): - repr = "ColorAug(" - repr += f"\n\tgamma_range={self.img_transform.gamma_range}," - repr += f"\n\tbrightness_range={self.img_transform.brightness_range}," - repr += f"\n\tcolor_range={self.img_transform.color_range}," - repr += f"\n\tprob={self.prob}," - repr += ")" - return repr - - -class LeftRightFlipAug(Aug): - """ - Test time augmentation for depth estimation - from https://github.com/open-mmlab/mmcv/blob/main/mmcv/transforms/processing.py#L721 - - this is just returning two versions of the same image, and the according labels - """ - - def __init__( - self, - flip: bool = False, - ): - self._flip = flip - - def __call__(self, img, label=None): - """Call function to apply test time augment transforms on results. - - Args: - img: Data to transform. - - Returns: - list: A list of augmented data. - """ - - do_flips = [False, True] if self._flip else [False] - results_images = [] - results_labels = [] - - for do_flip in do_flips: - image_aug = TF.hflip(img) if do_flip else img - results_images.append(image_aug) - label_aug = TF.hflip(label) if do_flip else label - results_labels.append(label_aug) - - return results_images, results_labels - - def inverse(self, stacked_left_right_pair: torch.Tensor) -> torch.Tensor: - if not self._flip: - return stacked_left_right_pair - - pre_aug_batch_size = stacked_left_right_pair.shape[0] // 2 - assert pre_aug_batch_size * 2 == stacked_left_right_pair.shape[0] - return ( - stacked_left_right_pair[:pre_aug_batch_size] + TF.hflip(stacked_left_right_pair[pre_aug_batch_size:]) - ) / 2 - - -class NormalizeDepth(torch.nn.Module): - def __init__(self, normalization_factor): - super().__init__() - self.factor = normalization_factor - assert self.factor > 1e-6, f"Normalization factor should be > 1e-6, got {self.factor}" - - def forward(self, img, label): - assert label is not None - label = Depth(label / self.factor) # have to rewrap otherwise it becomes a torch.Tensor - return img, label - - def __repr__(self): - repr = f"NormalizeDepth(normalization_factor={self.factor})" - return repr - - -class NYUCrop: - def __init__(self, crop_box: tuple[int, int, int, int] = (43, 45, 608, 472)): - """NYU standard krop when training monocular depth estimation on NYU dataset. - - Args: - crop_box: (x1, y1, x2, y2) of cropped region. - """ - self._orig_width = 640 - self._orig_height = 480 - self._x1, self._y1, self._x2, self._y2 = crop_box - - def __call__(self, img): - """Call function to apply NYUCrop on images.""" - orig_h, orig_w = 480, 640 - w, h = img.size if isinstance(img, Image.Image) else img.shape[-2:][::-1] - y1_new = int((self._y1 / orig_h) * h) - y2_new = int((self._y2 / orig_h) * h) - x1_new = int((self._x1 / orig_w) * w) - x2_new = int((self._x2 / orig_w) * w) - if isinstance(img, Image.Image): - output_img = img.crop((x1_new, y1_new, x2_new, y2_new)) - elif isinstance(img, (torch.Tensor, np.ndarray)): - output_img = img[..., y1_new:y2_new, x1_new:x2_new] - else: - raise NotImplementedError(f"got unsupported input type {type(img)}") - return output_img - - -class ResizeV2: - """ - Resize both image and label using different interpolation modes. - """ - - def __init__( - self, - size, - image_interpolation: T.InterpolationMode = T.InterpolationMode.BILINEAR, - label_interpolation: T.InterpolationMode = T.InterpolationMode.NEAREST, - resize_label: bool = False, - ): - self.size = size - self.image_interpolation = image_interpolation - self.label_interpolation = label_interpolation - self.resize_label = resize_label - - def __call__(self, img, label): - img = T.Resize(size=self.size, interpolation=self.image_interpolation)(img) - if self.resize_label: - label = T.Resize(size=self.size, interpolation=self.label_interpolation)(label) - return img, label - - def __repr__(self): - repr = f"Resize(img_size={self.size},label=" - repr += "None)" if not self.resize_label else f"{self.size})" - return repr - - -class FixedCrop(torch.nn.Module): - def __init__(self, crop_type: _FixedCropType | str): - super().__init__() - if isinstance(crop_type, str): - crop_type = _FixedCropType(crop_type) - self.crop: Callable - if crop_type == _FixedCropType.NYU: - self.crop = NYUCrop() - elif crop_type == _FixedCropType.FULL: - self.crop = lambda x: x - self.crop_type = crop_type - - def forward(self, img, label): - img = self.crop(img) - if label is not None: - label = self.crop(label) - return img, label - - def __repr__(self): - repr = f"FixedCrop({self.crop_type})" - return repr - - -class MaybeApply(torch.nn.Module): - def __init__(self, transform, threshold: float = 0.5): - super().__init__() - self._transform = transform - self._threshold = threshold - - def forward(self, img, label): - x = np.random.rand() - if x < self._threshold: - return self._transform(img, label) - return img, label - - -class Depth(tv_tensors.Mask): - pass - - -class ToRGBDTensorPair(torch.nn.Module): - """Read segmentation mask from arrays or PIL images""" - - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) - - def forward(self, img, label): - img = T.ToTensor()(img) - if isinstance(label, Image.Image): - label = Depth(label, dtype=torch.uint16) - return img, label - - -# Add custom parameters to Resize and Rotate transforms for Depth (use Nearest interpolation) -# https://docs.pytorch.org/vision/master/auto_examples/transforms/plot_custom_tv_tensors.html - - -@v2.functional.register_kernel(functional="resize", tv_tensor_cls=Depth) -def depth_resize(my_dp, size): - out = TF.resize(my_dp, size=size, interpolation=T.InterpolationMode.NEAREST, antialias=True) - return tv_tensors.wrap(out, like=my_dp) - - -@v2.functional.register_kernel(functional="rotate", tv_tensor_cls=Depth) -def depth_rotate(my_dp, angle, *args, **kwargs): - out = TF.rotate(my_dp, angle=angle, interpolation=T.InterpolationMode.NEAREST) - return tv_tensors.wrap(out, like=my_dp) - - -def make_depth_train_transforms( - *, - normalization_constant: float = 1.0, - rotation_angle: float = 2.5, - interpolation=T.InterpolationMode.BILINEAR, - img_size: int | tuple[int, int] | None = None, - random_crop_size: tuple[int, int] | None = (352, 704), - fixed_crop: str = "FULL", - mean: tuple[float, float, float] = IMAGENET_DEFAULT_MEAN, - std: tuple[float, float, float] = IMAGENET_DEFAULT_STD, - brightness_range: tuple[float, float] = (0.9, 1.1), -): - # Fixed geometric transforms - transforms_list: list[Callable] = [] - transforms_list.append(FixedCrop(_FixedCropType(fixed_crop))) - if img_size is not None: - transforms_list.append( - ResizeV2( - img_size, - image_interpolation=interpolation, - label_interpolation=T.InterpolationMode.NEAREST, - resize_label=True, - ) - ) - - # To (TV)tensor - transforms_list.append(ToRGBDTensorPair()) - transforms_list.append(NormalizeDepth(normalization_constant)) - - # Random geometric augmentations - transforms_list.append( - MaybeApply( - v2.Compose( - [ - v2.RandomRotation(degrees=rotation_angle, interpolation=interpolation), - ] - ), - threshold=0.5, - ) - ) - transforms_list.append(v2.RandomHorizontalFlip()) - transforms_list.append(v2.RandomCrop(random_crop_size)) - - # Random color augmentations - transforms_list.append(ColorAugV2(prob=0.5, brightness_range=brightness_range)) - - # Normalize image - transforms_list.append(v2.Normalize(mean=mean, std=std)) - - return v2.Compose(transforms_list) - - -def make_depth_eval_transforms( - *, - normalization_constant: float = 1.0, - img_size: int | tuple[int, int] | None = None, - image_interpolation: T.InterpolationMode = T.InterpolationMode.BILINEAR, - mean: tuple[float, float, float] = IMAGENET_DEFAULT_MEAN, - std: tuple[float, float, float] = IMAGENET_DEFAULT_STD, - fixed_crop: str = "FULL", - tta: bool = False, -): - transforms_list: list[Callable] = [] - # Apply the fixed evaluation crop - transforms_list.append(FixedCrop(fixed_crop)) - - # Convert image and depth to tensors - transforms_list.append(ToRGBDTensorPair()) - if img_size: - # don't resize the label for evaluation - transforms_list.append(ResizeV2(size=img_size, image_interpolation=image_interpolation, resize_label=False)) - - # Normalize input image and depth - transforms_list.append(v2.Normalize(mean=mean, std=std)) - transforms_list.append(NormalizeDepth(normalization_constant)) - transforms_list.append(LeftRightFlipAug(flip=tta)) - return v2.Compose(transforms_list) diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/depth/utils.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/depth/utils.py deleted file mode 100644 index 868152ab974c5698aedcea1734d9d2f2dca66e6f..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/depth/utils.py +++ /dev/null @@ -1,77 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import logging -import numpy as np - -import torch -from torch.nn.parallel import DistributedDataParallel as DDP - -logger = logging.getLogger("dinov3") - - -def align_depth_least_square( - gt_arr: np.ndarray | torch.Tensor, - pred_arr: np.ndarray | torch.Tensor, - valid_mask_arr: np.ndarray | torch.Tensor, - max_resolution=None, -): - """ - Adapted from Marigold - https://github.com/prs-eth/Marigold/blob/62413d56099d36573b2de1eb8c429839734b7782/src/util/alignment.py#L8 - """ - ori_shape = pred_arr.shape # input shape - dtype = pred_arr.dtype - if isinstance(pred_arr, torch.Tensor): - assert isinstance(gt_arr, torch.Tensor) and isinstance(valid_mask_arr, torch.Tensor) - pred_arr = pred_arr.to(torch.float32) # unsupported other types - device = gt_arr.device - gt_arr = gt_arr.detach().cpu().numpy() - pred_arr = pred_arr.detach().cpu().numpy() - valid_mask_arr = valid_mask_arr.detach().cpu().numpy() - - gt = gt_arr.squeeze() # [H, W] - pred = pred_arr.squeeze() - valid_mask = valid_mask_arr.squeeze() - - # Downsample - if max_resolution is not None: - scale_factor = np.min(max_resolution / np.array(ori_shape[-2:])) - if scale_factor < 1: - downscaler = torch.nn.Upsample(scale_factor=scale_factor, mode="nearest") - gt = downscaler(torch.as_tensor(gt).unsqueeze(0)).numpy() - pred = downscaler(torch.as_tensor(pred).unsqueeze(0)).numpy() - valid_mask = downscaler(torch.as_tensor(valid_mask).unsqueeze(0).float()).bool().numpy() - - assert gt.shape == pred.shape == valid_mask.shape, f"{gt.shape}, {pred.shape}, {valid_mask.shape}" - - gt_masked = gt[valid_mask].reshape((-1, 1)) - pred_masked = pred[valid_mask].reshape((-1, 1)) - - # numpy solver - _ones = np.ones_like(pred_masked) - A = np.concatenate([pred_masked, _ones], axis=-1) - try: - X = np.linalg.lstsq(A, gt_masked, rcond=None)[0] - scale, shift = X - except np.linalg.LinAlgError: - scale = 1 - shift = 0 - logger.info(f"Found wrong depth: \n Pred m:{pred_arr.min()} M:{pred_arr.max()} mean: {pred_arr.mean()}") - logger.info(f"Gt m:{gt_arr.min()} M:{gt_arr.max()} mean: {gt_arr.mean()}") - - aligned_pred = pred_arr * scale + shift - - # restore dimensions - aligned_pred = aligned_pred.reshape(ori_shape) - if isinstance(aligned_pred, np.ndarray): - aligned_pred = torch.as_tensor(aligned_pred, dtype=dtype, device=device) - return aligned_pred, scale, shift - - -def setup_model_ddp(model: torch.nn.Module, device: torch.device | int): - model = DDP(model.to(device), device_ids=[device]) - logger.info(f"Model moved to rank {device}") - return model diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/depth/visualization_utils.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/depth/visualization_utils.py deleted file mode 100644 index e681f45bf90f43faa0eb67bc934bd869bd37d71a..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/depth/visualization_utils.py +++ /dev/null @@ -1,160 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import os -from typing import Callable - -import matplotlib -import numpy as np -import torch -import torchvision.transforms as transforms -from PIL import Image - -from dinov3.eval.depth.config import ResultConfig, ResultExtension - -from dinov3.data.transforms import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD - - -def alpha_blend(img_pil: Image.Image, mask_rgb: np.ndarray, alpha: float = 0.5) -> Image.Image: - img_rgba = img_pil.convert("RGBA") - mask_alpha = np.full_like(mask_rgb, np.round(255 * alpha))[..., 0:1] - mask_rgba = Image.fromarray(np.concatenate([mask_rgb, mask_alpha], axis=-1).astype(np.uint8)) - overlay = Image.alpha_composite(img_rgba, mask_rgba) - return overlay.convert("RGB") - - -def normalized_tensor_to_pil( - tensor: torch.Tensor, - mean=IMAGENET_DEFAULT_MEAN, - std=IMAGENET_DEFAULT_STD, -) -> Image.Image: - """ - Transforms a normalized image tensor back into PIL image. - """ - assert tensor.ndim == 3 and tensor.shape[0] == 3, f"input should be 3xHxW, got {tensor.shape}" - std = torch.tensor(std, device=tensor.device)[:, None, None] - mean = torch.tensor(mean, device=tensor.device)[:, None, None] - unnormalized_tensor = torch.clamp(tensor * std + mean, 0.0, 1.0) - return transforms.functional.to_pil_image(unnormalized_tensor) - - -def depth_tensor_to_colorized_pil(depth_tensor: torch.Tensor, cmap="plasma", vmin=None, vmax=None): - # derived from https://github.com/zhyever/Monocular-Depth-Estimation-Toolbox/blob/main/depth/utils/color_depth.py - value = depth_tensor.detach().cpu().reshape(depth_tensor.shape[-2:]).numpy() - # normalize - vmin = value.min() if vmin is None else vmin - vmax = value.max() if vmax is None else vmax - if vmin != vmax: - value = (value - vmin) / (vmax - vmin) # vmin..vmax - else: - # Avoid 0-division - value = value * 0.0 - cmapper = matplotlib.cm.get_cmap(cmap) - value = cmapper(value, bytes=True) # ((1)xhxwx4) - value = value[:, :, :3] # bgr -> rgb - rgb_value = value # [..., ::-1] - return Image.fromarray(rgb_value) - - -def save_raw_predictions( - img: torch.Tensor, # [1, 3, H, W] - pred: torch.Tensor, # [1, C, H, W] - gt: torch.Tensor, # [1, C, H, W] - save_dir: str, - save_index: int, -) -> None: - torch.save( - { - "image": transforms.functional.to_tensor(normalized_tensor_to_pil(img[0])).cpu(), - "pred": pred.detach().cpu(), - "target": gt.cpu(), - }, - os.path.join(save_dir, f"results_{int(save_index)}.pth"), - ) - - -def get_prediction_images( - img: torch.Tensor, # [1, 3, H, W] - pred: torch.Tensor, # [1, C, H, W] - gt: torch.Tensor, # [1, C, H, W] - pred_tensor_to_pil: Callable[[torch.Tensor], Image.Image], - alpha: float = 1.0, -) -> tuple[Image.Image, Image.Image, Image.Image]: - img_pil = normalized_tensor_to_pil(img[0]) - assert pred.shape[0] == gt.shape[0] == 1 - pred_pil = pred_tensor_to_pil(pred[0].cpu()) - gt_pil = pred_tensor_to_pil(gt[0].cpu()) - if img_pil.size != gt_pil.size: - img_pil = img_pil.resize(gt_pil.size) - - if alpha < 1.0: - pred_pil = alpha_blend(img_pil, np.array(pred_pil), alpha) - gt_pil = alpha_blend(img_pil, np.array(gt_pil), alpha) - return img_pil, pred_pil, gt_pil - - -def resize_results( - img_pil: Image.Image, - pred_pil: Image.Image, - gt_pil: Image.Image, - resolution: int, -) -> tuple[Image.Image, Image.Image, Image.Image]: - img_pil = transforms.functional.resize( - img_pil, - resolution, - interpolation=transforms.InterpolationMode.BILINEAR, - ) - pred_pil, gt_pil = [ - transforms.functional.resize( - x, - resolution, - interpolation=transforms.InterpolationMode.NEAREST, - ) - for x in [pred_pil, gt_pil] - ] - return img_pil, pred_pil, gt_pil - - -def save_predictions( - img: torch.Tensor, # [1, 3, H, W] - pred: torch.Tensor, # [1, C, H, W] - gt: torch.Tensor, # [1, C, H, W] - result_config: ResultConfig, - save_dir: str, - save_index: int, - pred_tensor_to_pil_fn: Callable, -) -> None: - vis_save_dir = os.path.join(save_dir, "visualizations") - os.makedirs(vis_save_dir, exist_ok=True) - if result_config.extension == ResultExtension.PTH: - save_raw_predictions( - img=img, - pred=pred, - gt=gt, - save_dir=vis_save_dir, - save_index=save_index, - ) - return - - img_pil, pred_pil, gt_pil = get_prediction_images( - img=img, - pred=pred, - gt=gt, - pred_tensor_to_pil=pred_tensor_to_pil_fn, - alpha=result_config.overlay_alpha, - ) - - if result_config.save_resolution: - img_pil, pred_pil, gt_pil = resize_results(img_pil, pred_pil, gt_pil, result_config.save_resolution) - - ext = result_config.extension.value - if result_config.save_separate_files: - img_pil.save(os.path.join(vis_save_dir, f"image_{int(save_index)}.{ext}")) - pred_pil.save(os.path.join(vis_save_dir, f"pred_{int(save_index)}.{ext}")) - gt_pil.save(os.path.join(vis_save_dir, f"gt_{int(save_index)}.{ext}")) - - band = np.concatenate([np.array(segm) for segm in [pred_pil, gt_pil]], axis=1) # horizontal band - band = Image.fromarray(band) - band.save(os.path.join(vis_save_dir, f"results_{int(save_index)}.{ext}")) diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/detection/config.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/detection/config.py deleted file mode 100644 index 38cfea3f3e021880e41321dfd419c3e20cbeceb4..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/detection/config.py +++ /dev/null @@ -1,65 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -from dataclasses import dataclass - -from .models.position_encoding import PositionEncoding - - -@dataclass(kw_only=True) -class DetectionHeadConfig: - num_classes: int = 91 # 91 classes in COCO - # Deformable DETR tricks - with_box_refine: bool = True - two_stage: bool = True - # DINO DETR tricks - mixed_selection: bool = True - look_forward_twice: bool = True # was default False - # Hybrid Matching tricks - k_one2many: int = 6 # was 5 - lambda_one2many: float = 1.0 - num_queries_one2one: int = 300 # number of query slots for one_to_one matching - num_queries_one2many: int = 1500 # was 0, number of query slots for one_to_many matching - """ - Absolute coordinates & box regression reparameterization. - If true, we use absolute coordindates & reparameterization for bounding boxes. - """ - reparam: bool = True - topk: int = 100 - - # * Backbone - # type of positional embedding to use on top of the image features - position_embedding: PositionEncoding = PositionEncoding.SINE - num_feature_levels: int = 1 # number of feature levels - - # * Transformer - dec_layers: int = 6 # number of decoding layers in the transformer - dim_feedforward: int = 2048 # intermediate size of the feedforward layers in the transformer blocks - hidden_dim: int = 256 # size of the embeddings (dimension of the transformer) - dropout: float = 0.0 # dropout applied in the transformer, was 0.1 - nheads: int = 8 # number of attention heads inside the transformer's attentions - norm_type: str = "pre_norm" - - # Loss - aux_loss: bool = True # auxiliary decoding losses (loss at each layer) - - # * dev: proposals - proposal_feature_levels: int = 4 # was 1 - proposal_min_size: int = 50 - # * dev decoder: global decoder - decoder_type: str = "global_rpe_decomp" # was deform - decoder_use_checkpoint: bool = False - decoder_rpe_hidden_dim: int = 512 - decoder_rpe_type: str = "linear" - - # Custom - add_transformer_encoder: bool = True - num_encoder_layers: int = 6 - layers_to_use: list[int] | None = None - blocks_to_train: list[int] | None = None - n_windows_sqrt: int = 0 - proposal_in_stride: int | None = None - proposal_tgt_strides: list[int] | None = None - backbone_use_layernorm: bool = False # whether to use layernorm on each layer of the backbone's features diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/detection/models/__init__.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/detection/models/__init__.py deleted file mode 100644 index 5375bc66e1ed841a7091b81a0dcf56d1993c1f87..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/detection/models/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/detection/models/backbone.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/detection/models/backbone.py deleted file mode 100644 index edc1c69dd328769acfb7319db940bf08dced43ff..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/detection/models/backbone.py +++ /dev/null @@ -1,117 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -# ------------------------------------------------------------------------ -# Plain-DETR -# Copyright (c) 2023 Xi'an Jiaotong University & Microsoft Research Asia. -# Licensed under The MIT License [see LICENSE for details] -# ------------------------------------------------------------------------ -# Deformable DETR -# Copyright (c) 2020 SenseTime. All Rights Reserved. -# Licensed under the Apache License, Version 2.0 [see LICENSE for details] -# ------------------------------------------------------------------------ -# Modified from DETR (https://github.com/facebookresearch/detr) -# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved -# ------------------------------------------------------------------------ - -""" -Backbone modules. -""" -import logging -from typing import List, Optional, Union - -import torch -import torch.nn.functional as F -from torch import nn - -from ..util.misc import NestedTensor -from .position_encoding import build_position_encoding -from .utils import LayerNorm2D -from .windows import WindowsWrapper - -logger = logging.getLogger("dinov3") - - -class DINOBackbone(nn.Module): - def __init__( - self, - backbone_model: nn.Module, - train_backbone: bool, - blocks_to_train: Optional[List[str]] = None, - layers_to_use: Union[int, List] = 1, - use_layernorm: bool = True, - ): - super().__init__() - self.backbone = backbone_model - self.blocks_to_train = blocks_to_train - self.patch_size = self.backbone.patch_size - self.use_layernorm = use_layernorm - - for _, (name, parameter) in enumerate(self.backbone.named_parameters()): - train_condition = any(f".{b}." in name for b in self.blocks_to_train) if self.blocks_to_train else True - if (not train_backbone) or "mask_token" in name or (not train_condition): - parameter.requires_grad_(False) - - self.strides = [self.backbone.patch_size] - - # get embed_dim for each intermediate output - n_all_layers = self.backbone.n_blocks - blocks_to_take = ( - range(n_all_layers - layers_to_use, n_all_layers) if isinstance(layers_to_use, int) else layers_to_use - ) - - # if models do not define embed_dims, repeat embed_dim n_blocks times - embed_dims = getattr(self.backbone, "embed_dims", [self.backbone.embed_dim] * self.backbone.n_blocks) - embed_dims = [embed_dims[i] for i in range(n_all_layers) if i in blocks_to_take] - - if self.use_layernorm: - self.layer_norms = nn.ModuleList([LayerNorm2D(embed_dim) for embed_dim in embed_dims]) - - self.num_channels = [sum(embed_dims)] - self.layers_to_use = layers_to_use - - def forward(self, tensor_list: NestedTensor): - xs = self.backbone.get_intermediate_layers(tensor_list.tensors, n=self.layers_to_use, reshape=True) - if self.use_layernorm: - xs = [ln(x).contiguous() for ln, x in zip(self.layer_norms, xs)] - - xs = [torch.cat(xs, axis=1)] - - out: list[NestedTensor] = [] - for x in xs: - m = tensor_list.mask - assert m is not None - mask = F.interpolate(m[None].float(), size=x.shape[-2:]).to(torch.bool)[0] - out.append(NestedTensor(x, mask)) - return out - - -class BackboneWithPositionEncoding(nn.Sequential): - def __init__(self, backbone, position_embedding): - super().__init__(backbone, position_embedding) - self.strides = backbone.strides - self.num_channels = backbone.num_channels - - def forward(self, tensor_list: NestedTensor): - out: List[NestedTensor] = list(self[0](tensor_list)) - pos = [self[1][idx](x).to(x.tensors.dtype) for idx, x in enumerate(out)] - return out, pos - - -def build_backbone(backbone_model, args): - position_embedding = build_position_encoding(args) - train_backbone = False - backbone = DINOBackbone( - backbone_model, train_backbone, args.blocks_to_train, args.layers_to_use, args.backbone_use_layernorm - ) - if args.n_windows_sqrt > 0: - logger.info(f"Wrapping with {args.n_windows_sqrt} x {args.n_windows_sqrt} windows") - backbone = WindowsWrapper( - backbone, n_windows_w=args.n_windows_sqrt, n_windows_h=args.n_windows_sqrt, patch_size=backbone.patch_size - ) - else: - logger.info("Not wrapping with windows") - - return BackboneWithPositionEncoding(backbone, position_embedding) diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/detection/models/detr.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/detection/models/detr.py deleted file mode 100644 index c75a74f89f3b8e8255b0e4ad8ec2c7c5321204e4..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/detection/models/detr.py +++ /dev/null @@ -1,462 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -# ------------------------------------------------------------------------ -# Plain-DETR -# Copyright (c) 2023 Xi'an Jiaotong University & Microsoft Research Asia. -# Licensed under The MIT License [see LICENSE for details] -# ------------------------------------------------------------------------ -# Deformable DETR -# Copyright (c) 2020 SenseTime. All Rights Reserved. -# Licensed under the Apache License, Version 2.0 [see LICENSE for details] -# ------------------------------------------------------------------------ -# Modified from DETR (https://github.com/facebookresearch/detr) -# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved -# ------------------------------------------------------------------------ - -""" -Deformable DETR model and criterion classes. -""" -import math - -import torch -import torch.nn.functional as F -from torch import nn - -from ..util import box_ops -from ..util.misc import NestedTensor, _get_clones, inverse_sigmoid, nested_tensor_from_tensor_list -from .backbone import build_backbone -from .transformer import build_transformer - - -class PlainDETR(nn.Module): - """This is the Deformable DETR module that performs object detection""" - - def __init__( - self, - backbone, - transformer, - num_classes, - num_feature_levels, - aux_loss=True, - with_box_refine=False, - two_stage=False, - num_queries_one2one=300, - num_queries_one2many=0, - mixed_selection=False, - ): - """Initializes the model. - Parameters: - backbone: torch module of the backbone to be used. See backbone.py - transformer: torch module of the transformer architecture. See transformer.py - num_classes: number of object classes - aux_loss: True if auxiliary decoding losses (loss at each decoder layer) are to be used. - with_box_refine: iterative bounding box refinement - two_stage: two-stage Deformable DETR - num_queries_one2one: number of object queries for one-to-one matching part - num_queries_one2many: number of object queries for one-to-many matching part - mixed_selection: a trick for Deformable DETR two stage - - """ - super().__init__() - num_queries = num_queries_one2one + num_queries_one2many - self.num_queries = num_queries - self.transformer = transformer - hidden_dim = transformer.d_model - self.class_embed = nn.Linear(hidden_dim, num_classes) - self.bbox_embed = MLP(hidden_dim, hidden_dim, 4, 3) - self.num_feature_levels = num_feature_levels - if not two_stage: - self.query_embed = nn.Embedding(num_queries, hidden_dim * 2) - elif mixed_selection: - self.query_embed = nn.Embedding(num_queries, hidden_dim) - self.input_proj = nn.ModuleList( - [ - nn.Sequential( - nn.Conv2d(backbone.num_channels[0], hidden_dim, kernel_size=1), - nn.GroupNorm(32, hidden_dim), - ) - ] - ) - self.backbone = backbone - self.aux_loss = aux_loss - self.with_box_refine = with_box_refine - self.two_stage = two_stage - - prior_prob = 0.01 - bias_value = -math.log((1 - prior_prob) / prior_prob) - self.class_embed.bias.data = torch.ones(num_classes) * bias_value - nn.init.constant_(self.bbox_embed.layers[-1].weight.data, 0) - nn.init.constant_(self.bbox_embed.layers[-1].bias.data, 0) - for proj in self.input_proj: - nn.init.xavier_uniform_(proj[0].weight, gain=1) - nn.init.constant_(proj[0].bias, 0) - - # if two-stage, the last class_embed and bbox_embed is for region proposal generation - num_pred = (transformer.decoder.num_layers + 1) if two_stage else transformer.decoder.num_layers - if with_box_refine: - self.class_embed = _get_clones(self.class_embed, num_pred) - self.bbox_embed = _get_clones(self.bbox_embed, num_pred) - nn.init.constant_(self.bbox_embed[0].layers[-1].bias.data[2:], -2.0) - # hack implementation for iterative bounding box refinement - self.transformer.decoder.bbox_embed = self.bbox_embed - else: - nn.init.constant_(self.bbox_embed.layers[-1].bias.data[2:], -2.0) - self.class_embed = nn.ModuleList([self.class_embed for _ in range(num_pred)]) - self.bbox_embed = nn.ModuleList([self.bbox_embed for _ in range(num_pred)]) - self.transformer.decoder.bbox_embed = None - if two_stage: - # hack implementation for two-stage - self.transformer.decoder.class_embed = self.class_embed - for box_embed in self.bbox_embed: - nn.init.constant_(box_embed.layers[-1].bias.data[2:], 0.0) - - self.num_queries_one2one = num_queries_one2one - self.mixed_selection = mixed_selection - - def forward(self, samples: NestedTensor): - """The forward expects a NestedTensor, which consists of: - - samples.tensor: batched images, of shape [batch_size x 3 x H x W] - - samples.mask: a binary mask of shape [batch_size x H x W], containing 1 on padded pixels - - It returns a dict with the following elements: - - "pred_logits": the classification logits (including no-object) for all queries. - Shape= [batch_size x num_queries x (num_classes + 1)] - - "pred_boxes": The normalized boxes coordinates for all queries, represented as - (center_x, center_y, height, width). These values are normalized in [0, 1], - relative to the size of each individual image (disregarding possible padding). - See PostProcess for information on how to retrieve the unnormalized bounding box. - - "aux_outputs": Optional, only returned when auxilary losses are activated. It is a list of - dictionnaries containing the two above keys for each decoder layer. - """ - if not isinstance(samples, NestedTensor): - samples = nested_tensor_from_tensor_list(samples) - features, pos = self.backbone(samples) - - srcs = [] - masks = [] - for layer, feat in enumerate(features): - src, mask = feat.decompose() - srcs.append(self.input_proj[layer](src)) - masks.append(mask) - assert mask is not None - - query_embeds = None - if not self.two_stage or self.mixed_selection: - query_embeds = self.query_embed.weight[0 : self.num_queries, :] - - # make attn mask - """ attention mask to prevent information leakage - """ - self_attn_mask = torch.zeros( - [ - self.num_queries, - self.num_queries, - ], - dtype=bool, - device=src.device, - ) - self_attn_mask[ - self.num_queries_one2one :, - 0 : self.num_queries_one2one, - ] = True - self_attn_mask[ - 0 : self.num_queries_one2one, - self.num_queries_one2one :, - ] = True - - ( - hs, - init_reference, - inter_references, - enc_outputs_class, - enc_outputs_coord_unact, - enc_outputs_delta, - output_proposals, - max_shape, - ) = self.transformer(srcs, masks, pos, query_embeds, self_attn_mask) - - outputs_classes_one2one = [] - outputs_coords_one2one = [] - outputs_classes_one2many = [] - outputs_coords_one2many = [] - for lvl in range(hs.shape[0]): - if lvl == 0: - reference = init_reference - else: - reference = inter_references[lvl - 1] - reference = inverse_sigmoid(reference) - outputs_class = self.class_embed[lvl](hs[lvl]) - tmp = self.bbox_embed[lvl](hs[lvl]) - if reference.shape[-1] == 4: - tmp += reference - else: - assert reference.shape[-1] == 2 - tmp[..., :2] += reference - outputs_coord = tmp.sigmoid() - - outputs_classes_one2one.append(outputs_class[:, 0 : self.num_queries_one2one]) - outputs_classes_one2many.append(outputs_class[:, self.num_queries_one2one :]) - - outputs_coords_one2one.append(outputs_coord[:, 0 : self.num_queries_one2one]) - outputs_coords_one2many.append(outputs_coord[:, self.num_queries_one2one :]) - - outputs_classes_one2one = torch.stack(outputs_classes_one2one) - outputs_coords_one2one = torch.stack(outputs_coords_one2one) - - outputs_classes_one2many = torch.stack(outputs_classes_one2many) - outputs_coords_one2many = torch.stack(outputs_coords_one2many) - - out = { - "pred_logits": outputs_classes_one2one[-1], - "pred_boxes": outputs_coords_one2one[-1], - "pred_logits_one2many": outputs_classes_one2many[-1], - "pred_boxes_one2many": outputs_coords_one2many[-1], - } - if self.aux_loss: - out["aux_outputs"] = self._set_aux_loss(outputs_classes_one2one, outputs_coords_one2one) - out["aux_outputs_one2many"] = self._set_aux_loss(outputs_classes_one2many, outputs_coords_one2many) - - if self.two_stage: - enc_outputs_coord = enc_outputs_coord_unact.sigmoid() - out["enc_outputs"] = { - "pred_logits": enc_outputs_class, - "pred_boxes": enc_outputs_coord, - } - return out - - @torch.jit.unused - def _set_aux_loss(self, outputs_class, outputs_coord): - # this is a workaround to make torchscript happy, as torchscript - # doesn't support dictionary with non-homogeneous values, such - # as a dict having both a Tensor and a list. - return [{"pred_logits": a, "pred_boxes": b} for a, b in zip(outputs_class[:-1], outputs_coord[:-1])] - - -class PlainDETRReParam(PlainDETR): - def forward(self, samples: NestedTensor): - """The forward expects a NestedTensor, which consists of: - - samples.tensor: batched images, of shape [batch_size x 3 x H x W] - - samples.mask: a binary mask of shape [batch_size x H x W], containing 1 on padded pixels - - It returns a dict with the following elements: - - "pred_logits": the classification logits (including no-object) for all queries. - Shape= [batch_size x num_queries x (num_classes + 1)] - - "pred_boxes": The normalized boxes coordinates for all queries, represented as - (center_x, center_y, height, width). These values are normalized in [0, 1], - relative to the size of each individual image (disregarding possible padding). - See PostProcess for information on how to retrieve the unnormalized bounding box. - - "aux_outputs": Optional, only returned when auxilary losses are activated. It is a list of - dictionnaries containing the two above keys for each decoder layer. - """ - if not isinstance(samples, NestedTensor): - samples = nested_tensor_from_tensor_list(samples) - features, pos = self.backbone(samples) - - srcs = [] - masks = [] - for layer, feat in enumerate(features): - src, mask = feat.decompose() - srcs.append(self.input_proj[layer](src)) - masks.append(mask) - assert mask is not None - - query_embeds = None - if not self.two_stage or self.mixed_selection: - query_embeds = self.query_embed.weight[0 : self.num_queries, :] - - # make attn mask - """ attention mask to prevent information leakage - """ - self_attn_mask = torch.zeros( - [ - self.num_queries, - self.num_queries, - ], - dtype=bool, - device=src.device, - ) - self_attn_mask[ - self.num_queries_one2one :, - 0 : self.num_queries_one2one, - ] = True - self_attn_mask[ - 0 : self.num_queries_one2one, - self.num_queries_one2one :, - ] = True - - ( - hs, - init_reference, - inter_references, - enc_outputs_class, - enc_outputs_coord_unact, - enc_outputs_delta, - output_proposals, - max_shape, - ) = self.transformer(srcs, masks, pos, query_embeds, self_attn_mask) - - outputs_classes_one2one = [] - outputs_coords_one2one = [] - outputs_classes_one2many = [] - outputs_coords_one2many = [] - - outputs_coords_old_one2one = [] - outputs_deltas_one2one = [] - outputs_coords_old_one2many = [] - outputs_deltas_one2many = [] - - for lvl in range(hs.shape[0]): - if lvl == 0: - reference = init_reference - else: - reference = inter_references[lvl - 1] - outputs_class = self.class_embed[lvl](hs[lvl]) - tmp = self.bbox_embed[lvl](hs[lvl]) - if reference.shape[-1] == 4: - outputs_coord = box_ops.box_xyxy_to_cxcywh(box_ops.delta2bbox(reference, tmp, max_shape)) - else: - raise NotImplementedError - - outputs_classes_one2one.append(outputs_class[:, 0 : self.num_queries_one2one]) - outputs_classes_one2many.append(outputs_class[:, self.num_queries_one2one :]) - - outputs_coords_one2one.append(outputs_coord[:, 0 : self.num_queries_one2one]) - outputs_coords_one2many.append(outputs_coord[:, self.num_queries_one2one :]) - - outputs_coords_old_one2one.append(reference[:, : self.num_queries_one2one]) - outputs_coords_old_one2many.append(reference[:, self.num_queries_one2one :]) - outputs_deltas_one2one.append(tmp[:, : self.num_queries_one2one]) - outputs_deltas_one2many.append(tmp[:, self.num_queries_one2one :]) - - outputs_classes_one2one = torch.stack(outputs_classes_one2one) - outputs_coords_one2one = torch.stack(outputs_coords_one2one) - - outputs_classes_one2many = torch.stack(outputs_classes_one2many) - outputs_coords_one2many = torch.stack(outputs_coords_one2many) - - out = { - "pred_logits": outputs_classes_one2one[-1], - "pred_boxes": outputs_coords_one2one[-1], - "pred_logits_one2many": outputs_classes_one2many[-1], - "pred_boxes_one2many": outputs_coords_one2many[-1], - "pred_boxes_old": outputs_coords_old_one2one[-1], - "pred_deltas": outputs_deltas_one2one[-1], - "pred_boxes_old_one2many": outputs_coords_old_one2many[-1], - "pred_deltas_one2many": outputs_deltas_one2many[-1], - } - - if self.aux_loss: - out["aux_outputs"] = self._set_aux_loss( - outputs_classes_one2one, outputs_coords_one2one, outputs_coords_old_one2one, outputs_deltas_one2one - ) - out["aux_outputs_one2many"] = self._set_aux_loss( - outputs_classes_one2many, outputs_coords_one2many, outputs_coords_old_one2many, outputs_deltas_one2many - ) - - if self.two_stage: - out["enc_outputs"] = { - "pred_logits": enc_outputs_class, - "pred_boxes": enc_outputs_coord_unact, - "pred_boxes_old": output_proposals, - "pred_deltas": enc_outputs_delta, - } - return out - - @torch.jit.unused - def _set_aux_loss(self, outputs_class, outputs_coord, outputs_coord_old, outputs_deltas): - # this is a workaround to make torchscript happy, as torchscript - # doesn't support dictionary with non-homogeneous values, such - # as a dict having both a Tensor and a list. - return [ - { - "pred_logits": a, - "pred_boxes": b, - "pred_boxes_old": c, - "pred_deltas": d, - } - for a, b, c, d in zip(outputs_class[:-1], outputs_coord[:-1], outputs_coord_old[:-1], outputs_deltas[:-1]) - ] - - -class PostProcess(nn.Module): - """This module converts the model's output into the format expected by the coco api""" - - def __init__(self, topk=100, reparam=False): - super().__init__() - self.topk = topk - self.reparam = reparam - - @torch.no_grad() - def forward(self, outputs, target_sizes, original_target_sizes=None): - """Perform the computation - Parameters: - outputs: raw outputs of the model - target_sizes: tensor of dimension [batch_size x 2] containing the size of each images of the batch - For evaluation, this must be the original image size (before any data augmentation) - For visualization, this should be the image size after data augment, but before padding - """ - out_logits, out_bbox = outputs["pred_logits"], outputs["pred_boxes"] - - assert len(out_logits) == len(target_sizes) - assert target_sizes.shape[1] == 2 - assert not self.reparam or original_target_sizes.shape[1] == 2 - - prob = out_logits.sigmoid() - topk_values, topk_indexes = torch.topk(prob.view(out_logits.shape[0], -1), self.topk, dim=1) - scores = topk_values - topk_boxes = topk_indexes // out_logits.shape[2] - labels = topk_indexes % out_logits.shape[2] - boxes = box_ops.box_cxcywh_to_xyxy(out_bbox) - boxes = torch.gather(boxes, 1, topk_boxes.unsqueeze(-1).repeat(1, 1, 4)) - - # and from relative [0, 1] to absolute [0, height] coordinates - img_h, img_w = target_sizes.unbind(1) - if self.reparam: - img_h, img_w = img_h[:, None, None], img_w[:, None, None] # [BS, 1, 1] - boxes[..., 0::2].clamp_(min=torch.zeros_like(img_w), max=img_w) - boxes[..., 1::2].clamp_(min=torch.zeros_like(img_h), max=img_h) - scale_h, scale_w = (original_target_sizes / target_sizes).unbind(1) - scale_fct = torch.stack([scale_w, scale_h, scale_w, scale_h], dim=1) - else: - scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1) - boxes = boxes * scale_fct[:, None, :] - - results = [{"scores": s, "labels": l, "boxes": b} for s, l, b in zip(scores, labels, boxes)] - - return results - - -class MLP(nn.Module): - """Very simple multi-layer perceptron (also called FFN)""" - - def __init__(self, input_dim, hidden_dim, output_dim, num_layers): - super().__init__() - self.num_layers = num_layers - h = [hidden_dim] * (num_layers - 1) - self.layers = nn.ModuleList(nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim])) - - def forward(self, x): - for i, layer in enumerate(self.layers): - x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x) - return x - - -def build_model(backbone_model, args): - backbone = build_backbone(backbone_model, args) - transformer = build_transformer(args) - model_class = PlainDETR if (not args.reparam) else PlainDETRReParam - return model_class( - backbone, - transformer, - num_classes=args.num_classes, - num_feature_levels=args.num_feature_levels, - aux_loss=args.aux_loss, - with_box_refine=args.with_box_refine, - two_stage=args.two_stage, - num_queries_one2one=args.num_queries_one2one, - num_queries_one2many=args.num_queries_one2many, - mixed_selection=args.mixed_selection, - ) diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/detection/models/global_ape_decoder.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/detection/models/global_ape_decoder.py deleted file mode 100644 index 32b2f9a4c196a81d1448aa32a9991a311a1237d5..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/detection/models/global_ape_decoder.py +++ /dev/null @@ -1,321 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -# ------------------------------------------------------------------------ -# Plain-DETR -# Copyright (c) 2023 Xi'an Jiaotong University & Microsoft Research Asia. -# Licensed under The MIT License [see LICENSE for details] -# ------------------------------------------------------------------------ -# -*- coding: utf-8 -*- -import torch -import torch.nn as nn -import torch.utils.checkpoint as checkpoint - -from ..util.misc import _get_activation_fn, _get_clones, inverse_sigmoid - - -class GlobalCrossAttention(nn.Module): - def __init__( - self, - dim, - num_heads, - qkv_bias=True, - qk_scale=None, - attn_drop=0.0, - proj_drop=0.0, - ): - super().__init__() - self.dim = dim - self.num_heads = num_heads - head_dim = dim // num_heads - self.scale = qk_scale or head_dim**-0.5 - - self.q = nn.Linear(dim, dim, bias=qkv_bias) - self.k = nn.Linear(dim, dim, bias=qkv_bias) - self.v = nn.Linear(dim, dim, bias=qkv_bias) - self.attn_drop = nn.Dropout(attn_drop) - self.proj = nn.Linear(dim, dim) - self.proj_drop = nn.Dropout(proj_drop) - self.softmax = nn.Softmax(dim=-1) - - def forward( - self, - query, - k_input_flatten, - v_input_flatten, - input_padding_mask=None, - ): - B_, N, C = k_input_flatten.shape - k = self.k(k_input_flatten).reshape(B_, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3) - v = self.v(v_input_flatten).reshape(B_, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3) - B_, N, C = query.shape - q = self.q(query).reshape(B_, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3) - - attn_mask = None - if input_padding_mask is not None: - attn_mask = input_padding_mask[:, None, None] * -100 - attn_mask = attn_mask.contiguous() # to enable efficient attention - - x = torch.nn.functional.scaled_dot_product_attention( - query=q, - key=k, - value=v, - attn_mask=attn_mask, - dropout_p=self.attn_drop.p if self.training else 0, - scale=self.scale, - ) - x = x.transpose(1, 2).reshape(B_, N, C) - x = self.proj(x) - x = self.proj_drop(x) - return x - - -class GlobalDecoderLayer(nn.Module): - def __init__( - self, - d_model=256, - d_ffn=1024, - dropout=0.1, - activation="relu", - n_heads=8, - norm_type="post_norm", - ): - super().__init__() - - self.norm_type = norm_type - - # global cross attention - self.cross_attn = GlobalCrossAttention(d_model, n_heads) - self.dropout1 = nn.Dropout(dropout) - self.norm1 = nn.LayerNorm(d_model) - - # self attention - self.self_attn = nn.MultiheadAttention(d_model, n_heads, dropout=dropout) - self.dropout2 = nn.Dropout(dropout) - self.norm2 = nn.LayerNorm(d_model) - - # ffn - self.linear1 = nn.Linear(d_model, d_ffn) - self.activation = _get_activation_fn(activation) - self.dropout3 = nn.Dropout(dropout) - self.linear2 = nn.Linear(d_ffn, d_model) - self.dropout4 = nn.Dropout(dropout) - self.norm3 = nn.LayerNorm(d_model) - - @staticmethod - def with_pos_embed(tensor, pos): - return tensor if pos is None else tensor + pos - - def forward_pre( - self, - tgt, - query_pos, - src, - src_pos_embed, - src_padding_mask=None, - self_attn_mask=None, - ): - # self attention - tgt2 = self.norm2(tgt) - q = k = self.with_pos_embed(tgt2, query_pos) - tgt2 = self.self_attn( - q.transpose(0, 1), k.transpose(0, 1), tgt2.transpose(0, 1), attn_mask=self_attn_mask, need_weights=False - )[0].transpose(0, 1) - tgt = tgt + self.dropout2(tgt2) - - # global cross attention - tgt2 = self.norm1(tgt) - tgt2 = self.cross_attn( - self.with_pos_embed(tgt2, query_pos), - self.with_pos_embed(src, src_pos_embed), - src, - src_padding_mask, - ) - tgt = tgt + self.dropout1(tgt2) - - # ffn - tgt2 = self.norm3(tgt) - tgt2 = self.linear2(self.dropout3(self.activation(self.linear1(tgt2)))) - tgt = tgt + self.dropout4(tgt2) - - return tgt - - def forward_post( - self, - tgt, - query_pos, - src, - src_pos_embed, - src_padding_mask=None, - self_attn_mask=None, - ): - # self attention - q = k = self.with_pos_embed(tgt, query_pos) - tgt2 = self.self_attn( - q.transpose(0, 1), k.transpose(0, 1), tgt.transpose(0, 1), attn_mask=self_attn_mask, need_weights=False - )[0].transpose(0, 1) - tgt = tgt + self.dropout2(tgt2) - tgt = self.norm2(tgt) - - # cross attention - tgt2 = self.cross_attn( - self.with_pos_embed(tgt, query_pos), - self.with_pos_embed(src, src_pos_embed), - src, - src_padding_mask, - ) - tgt = tgt + self.dropout1(tgt2) - tgt = self.norm1(tgt) - - # ffn - tgt2 = self.linear2(self.dropout3(self.activation(self.linear1(tgt)))) - tgt = tgt + self.dropout4(tgt2) - tgt = self.norm3(tgt) - - return tgt - - def forward( - self, - tgt, - query_pos, - src, - src_pos_embed, - src_padding_mask=None, - self_attn_mask=None, - ): - if self.norm_type == "pre_norm": - return self.forward_pre(tgt, query_pos, src, src_pos_embed, src_padding_mask, self_attn_mask) - if self.norm_type == "post_norm": - return self.forward_post(tgt, query_pos, src, src_pos_embed, src_padding_mask, self_attn_mask) - - -class GlobalDecoder(nn.Module): - def __init__( - self, - decoder_layer, - num_layers, - return_intermediate=False, - look_forward_twice=False, - use_checkpoint=False, - d_model=256, - norm_type="post_norm", - ): - super().__init__() - self.layers = _get_clones(decoder_layer, num_layers) - self.num_layers = num_layers - self.return_intermediate = return_intermediate - self.look_forward_twice = look_forward_twice - self.use_checkpoint = use_checkpoint - # hack implementation for iterative bounding box refinement and two-stage Deformable DETR - self.bbox_embed = None - self.class_embed = None - - self.norm_type = norm_type - if self.norm_type == "pre_norm": - self.final_layer_norm = nn.LayerNorm(d_model) - else: - self.final_layer_norm = None - - def _reset_parameters(self): - # stolen from Swin Transformer - def _init_weights(m): - if isinstance(m, nn.Linear): - nn.init.trunc_normal_(m.weight, std=0.02) - if isinstance(m, nn.Linear) and m.bias is not None: - nn.init.constant_(m.bias, 0) - elif isinstance(m, nn.LayerNorm): - nn.init.constant_(m.bias, 0) - nn.init.constant_(m.weight, 1.0) - - self.apply(_init_weights) - - def forward( - self, - tgt, - reference_points, - src, - src_pos_embed, - src_spatial_shapes, - src_level_start_index, - src_valid_ratios, - query_pos=None, - src_padding_mask=None, - self_attn_mask=None, - max_shape=None, - ): - output = tgt - - intermediate = [] - intermediate_reference_points = [] - for lid, layer in enumerate(self.layers): - if self.use_checkpoint: - output = checkpoint.checkpoint( - layer, - output, - query_pos, - src, - src_pos_embed, - src_padding_mask, - self_attn_mask, - ) - else: - output = layer( - output, - query_pos, - src, - src_pos_embed, - src_padding_mask, - self_attn_mask, - ) - - if self.final_layer_norm is not None: - output_after_norm = self.final_layer_norm(output) - else: - output_after_norm = output - - # hack implementation for iterative bounding box refinement - if self.bbox_embed is not None: - tmp = self.bbox_embed[lid](output_after_norm) - if reference_points.shape[-1] == 4: - new_reference_points = tmp + inverse_sigmoid(reference_points) - new_reference_points = new_reference_points.sigmoid() - else: - assert reference_points.shape[-1] == 2 - new_reference_points = tmp - new_reference_points[..., :2] = tmp[..., :2] + inverse_sigmoid(reference_points) - new_reference_points = new_reference_points.sigmoid() - reference_points = new_reference_points.detach() - - if self.return_intermediate: - intermediate.append(output_after_norm) - intermediate_reference_points.append( - new_reference_points if self.look_forward_twice else reference_points - ) - - if self.return_intermediate: - return torch.stack(intermediate), torch.stack(intermediate_reference_points) - - return output_after_norm, reference_points - - -def build_global_ape_decoder(args): - decoder_layer = GlobalDecoderLayer( - d_model=args.hidden_dim, - d_ffn=args.dim_feedforward, - dropout=args.dropout, - activation="relu", - n_heads=args.nheads, - norm_type=args.norm_type, - ) - decoder = GlobalDecoder( - decoder_layer, - num_layers=args.dec_layers, - return_intermediate=True, - look_forward_twice=args.look_forward_twice, - use_checkpoint=args.decoder_use_checkpoint, - d_model=args.hidden_dim, - norm_type=args.norm_type, - ) - return decoder diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/detection/models/global_rpe_decomp_decoder.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/detection/models/global_rpe_decomp_decoder.py deleted file mode 100644 index 667c6a10b011a423d58f09bfd79ca3bd669adb2b..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/detection/models/global_rpe_decomp_decoder.py +++ /dev/null @@ -1,443 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -# ------------------------------------------------------------------------ -# Plain-DETR -# Copyright (c) 2023 Xi'an Jiaotong University & Microsoft Research Asia. -# Licensed under The MIT License [see LICENSE for details] -# ------------------------------------------------------------------------ -# -*- coding: utf-8 -*- -import numpy as np -import torch -import torch.nn as nn -import torch.utils.checkpoint as checkpoint - -from ..util.box_ops import box_xyxy_to_cxcywh, delta2bbox -from ..util.misc import _get_activation_fn, _get_clones, inverse_sigmoid - - -class GlobalCrossAttention(nn.Module): - def __init__( - self, - dim, - num_heads, - qkv_bias=True, - qk_scale=None, - attn_drop=0.0, - proj_drop=0.0, - rpe_hidden_dim=512, - rpe_type="linear", - feature_stride=16, - reparam=False, - ): - super().__init__() - self.dim = dim - self.num_heads = num_heads - head_dim = dim // num_heads - self.scale = qk_scale or head_dim**-0.5 - self.rpe_type = rpe_type - self.feature_stride = feature_stride - self.reparam = reparam - - self.cpb_mlp1 = self.build_cpb_mlp(2, rpe_hidden_dim, num_heads) - self.cpb_mlp2 = self.build_cpb_mlp(2, rpe_hidden_dim, num_heads) - self.q = nn.Linear(dim, dim, bias=qkv_bias) - self.k = nn.Linear(dim, dim, bias=qkv_bias) - self.v = nn.Linear(dim, dim, bias=qkv_bias) - self.attn_drop = nn.Dropout(attn_drop) - self.proj = nn.Linear(dim, dim) - self.proj_drop = nn.Dropout(proj_drop) - - self.softmax = nn.Softmax(dim=-1) - - def build_cpb_mlp(self, in_dim, hidden_dim, out_dim): - cpb_mlp = nn.Sequential( - nn.Linear(in_dim, hidden_dim, bias=True), nn.ReLU(inplace=True), nn.Linear(hidden_dim, out_dim, bias=False) - ) - return cpb_mlp - - def forward( - self, - query, - reference_points, - k_input_flatten, - v_input_flatten, - input_spatial_shapes, - input_padding_mask=None, - ): - assert len(input_spatial_shapes) == 1, "This is designed for single-scale decoder." - h, w = input_spatial_shapes[0] - stride = self.feature_stride - - ref_pts = torch.cat( - [ - reference_points[:, :, :, :2] - reference_points[:, :, :, 2:] / 2, - reference_points[:, :, :, :2] + reference_points[:, :, :, 2:] / 2, - ], - dim=-1, - ) # B, nQ, 1, 4 - if not self.reparam: - ref_pts[..., 0::2] *= w * stride - ref_pts[..., 1::2] *= h * stride - pos_x = ( - torch.linspace(0.5, w - 0.5, w, dtype=torch.float32, device=ref_pts.device)[None, None, :, None] * stride - ) # 1, 1, w, 1 - pos_y = ( - torch.linspace(0.5, h - 0.5, h, dtype=torch.float32, device=ref_pts.device)[None, None, :, None] * stride - ) # 1, 1, h, 1 - - if self.rpe_type == "abs_log8": - delta_x = ref_pts[..., 0::2] - pos_x # B, nQ, w, 2 - delta_y = ref_pts[..., 1::2] - pos_y # B, nQ, h, 2 - delta_x = torch.sign(delta_x) * torch.log2(torch.abs(delta_x) + 1.0) / np.log2(8) - delta_y = torch.sign(delta_y) * torch.log2(torch.abs(delta_y) + 1.0) / np.log2(8) - elif self.rpe_type == "linear": - delta_x = ref_pts[..., 0::2] - pos_x # B, nQ, w, 2 - delta_y = ref_pts[..., 1::2] - pos_y # B, nQ, h, 2 - else: - raise NotImplementedError - - rpe_x, rpe_y = self.cpb_mlp1(delta_x), self.cpb_mlp2(delta_y) # B, nQ, w/h, nheads - rpe = (rpe_x[:, :, None] + rpe_y[:, :, :, None]).flatten(2, 3) # B, nQ, h, w, nheads -> B, nQ, h*w, nheads - rpe = rpe.permute(0, 3, 1, 2) - - B_, N, C = k_input_flatten.shape - k = self.k(k_input_flatten).reshape(B_, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3) - v = self.v(v_input_flatten).reshape(B_, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3) - B_, N, C = query.shape - q = self.q(query).reshape(B_, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3) - - attn_mask = rpe - if input_padding_mask is not None: - attn_mask += input_padding_mask[:, None, None] * -100 - attn_mask = attn_mask.contiguous() # to enable efficient attention - - x = torch.nn.functional.scaled_dot_product_attention( - query=q, - key=k, - value=v, - attn_mask=attn_mask, - dropout_p=self.attn_drop.p if self.training else 0, - scale=self.scale, - ) - - x = x.transpose(1, 2).reshape(B_, N, C) - x = self.proj(x) - x = self.proj_drop(x) - return x - - -class GlobalDecoderLayer(nn.Module): - def __init__( - self, - d_model=256, - d_ffn=1024, - dropout=0.1, - activation="relu", - n_heads=8, - norm_type="post_norm", - rpe_hidden_dim=512, - rpe_type="box_norm", - feature_stride=16, - reparam=False, - ): - super().__init__() - - self.norm_type = norm_type - - # global cross attention - self.cross_attn = GlobalCrossAttention( - d_model, - n_heads, - rpe_hidden_dim=rpe_hidden_dim, - rpe_type=rpe_type, - feature_stride=feature_stride, - reparam=reparam, - ) - self.dropout1 = nn.Dropout(dropout) - self.norm1 = nn.LayerNorm(d_model) - - # self attention - self.self_attn = nn.MultiheadAttention(d_model, n_heads, dropout=dropout) - self.dropout2 = nn.Dropout(dropout) - self.norm2 = nn.LayerNorm(d_model) - - # ffn - self.linear1 = nn.Linear(d_model, d_ffn) - self.activation = _get_activation_fn(activation) - self.dropout3 = nn.Dropout(dropout) - self.linear2 = nn.Linear(d_ffn, d_model) - self.dropout4 = nn.Dropout(dropout) - self.norm3 = nn.LayerNorm(d_model) - - @staticmethod - def with_pos_embed(tensor, pos): - return tensor if pos is None else tensor + pos - - def forward_pre( - self, - tgt, - query_pos, - reference_points, - src, - src_pos_embed, - src_spatial_shapes, - src_padding_mask=None, - self_attn_mask=None, - ): - # self attention - tgt2 = self.norm2(tgt) - q = k = self.with_pos_embed(tgt2, query_pos) - tgt2 = self.self_attn( - q.transpose(0, 1), k.transpose(0, 1), tgt2.transpose(0, 1), attn_mask=self_attn_mask, need_weights=False - )[0].transpose(0, 1) - tgt = tgt + self.dropout2(tgt2) - - # global cross attention - tgt2 = self.norm1(tgt) - tgt2 = self.cross_attn( - self.with_pos_embed(tgt2, query_pos), - reference_points, - self.with_pos_embed(src, src_pos_embed), - src, - src_spatial_shapes, - src_padding_mask, - ) - tgt = tgt + self.dropout1(tgt2) - - # ffn - tgt2 = self.norm3(tgt) - tgt2 = self.linear2(self.dropout3(self.activation(self.linear1(tgt2)))) - tgt = tgt + self.dropout4(tgt2) - - return tgt - - def forward_post( - self, - tgt, - query_pos, - reference_points, - src, - src_pos_embed, - src_spatial_shapes, - src_padding_mask=None, - self_attn_mask=None, - ): - # self attention - q = k = self.with_pos_embed(tgt, query_pos) - tgt2 = self.self_attn( - q.transpose(0, 1), k.transpose(0, 1), tgt.transpose(0, 1), attn_mask=self_attn_mask, need_weights=False - )[0].transpose(0, 1) - tgt = tgt + self.dropout2(tgt2) - tgt = self.norm2(tgt) - - # cross attention - tgt2 = self.cross_attn( - self.with_pos_embed(tgt, query_pos), - reference_points, - self.with_pos_embed(src, src_pos_embed), - src, - src_spatial_shapes, - src_padding_mask, - ) - tgt = tgt + self.dropout1(tgt2) - tgt = self.norm1(tgt) - - # ffn - tgt2 = self.linear2(self.dropout3(self.activation(self.linear1(tgt)))) - tgt = tgt + self.dropout4(tgt2) - tgt = self.norm3(tgt) - - return tgt - - def forward( - self, - tgt, - query_pos, - reference_points, - src, - src_pos_embed, - src_spatial_shapes, - src_padding_mask=None, - self_attn_mask=None, - ): - if self.norm_type == "pre_norm": - return self.forward_pre( - tgt, - query_pos, - reference_points, - src, - src_pos_embed, - src_spatial_shapes, - src_padding_mask, - self_attn_mask, - ) - if self.norm_type == "post_norm": - return self.forward_post( - tgt, - query_pos, - reference_points, - src, - src_pos_embed, - src_spatial_shapes, - src_padding_mask, - self_attn_mask, - ) - - -class GlobalDecoder(nn.Module): - def __init__( - self, - decoder_layer, - num_layers, - return_intermediate=False, - look_forward_twice=False, - use_checkpoint=False, - d_model=256, - norm_type="post_norm", - reparam=False, - ): - super().__init__() - self.layers = _get_clones(decoder_layer, num_layers) - self.num_layers = num_layers - self.return_intermediate = return_intermediate - self.look_forward_twice = look_forward_twice - self.use_checkpoint = use_checkpoint - # hack implementation for iterative bounding box refinement and two-stage Deformable DETR - self.bbox_embed = None - self.class_embed = None - self.reparam = reparam - - self.norm_type = norm_type - if self.norm_type == "pre_norm": - self.final_layer_norm = nn.LayerNorm(d_model) - else: - self.final_layer_norm = None - - def _reset_parameters(self): - # stolen from Swin Transformer - def _init_weights(m): - if isinstance(m, nn.Linear): - nn.init.trunc_normal_(m.weight, std=0.02) - if isinstance(m, nn.Linear) and m.bias is not None: - nn.init.constant_(m.bias, 0) - elif isinstance(m, nn.LayerNorm): - nn.init.constant_(m.bias, 0) - nn.init.constant_(m.weight, 1.0) - - self.apply(_init_weights) - - def forward( - self, - tgt, - reference_points, - src, - src_pos_embed, - src_spatial_shapes, - src_level_start_index, - src_valid_ratios, - query_pos=None, - src_padding_mask=None, - self_attn_mask=None, - max_shape=None, - ): - output = tgt - - intermediate = [] - intermediate_reference_points = [] - for lid, layer in enumerate(self.layers): - if self.reparam: - reference_points_input = reference_points[:, :, None] - else: - if reference_points.shape[-1] == 4: - reference_points_input = ( - reference_points[:, :, None] * torch.cat([src_valid_ratios, src_valid_ratios], -1)[:, None] - ) - else: - assert reference_points.shape[-1] == 2 - reference_points_input = reference_points[:, :, None] * src_valid_ratios[:, None] - if self.use_checkpoint: - output = checkpoint.checkpoint( - layer, - output, - query_pos, - reference_points_input, - src, - src_pos_embed, - src_spatial_shapes, - src_padding_mask, - self_attn_mask, - ) - else: - output = layer( - output, - query_pos, - reference_points_input, - src, - src_pos_embed, - src_spatial_shapes, - src_padding_mask, - self_attn_mask, - ) - - if self.final_layer_norm is not None: - output_after_norm = self.final_layer_norm(output) - else: - output_after_norm = output - - # hack implementation for iterative bounding box refinement - if self.bbox_embed is not None: - tmp = self.bbox_embed[lid](output_after_norm) - if reference_points.shape[-1] == 4: - if self.reparam: - new_reference_points = box_xyxy_to_cxcywh(delta2bbox(reference_points, tmp, max_shape)) - else: - new_reference_points = tmp + inverse_sigmoid(reference_points) - new_reference_points = new_reference_points.sigmoid() - else: - if self.reparam: - raise NotImplementedError - assert reference_points.shape[-1] == 2 - new_reference_points = tmp - new_reference_points[..., :2] = tmp[..., :2] + inverse_sigmoid(reference_points) - new_reference_points = new_reference_points.sigmoid() - reference_points = new_reference_points.detach() - - if self.return_intermediate: - intermediate.append(output_after_norm) - intermediate_reference_points.append( - new_reference_points if self.look_forward_twice else reference_points - ) - - if self.return_intermediate: - return torch.stack(intermediate), torch.stack(intermediate_reference_points) - - return output_after_norm, reference_points - - -def build_global_rpe_decomp_decoder(args): - decoder_layer = GlobalDecoderLayer( - d_model=args.hidden_dim, - d_ffn=args.dim_feedforward, - dropout=args.dropout, - activation="relu", - n_heads=args.nheads, - norm_type=args.norm_type, - rpe_hidden_dim=args.decoder_rpe_hidden_dim, - rpe_type=args.decoder_rpe_type, - feature_stride=args.proposal_in_stride, - reparam=args.reparam, - ) - decoder = GlobalDecoder( - decoder_layer, - num_layers=args.dec_layers, - return_intermediate=True, - look_forward_twice=args.look_forward_twice, - use_checkpoint=args.decoder_use_checkpoint, - d_model=args.hidden_dim, - norm_type=args.norm_type, - reparam=args.reparam, - ) - return decoder diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/detection/models/position_encoding.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/detection/models/position_encoding.py deleted file mode 100644 index 53b2c03085cbcaa3db10ee680d2fee2b129ec426..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/detection/models/position_encoding.py +++ /dev/null @@ -1,130 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -# ------------------------------------------------------------------------ -# Plain-DETR -# Copyright (c) 2023 Xi'an Jiaotong University & Microsoft Research Asia. -# Licensed under The MIT License [see LICENSE for details] -# ------------------------------------------------------------------------ -# Deformable DETR -# Copyright (c) 2020 SenseTime. All Rights Reserved. -# Licensed under the Apache License, Version 2.0 [see LICENSE for details] -# ------------------------------------------------------------------------ -# Modified from DETR (https://github.com/facebookresearch/detr) -# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved -# ------------------------------------------------------------------------ - -""" -Various positional encodings for the transformer. -""" -import math -from enum import Enum - -import torch -from torch import nn - -from ..util.misc import NestedTensor - - -class PositionEncoding(Enum): - LEARNED = "learned" - SINE = "sine" - SINE_UNNORM = "sine_unnorm" - - -class PositionEmbeddingSine(nn.Module): - """ - This is a more standard version of the position embedding, very similar to the one - used by the Attention is all you need paper, generalized to work on images. - """ - - def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None): - super().__init__() - self.num_pos_feats = num_pos_feats - self.temperature = temperature - self.normalize = normalize - if scale is not None and normalize is False: - raise ValueError("normalize should be True if scale is passed") - if scale is None: - scale = 2 * math.pi - self.scale = scale - - def forward(self, tensor_list: NestedTensor): - x = tensor_list.tensors - mask = tensor_list.mask - assert mask is not None - not_mask = ~mask - y_embed = not_mask.cumsum(1, dtype=torch.float32) - x_embed = not_mask.cumsum(2, dtype=torch.float32) - if self.normalize: - eps = 1e-6 - y_embed = (y_embed - 0.5) / (y_embed[:, -1:, :] + eps) * self.scale - x_embed = (x_embed - 0.5) / (x_embed[:, :, -1:] + eps) * self.scale - else: - y_embed = (y_embed - 0.5) * self.scale - x_embed = (x_embed - 0.5) * self.scale - - dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device) - dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats) - - pos_x = x_embed[:, :, :, None] / dim_t - pos_y = y_embed[:, :, :, None] / dim_t - pos_x = torch.stack((pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4).flatten(3) - pos_y = torch.stack((pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4).flatten(3) - pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2) - return pos - - -class PositionEmbeddingLearned(nn.Module): - """ - Absolute pos embedding, learned. - """ - - def __init__(self, num_pos_feats=256): - super().__init__() - self.row_embed = nn.Embedding(50, num_pos_feats) - self.col_embed = nn.Embedding(50, num_pos_feats) - self.reset_parameters() - - def reset_parameters(self): - nn.init.uniform_(self.row_embed.weight) - nn.init.uniform_(self.col_embed.weight) - - def forward(self, tensor_list: NestedTensor): - x = tensor_list.tensors - h, w = x.shape[-2:] - i = torch.arange(w, device=x.device) - j = torch.arange(h, device=x.device) - x_emb = self.col_embed(i) - y_emb = self.row_embed(j) - pos = ( - torch.cat( - [ - x_emb.unsqueeze(0).repeat(h, 1, 1), - y_emb.unsqueeze(1).repeat(1, w, 1), - ], - dim=-1, - ) - .permute(2, 0, 1) - .unsqueeze(0) - .repeat(x.shape[0], 1, 1, 1) - ) - return pos - - -def build_position_encoding(args): - N_steps = args.hidden_dim // 2 - if args.position_embedding == PositionEncoding.SINE: # also called v2 - # TODO find a better way of exposing other arguments - position_embedding = PositionEmbeddingSine(N_steps, normalize=True) - elif args.position_embedding == PositionEncoding.LEARNED: # also called v3 - position_embedding = PositionEmbeddingLearned(N_steps) - elif args.position_embedding == PositionEncoding.SINE_UNNORM: # also called v4 - position_embedding = PositionEmbeddingSine(N_steps, normalize=False) - else: - raise ValueError(f"not supported {args.position_embedding}") - position_embedding = nn.ModuleList([position_embedding for _ in range(args.num_feature_levels)]) - - return position_embedding diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/detection/models/transformer.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/detection/models/transformer.py deleted file mode 100644 index 7684e86533d7f2b7694039cae29752481b4f8541..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/detection/models/transformer.py +++ /dev/null @@ -1,432 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -# ------------------------------------------------------------------------ -# Deformable DETR -# Copyright (c) 2020 SenseTime. All Rights Reserved. -# Licensed under the Apache License, Version 2.0 [see LICENSE for details] -# ------------------------------------------------------------------------ -# Modified from DETR (https://github.com/facebookresearch/detr) -# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved -# ------------------------------------------------------------------------ - -import math - -import torch -import torch.nn.functional as F -from torch import nn -from torch.nn.init import constant_, normal_, xavier_uniform_ - -from ..util.box_ops import box_xyxy_to_cxcywh, delta2bbox -from .global_ape_decoder import build_global_ape_decoder -from .global_rpe_decomp_decoder import build_global_rpe_decomp_decoder -from .transformer_encoder import TransformerEncoder, TransformerEncoderLayer -from .utils import LayerNorm2D - - -class Transformer(nn.Module): - def __init__( - self, - d_model=256, - nhead=8, - num_feature_levels=4, - two_stage=False, - two_stage_num_proposals=300, - mixed_selection=False, - norm_type="post_norm", - decoder_type="deform", - proposal_feature_levels=1, - proposal_in_stride=16, - proposal_tgt_strides=[8, 16, 32, 64], - proposal_min_size=50, - args=None, - # transformer_encoder - add_transformer_encoder=False, - dim_feedforward=2048, - dropout=0.1, - activation="relu", - normalize_before=False, - num_encoder_layers=6, - ): - super().__init__() - - self.d_model = d_model - self.nhead = nhead - self.two_stage = two_stage - self.two_stage_num_proposals = two_stage_num_proposals - assert norm_type in ["pre_norm", "post_norm"], f"expected norm type is pre_norm or post_norm, get {norm_type}" - - if decoder_type == "global_ape": - self.decoder = build_global_ape_decoder(args) - elif decoder_type == "global_rpe_decomp": - self.decoder = build_global_rpe_decomp_decoder(args) - else: - raise NotImplementedError - - self.level_embed = nn.Parameter(torch.Tensor(num_feature_levels, d_model)) - - if two_stage: - self.enc_output = nn.Linear(d_model, d_model) - self.enc_output_norm = nn.LayerNorm(d_model) - self.pos_trans = nn.Linear(d_model * 2, d_model * 2) - self.pos_trans_norm = nn.LayerNorm(d_model * 2) - else: - self.reference_points = nn.Linear(d_model, 2) - - self.mixed_selection = mixed_selection - self.proposal_feature_levels = proposal_feature_levels - self.proposal_tgt_strides = proposal_tgt_strides - self.proposal_min_size = proposal_min_size - if two_stage and proposal_feature_levels > 1: - assert len(proposal_tgt_strides) == proposal_feature_levels - - self.proposal_in_stride = proposal_in_stride - self.enc_output_proj = nn.ModuleList([]) - for stride in proposal_tgt_strides: - if stride == proposal_in_stride: - self.enc_output_proj.append(nn.Identity()) - elif stride > proposal_in_stride: - scale = int(math.log2(stride / proposal_in_stride)) - layers = [] - for _ in range(scale - 1): - layers += [ - nn.Conv2d(d_model, d_model, kernel_size=2, stride=2), - LayerNorm2D(d_model), - nn.GELU(), - ] - layers.append(nn.Conv2d(d_model, d_model, kernel_size=2, stride=2)) - self.enc_output_proj.append(nn.Sequential(*layers)) - else: - scale = int(math.log2(proposal_in_stride / stride)) - layers = [] - for _ in range(scale - 1): - layers += [ - nn.ConvTranspose2d(d_model, d_model, kernel_size=2, stride=2), - LayerNorm2D(d_model), - nn.GELU(), - ] - layers.append(nn.ConvTranspose2d(d_model, d_model, kernel_size=2, stride=2)) - self.enc_output_proj.append(nn.Sequential(*layers)) - - # ENCODER TRANSFORMER - self.encoder = None - if add_transformer_encoder: - encoder_layer = TransformerEncoderLayer( - d_model, - nhead, - dim_feedforward, - dropout, - activation, - normalize_before, - ) - encoder_norm = nn.LayerNorm(d_model) if normalize_before else None - self.encoder = TransformerEncoder(encoder_layer, num_encoder_layers, encoder_norm) - - self._reset_parameters() - - def _reset_parameters(self): - for p in self.parameters(): - if p.dim() > 1: - nn.init.xavier_uniform_(p) - if not self.two_stage: - xavier_uniform_(self.reference_points.weight.data, gain=1.0) - constant_(self.reference_points.bias.data, 0.0) - normal_(self.level_embed) - - if hasattr(self.decoder, "_reset_parameters"): - self.decoder._reset_parameters() - - def get_proposal_pos_embed(self, proposals): - num_pos_feats = self.d_model // 2 - temperature = 10000 - scale = 2 * math.pi - - dim_t = torch.arange(num_pos_feats, dtype=torch.float32, device=proposals.device) - dim_t = temperature ** (2 * (dim_t // 2) / num_pos_feats) - # N, L, 4 - proposals = proposals * scale - # N, L, 4, 128 - pos = proposals[:, :, :, None] / dim_t - # N, L, 4, 64, 2 - pos = torch.stack((pos[:, :, :, 0::2].sin(), pos[:, :, :, 1::2].cos()), dim=4).flatten(2) - return pos - - def gen_encoder_output_proposals(self, memory, memory_padding_mask, spatial_shapes): - if self.proposal_feature_levels > 1: - memory, memory_padding_mask, spatial_shapes = self.expand_encoder_output( - memory, memory_padding_mask, spatial_shapes - ) - N_, S_, C_ = memory.shape - # base_scale = 4.0 - proposals = [] - _cur = 0 - for lvl, (H_, W_) in enumerate(spatial_shapes): - mask_flatten_ = memory_padding_mask[:, _cur : (_cur + H_ * W_)].view(N_, H_, W_, 1) - valid_H = torch.sum(~mask_flatten_[:, :, 0, 0], 1) - valid_W = torch.sum(~mask_flatten_[:, 0, :, 0], 1) - - grid_y, grid_x = torch.meshgrid( - torch.linspace(0, H_ - 1, H_, dtype=torch.float32, device=memory.device), - torch.linspace(0, W_ - 1, W_, dtype=torch.float32, device=memory.device), - ) - grid = torch.cat([grid_x.unsqueeze(-1), grid_y.unsqueeze(-1)], -1) - - scale = torch.cat([valid_W.unsqueeze(-1), valid_H.unsqueeze(-1)], 1).view(N_, 1, 1, 2) - grid = (grid.unsqueeze(0).expand(N_, -1, -1, -1) + 0.5) / scale - wh = torch.ones_like(grid) * 0.05 * (2.0**lvl) - proposal = torch.cat((grid, wh), -1).view(N_, -1, 4) - proposals.append(proposal) - _cur += H_ * W_ - output_proposals = torch.cat(proposals, 1) - output_proposals_valid = ((output_proposals > 0.01) & (output_proposals < 0.99)).all(-1, keepdim=True) - output_proposals = torch.log(output_proposals / (1 - output_proposals)) - output_proposals = output_proposals.masked_fill(memory_padding_mask.unsqueeze(-1), float("inf")) - output_proposals = output_proposals.masked_fill(~output_proposals_valid, float("inf")) - - output_memory = memory - output_memory = output_memory.masked_fill(memory_padding_mask.unsqueeze(-1), float(0)) - output_memory = output_memory.masked_fill(~output_proposals_valid, float(0)) - output_memory = self.enc_output_norm(self.enc_output(output_memory)) - - max_shape = None - return output_memory, output_proposals, max_shape - - def get_valid_ratio(self, mask): - _, H, W = mask.shape - valid_H = torch.sum(~mask[:, :, 0], 1) - valid_W = torch.sum(~mask[:, 0, :], 1) - valid_ratio_h = valid_H.float() / H - valid_ratio_w = valid_W.float() / W - valid_ratio = torch.stack([valid_ratio_w, valid_ratio_h], -1) - return valid_ratio - - def expand_encoder_output(self, memory, memory_padding_mask, spatial_shapes): - assert len(spatial_shapes) == 1, f"Get encoder output of shape {spatial_shapes}, not sure how to expand" - - bs, _, c = memory.shape - h, w = spatial_shapes[0] - - _out_memory = memory.view(bs, h, w, c).permute(0, 3, 1, 2) - _out_memory_padding_mask = memory_padding_mask.view(bs, h, w) - - out_memory, out_memory_padding_mask, out_spatial_shapes = [], [], [] - for i in range(self.proposal_feature_levels): - mem = self.enc_output_proj[i](_out_memory) - mask = F.interpolate(_out_memory_padding_mask[None].float(), size=mem.shape[-2:]).to(torch.bool) - - out_memory.append(mem) - out_memory_padding_mask.append(mask.squeeze(0)) - out_spatial_shapes.append(mem.shape[-2:]) - - out_memory = torch.cat([mem.flatten(2).transpose(1, 2) for mem in out_memory], dim=1) - out_memory_padding_mask = torch.cat([mask.flatten(1) for mask in out_memory_padding_mask], dim=1) - return out_memory, out_memory_padding_mask, out_spatial_shapes - - def get_reference_points(self, memory, mask_flatten, spatial_shapes): - output_memory, output_proposals, max_shape = self.gen_encoder_output_proposals( - memory, mask_flatten, spatial_shapes - ) - - # hack implementation for two-stage Deformable DETR - enc_outputs_class = self.decoder.class_embed[self.decoder.num_layers](output_memory) - enc_outputs_delta = None - enc_outputs_coord_unact = self.decoder.bbox_embed[self.decoder.num_layers](output_memory) + output_proposals - - topk = self.two_stage_num_proposals - topk_proposals = torch.topk(enc_outputs_class[..., 0], topk, dim=1)[1] - topk_coords_unact = torch.gather(enc_outputs_coord_unact, 1, topk_proposals.unsqueeze(-1).repeat(1, 1, 4)) - topk_coords_unact = topk_coords_unact.detach() - reference_points = topk_coords_unact.sigmoid() - return ( - reference_points, - max_shape, - enc_outputs_class, - enc_outputs_coord_unact, - enc_outputs_delta, - output_proposals, - ) - - def forward(self, srcs, masks, pos_embeds, query_embed=None, self_attn_mask=None): - # TODO: we may remove this loop as we only have one feature level - # prepare input for encoder - src_flatten = [] - mask_flatten = [] - lvl_pos_embed_flatten = [] - spatial_shapes = [] - for lvl, (src, mask, pos_embed) in enumerate(zip(srcs, masks, pos_embeds)): - bs, c, h, w = src.shape - spatial_shape = (h, w) - spatial_shapes.append(spatial_shape) - src = src.flatten(2).transpose(1, 2) - mask = mask.flatten(1) - pos_embed = pos_embed.flatten(2).transpose(1, 2) - lvl_pos_embed = pos_embed + self.level_embed[lvl].view(1, 1, -1) - lvl_pos_embed_flatten.append(lvl_pos_embed) - src_flatten.append(src) - mask_flatten.append(mask) - src_flatten = torch.cat(src_flatten, 1) - mask_flatten = torch.cat(mask_flatten, 1) - lvl_pos_embed_flatten = torch.cat(lvl_pos_embed_flatten, 1) - level_start_index = None # not used so far - valid_ratios = torch.stack([self.get_valid_ratio(m) for m in masks], 1) - - if self.encoder is not None: - memory = self.encoder(src_flatten, src_key_padding_mask=mask_flatten, pos=lvl_pos_embed_flatten) - else: - memory = src_flatten - - # prepare input for decoder - bs, _, c = memory.shape - if self.two_stage: - ( - reference_points, - max_shape, - enc_outputs_class, - enc_outputs_coord_unact, - enc_outputs_delta, - output_proposals, - ) = self.get_reference_points(memory, mask_flatten, spatial_shapes) - init_reference_out = reference_points - pos_trans_out = torch.zeros((bs, self.two_stage_num_proposals, 2 * c), device=init_reference_out.device) - pos_trans_out = self.pos_trans_norm(self.pos_trans(self.get_proposal_pos_embed(reference_points))) - - if not self.mixed_selection: - query_embed, tgt = torch.split(pos_trans_out, c, dim=2) - else: - # query_embed here is the content embed for deformable DETR - tgt = query_embed.unsqueeze(0).expand(bs, -1, -1) - query_embed, _ = torch.split(pos_trans_out, c, dim=2) - else: - query_embed, tgt = torch.split(query_embed, c, dim=1) - query_embed = query_embed.unsqueeze(0).expand(bs, -1, -1) - tgt = tgt.unsqueeze(0).expand(bs, -1, -1) - reference_points = self.reference_points(query_embed).sigmoid() - init_reference_out = reference_points - max_shape = None - - # decoder - hs, inter_references = self.decoder( - tgt, - reference_points, - memory, - lvl_pos_embed_flatten, - spatial_shapes, - level_start_index, - valid_ratios, - query_embed, - mask_flatten, - self_attn_mask, - max_shape, - ) - - inter_references_out = inter_references - if self.two_stage: - return ( - hs, - init_reference_out, - inter_references_out, - enc_outputs_class, - enc_outputs_coord_unact, - enc_outputs_delta, - output_proposals, - max_shape, - ) - return hs, init_reference_out, inter_references_out, None, None, None, None, None - - -class TransformerReParam(Transformer): - def gen_encoder_output_proposals(self, memory, memory_padding_mask, spatial_shapes): - if self.proposal_feature_levels > 1: - memory, memory_padding_mask, spatial_shapes = self.expand_encoder_output( - memory, memory_padding_mask, spatial_shapes - ) - N_, S_, C_ = memory.shape - # base_scale = 4.0 - proposals = [] - _cur = 0 - for lvl, (H_, W_) in enumerate(spatial_shapes): - stride = self.proposal_tgt_strides[lvl] - - grid_y, grid_x = torch.meshgrid( - torch.linspace(0, H_ - 1, H_, dtype=torch.float32, device=memory.device), - torch.linspace(0, W_ - 1, W_, dtype=torch.float32, device=memory.device), - ) - grid = torch.cat([grid_x.unsqueeze(-1), grid_y.unsqueeze(-1)], -1) - grid = (grid.unsqueeze(0).expand(N_, -1, -1, -1) + 0.5) * stride - wh = torch.ones_like(grid) * self.proposal_min_size * (2.0**lvl) - proposal = torch.cat((grid, wh), -1).view(N_, -1, 4) - proposals.append(proposal) - _cur += H_ * W_ - output_proposals = torch.cat(proposals, 1) - - H_, W_ = spatial_shapes[0] - stride = self.proposal_tgt_strides[0] - mask_flatten_ = memory_padding_mask[:, : H_ * W_].view(N_, H_, W_, 1) - valid_H = torch.sum(~mask_flatten_[:, :, 0, 0], 1, keepdim=True) * stride - valid_W = torch.sum(~mask_flatten_[:, 0, :, 0], 1, keepdim=True) * stride - img_size = torch.cat([valid_W, valid_H, valid_W, valid_H], dim=-1) - img_size = img_size.unsqueeze(1) # [BS, 1, 4] - - output_proposals_valid = ((output_proposals > 0.01 * img_size) & (output_proposals < 0.99 * img_size)).all( - -1, keepdim=True - ) - output_proposals = output_proposals.masked_fill( - memory_padding_mask.unsqueeze(-1).repeat(1, 1, 1), max(H_, W_) * stride - ) - output_proposals = output_proposals.masked_fill(~output_proposals_valid, max(H_, W_) * stride) - - output_memory = memory - output_memory = output_memory.masked_fill(memory_padding_mask.unsqueeze(-1), float(0)) - output_memory = output_memory.masked_fill(~output_proposals_valid, float(0)) - output_memory = self.enc_output_norm(self.enc_output(output_memory)) - - max_shape = (valid_H[:, None, :], valid_W[:, None, :]) - return output_memory, output_proposals, max_shape - - def get_reference_points(self, memory, mask_flatten, spatial_shapes): - output_memory, output_proposals, max_shape = self.gen_encoder_output_proposals( - memory, mask_flatten, spatial_shapes - ) - - # hack implementation for two-stage Deformable DETR - enc_outputs_class = self.decoder.class_embed[self.decoder.num_layers](output_memory) - enc_outputs_delta = self.decoder.bbox_embed[self.decoder.num_layers](output_memory) - enc_outputs_coord_unact = box_xyxy_to_cxcywh(delta2bbox(output_proposals, enc_outputs_delta, max_shape)) - - topk = self.two_stage_num_proposals - topk_proposals = torch.topk(enc_outputs_class[..., 0], topk, dim=1)[1] - topk_coords_unact = torch.gather(enc_outputs_coord_unact, 1, topk_proposals.unsqueeze(-1).repeat(1, 1, 4)) - topk_coords_unact = topk_coords_unact.detach() - reference_points = topk_coords_unact - return ( - reference_points, - max_shape, - enc_outputs_class, - enc_outputs_coord_unact, - enc_outputs_delta, - output_proposals, - ) - - -def build_transformer(args): - model_class = Transformer if (not args.reparam) else TransformerReParam - return model_class( - d_model=args.hidden_dim, - nhead=args.nheads, - num_feature_levels=args.num_feature_levels, - two_stage=args.two_stage, - two_stage_num_proposals=args.num_queries_one2one + args.num_queries_one2many, - mixed_selection=args.mixed_selection, - norm_type=args.norm_type, - decoder_type=args.decoder_type, - proposal_feature_levels=args.proposal_feature_levels, - proposal_in_stride=args.proposal_in_stride, - proposal_tgt_strides=args.proposal_tgt_strides, - args=args, - proposal_min_size=args.proposal_min_size, - # transformer_encoder - add_transformer_encoder=args.add_transformer_encoder, - num_encoder_layers=args.num_encoder_layers, - ) diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/detection/models/transformer_encoder.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/detection/models/transformer_encoder.py deleted file mode 100644 index 1ef9c2537bb7c7245e0e6c49846955370164bccb..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/detection/models/transformer_encoder.py +++ /dev/null @@ -1,114 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -# https://github.com/facebookresearch/detr/blob/main/models/transformer.py -# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved -""" -DETR Transformer class. - -Copy-paste from torch.nn.Transformer with modifications: - * positional encodings are passed in MHattention - * extra LN at the end of encoder is removed - * decoder returns a stack of activations from all decoding layers -""" -from typing import Optional - -from torch import Tensor, nn - -from ..util.misc import _get_activation_fn, _get_clones - - -class TransformerEncoder(nn.Module): - def __init__(self, encoder_layer, num_layers, norm=None): - super().__init__() - self.layers = _get_clones(encoder_layer, num_layers) - self.num_layers = num_layers - self.norm = norm - - def forward( - self, - src, - mask: Optional[Tensor] = None, - src_key_padding_mask: Optional[Tensor] = None, - pos: Optional[Tensor] = None, - ): - output = src - - for layer in self.layers: - output = layer(output, src_mask=mask, src_key_padding_mask=src_key_padding_mask, pos=pos) - - if self.norm is not None: - output = self.norm(output) - - return output - - -class TransformerEncoderLayer(nn.Module): - def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1, activation="relu", normalize_before=False): - super().__init__() - # Keeping Dropout 0 in self attention as it makes the eval 10% faster without performance change - self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=0, batch_first=True) - # Implementation of Feedforward model - self.linear1 = nn.Linear(d_model, dim_feedforward) - self.dropout = nn.Dropout(dropout) - self.linear2 = nn.Linear(dim_feedforward, d_model) - - self.norm1 = nn.LayerNorm(d_model) - self.norm2 = nn.LayerNorm(d_model) - self.dropout1 = nn.Dropout(dropout) - self.dropout2 = nn.Dropout(dropout) - - self.activation = _get_activation_fn(activation) - self.normalize_before = normalize_before - - def with_pos_embed(self, tensor, pos: Optional[Tensor]): - return tensor if pos is None else tensor + pos - - def forward_post( - self, - src, - src_mask: Optional[Tensor] = None, - src_key_padding_mask: Optional[Tensor] = None, - pos: Optional[Tensor] = None, - ): - q = k = self.with_pos_embed(src, pos) - src2 = self.self_attn( - q, k, value=src, attn_mask=src_mask, key_padding_mask=src_key_padding_mask, need_weights=False - )[0] - src = src + self.dropout1(src2) - src = self.norm1(src) - src2 = self.linear2(self.dropout(self.activation(self.linear1(src)))) - src = src + self.dropout2(src2) - src = self.norm2(src) - return src - - def forward_pre( - self, - src, - src_mask: Optional[Tensor] = None, - src_key_padding_mask: Optional[Tensor] = None, - pos: Optional[Tensor] = None, - ): - src2 = self.norm1(src) - q = k = self.with_pos_embed(src2, pos) - src2 = self.self_attn( - q, k, value=src2, attn_mask=src_mask, key_padding_mask=src_key_padding_mask, need_weights=False - )[0] - src = src + self.dropout1(src2) - src2 = self.norm2(src) - src2 = self.linear2(self.dropout(self.activation(self.linear1(src2)))) - src = src + self.dropout2(src2) - return src - - def forward( - self, - src, - src_mask: Optional[Tensor] = None, - src_key_padding_mask: Optional[Tensor] = None, - pos: Optional[Tensor] = None, - ): - if self.normalize_before: - return self.forward_pre(src, src_mask, src_key_padding_mask, pos) - return self.forward_post(src, src_mask, src_key_padding_mask, pos) diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/detection/models/utils.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/detection/models/utils.py deleted file mode 100644 index a4f8e2f3986ca1fcee43a31ee45473d6bf65040b..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/detection/models/utils.py +++ /dev/null @@ -1,21 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import torch.nn as nn - - -class LayerNorm2D(nn.Module): - def __init__(self, normalized_shape, norm_layer=nn.LayerNorm): - super().__init__() - self.ln = norm_layer(normalized_shape) if norm_layer is not None else nn.Identity() - - def forward(self, x): - """ - x: N C H W - """ - x = x.permute(0, 2, 3, 1) - x = self.ln(x) - x = x.permute(0, 3, 1, 2) - return x diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/detection/models/windows.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/detection/models/windows.py deleted file mode 100644 index 5399d235b136baa98f0c75a57289dd406cb0d83b..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/detection/models/windows.py +++ /dev/null @@ -1,82 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import math - -import numpy as np -import torch -import torch.nn.functional as F -from torchvision.transforms import v2 - -from ..util.misc import NestedTensor - - -class WindowsWrapper(torch.nn.Module): - """ - This wrapper will take an input (NestedTensor) at size (h, w) and split it - in `N = n_windows_h * n_windows_w` equally sized windows (the bottom and right windows might - be a little bit smaller), with sizes that are multiples of the patch size (as the input should be). - - Then, the input will be resized at the size of the top left window (h / n_windows_h, w / n_windows_w). - This resized input, plus the N windows, will be passed through the backbone. - Then, the features of the resized input will be resized to the original input size, while the - features of the windows will be concatenated side by side to reconstruct a feature map also - corresponding to the original image's size. - - Finally, both the features from the windows and from the resized images are stacked. - Compared to the output of the backbone of size [B, C, H, W], the output here is [B, 2 * C, H, W] - """ - - def __init__(self, backbone, n_windows_w, n_windows_h, patch_size): - # Assuming image size is divisible by patch_size - super().__init__() - self._backbone = backbone - self._n_windows_w = n_windows_w - self._n_windows_h = n_windows_h - self._patch_size = patch_size - self.strides = backbone.strides - self.num_channels = [el * 2 for el in backbone.num_channels] # resized + windows - - def forward(self, tensor_list: NestedTensor): - tensors = tensor_list.tensors - original_h, original_w = tensors.shape[2], tensors.shape[3] - # Get height and width of the windows, such that it is a multiple of the patch size - window_h = math.ceil((original_h // self._n_windows_h) / self._patch_size) * self._patch_size - window_w = math.ceil((original_w // self._n_windows_w) / self._patch_size) * self._patch_size - all_h = [window_h] * (self._n_windows_h - 1) + [original_h - window_h * (self._n_windows_h - 1)] - all_w = [window_w] * (self._n_windows_w - 1) + [original_w - window_w * (self._n_windows_w - 1)] - all_h_cumsum = [0] + list(np.cumsum(all_h)) - all_w_cumsum = [0] + list(np.cumsum(all_w)) - window_patch_features = [[0 for _ in range(self._n_windows_w)] for _ in range(self._n_windows_h)] - - for ih in range(self._n_windows_h): - for iw in range(self._n_windows_w): - window_tensor = v2.functional.crop( - tensors, top=all_h_cumsum[ih], left=all_w_cumsum[iw], height=all_h[ih], width=all_w[iw] - ) - window_mask = v2.functional.crop( - tensor_list.mask, top=all_h_cumsum[ih], left=all_w_cumsum[iw], height=all_h[ih], width=all_w[iw] - ) - window_patch_features[ih][iw] = self._backbone(NestedTensor(tensors=window_tensor, mask=window_mask))[0] - - window_tensors = torch.cat( - [ - torch.cat([el.tensors for el in window_patch_features[ih]], dim=-1) # type: ignore - for ih in range(len(window_patch_features)) - ], - dim=-2, - ) - # Also compute the global features in a "preferential" setting, of lower resolution - resized_global_tensor = v2.functional.resize(tensors, size=(window_h, window_w)) - global_features = self._backbone( - NestedTensor(tensors=resized_global_tensor, mask=tensor_list.mask) - ) # mask is not used - - concat_tensors = torch.cat( - [v2.functional.resize(global_features[0].tensors, size=window_tensors.shape[-2:]), window_tensors], dim=1 - ) - global_mask = F.interpolate(tensor_list.mask[None].float(), size=concat_tensors.shape[-2:]).to(torch.bool)[0] - out = [NestedTensor(tensors=concat_tensors, mask=global_mask)] - return out diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/detection/util/__init__.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/detection/util/__init__.py deleted file mode 100644 index 5375bc66e1ed841a7091b81a0dcf56d1993c1f87..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/detection/util/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/detection/util/box_ops.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/detection/util/box_ops.py deleted file mode 100644 index de47dbc46a6c37bae27fbe279cd0877af6fd7ec2..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/detection/util/box_ops.py +++ /dev/null @@ -1,91 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -# ------------------------------------------------------------------------ -# Plain-DETR -# Copyright (c) 2023 Xi'an Jiaotong University & Microsoft Research Asia. -# Licensed under The MIT License [see LICENSE for details] -# ------------------------------------------------------------------------ -# Deformable DETR -# Copyright (c) 2020 SenseTime. All Rights Reserved. -# Licensed under the Apache License, Version 2.0 [see LICENSE for details] -# ------------------------------------------------------------------------ -# Modified from DETR (https://github.com/facebookresearch/detr) -# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved -# ------------------------------------------------------------------------ - -""" -Utilities for bounding box manipulation and GIoU. -""" -import numpy as np -import torch - - -def box_cxcywh_to_xyxy(x): - x_c, y_c, w, h = x.unbind(-1) - b = [(x_c - 0.5 * w), (y_c - 0.5 * h), (x_c + 0.5 * w), (y_c + 0.5 * h)] - return torch.stack(b, dim=-1) - - -def box_xyxy_to_cxcywh(x): - x0, y0, x1, y1 = x.unbind(-1) - b = [(x0 + x1) / 2, (y0 + y1) / 2, (x1 - x0), (y1 - y0)] - return torch.stack(b, dim=-1) - - -def delta2bbox( - proposals, deltas, max_shape=None, wh_ratio_clip=16 / 1000, clip_border=True, add_ctr_clamp=False, ctr_clamp=32 -): - dxy = deltas[..., :2] - dwh = deltas[..., 2:] - - # Compute width/height of each roi - pxy = proposals[..., :2] - pwh = proposals[..., 2:] - - dxy_wh = pwh * dxy - - max_ratio = np.abs(np.log(wh_ratio_clip)) - if add_ctr_clamp: - dxy_wh = torch.clamp(dxy_wh, max=ctr_clamp, min=-ctr_clamp) - dwh = torch.clamp(dwh, max=max_ratio) - else: - dwh = dwh.clamp(min=-max_ratio, max=max_ratio) - - gxy = pxy + dxy_wh - gwh = pwh * dwh.exp() - x1y1 = gxy - (gwh * 0.5) - x2y2 = gxy + (gwh * 0.5) - bboxes = torch.cat([x1y1, x2y2], dim=-1) - if clip_border and max_shape is not None: - bboxes[..., 0::2].clamp_(min=0).clamp_(max=max_shape[1]) - bboxes[..., 1::2].clamp_(min=0).clamp_(max=max_shape[0]) - return bboxes - - -def bbox2delta(proposals, gt, means=(0.0, 0.0, 0.0, 0.0), stds=(1.0, 1.0, 1.0, 1.0)): - # hack for matcher - if proposals.size() != gt.size(): - proposals = proposals[:, None] - gt = gt[None] - - proposals = proposals.float() - gt = gt.float() - px, py, pw, ph = proposals.unbind(-1) - gx, gy, gw, gh = gt.unbind(-1) - - dx = (gx - px) / (pw + 0.1) - dy = (gy - py) / (ph + 0.1) - dw = torch.log(gw / (pw + 0.1)) - dh = torch.log(gh / (ph + 0.1)) - deltas = torch.stack([dx, dy, dw, dh], dim=-1) - - # avoid unnecessary sync point if not needed - if means != (0.0, 0.0, 0.0, 0.0) or stds != (1.0, 1.0, 1.0, 1.0): - means = deltas.new_tensor(means).unsqueeze(0) - stds = deltas.new_tensor(stds).unsqueeze(0) - deltas = deltas.sub_(means).div_(stds) - - return deltas diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/detection/util/misc.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/detection/util/misc.py deleted file mode 100644 index 7fd2e7e758821de7363e856d053d8305ca780813..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/detection/util/misc.py +++ /dev/null @@ -1,281 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -# ------------------------------------------------------------------------ -# Plain-DETR -# Copyright (c) 2023 Xi'an Jiaotong University & Microsoft Research Asia. -# Licensed under The MIT License [see LICENSE for details] -# ------------------------------------------------------------------------ -# Deformable DETR -# Copyright (c) 2020 SenseTime. All Rights Reserved. -# Licensed under the Apache License, Version 2.0 [see LICENSE for details] -# ------------------------------------------------------------------------ -# Modified from DETR (https://github.com/facebookresearch/detr) -# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved -# ------------------------------------------------------------------------ - -""" -Misc functions, including distributed helpers. - -Mostly copy-paste from torchvision references. -""" -import copy -from typing import List, Optional - -import dinov3.distributed as distributed -import torch -import torch.distributed as dist -import torch.nn as nn -import torch.nn.functional as F - -# needed due to empty tensor bug in pytorch and torchvision 0.5 -import torchvision -from torch import Tensor - - -def reduce_dict(input_dict, average=True): - """ - Args: - input_dict (dict): all the values will be reduced - average (bool): whether to do average or sum - Reduce the values in the dictionary from all processes so that all processes - have the averaged results. Returns a dict with the same fields as - input_dict, after reduction. - """ - world_size = distributed.get_world_size() - if world_size < 2: - return input_dict - with torch.no_grad(): - names = [] - values = [] - # sort the keys so that they are consistent across processes - for k in sorted(input_dict.keys()): - names.append(k) - values.append(input_dict[k]) - values = torch.stack(values, dim=0) - dist.all_reduce(values) - if average: - values /= world_size - reduced_dict = {k: v for k, v in zip(names, values)} - return reduced_dict - - -def collate_fn(batch): - batch = list(zip(*batch)) - batch[0] = nested_tensor_from_tensor_list(batch[0]) - return tuple(batch) - - -def _max_by_axis(the_list): - # type: (List[List[int]]) -> List[int] - maxes = the_list[0] - for sublist in the_list[1:]: - for index, item in enumerate(sublist): - maxes[index] = max(maxes[index], item) - return maxes - - -def nested_tensor_from_tensor_list(tensor_list: List[Tensor]): - # TODO make this more general - if tensor_list[0].ndim == 3: - # TODO make it support different-sized images - max_size = _max_by_axis([list(img.shape) for img in tensor_list]) - # min_size = tuple(min(s) for s in zip(*[img.shape for img in tensor_list])) - batch_shape = [len(tensor_list)] + max_size - b, c, h, w = batch_shape - dtype = tensor_list[0].dtype - device = tensor_list[0].device - tensor = torch.zeros(batch_shape, dtype=dtype, device=device) - mask = torch.ones((b, h, w), dtype=torch.bool, device=device) - for img, pad_img, m in zip(tensor_list, tensor, mask): - pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img) - m[: img.shape[1], : img.shape[2]] = False - else: - raise ValueError("not supported") - return NestedTensor(tensor, mask) - - -class NestedTensor(object): - def __init__(self, tensors, mask: Optional[Tensor]): - self.tensors = tensors - self.mask = mask - - def to(self, device, non_blocking=False): - cast_tensor = self.tensors.to(device, non_blocking=non_blocking) - mask = self.mask - if mask is not None: - assert mask is not None - cast_mask = mask.to(device, non_blocking=non_blocking) - else: - cast_mask = None - return NestedTensor(cast_tensor, cast_mask) - - def record_stream(self, *args, **kwargs): - self.tensors.record_stream(*args, **kwargs) - if self.mask is not None: - self.mask.record_stream(*args, **kwargs) - - def decompose(self): - return self.tensors, self.mask - - def __repr__(self): - return str(self.tensors) - - def __len__(self): - return len(self.tensors) - - -@torch.no_grad() -def accuracy(output, target, topk=(1,)): - """Computes the precision@k for the specified values of k""" - if target.numel() == 0: - return [torch.zeros([], device=output.device)] - maxk = max(topk) - batch_size = target.size(0) - - _, pred = output.topk(maxk, 1, True, True) - pred = pred.t() - correct = pred.eq(target.view(1, -1).expand_as(pred)) - - res = [] - for k in topk: - correct_k = correct[:k].view(-1).float().sum(0) - res.append(correct_k.mul_(100.0 / batch_size)) - return res - - -def interpolate(input, size=None, scale_factor=None, mode="nearest", align_corners=None): - # type: (Tensor, Optional[List[int]], Optional[float], str, Optional[bool]) -> Tensor - """ - Equivalent to nn.functional.interpolate, but with support for empty batch sizes. - This will eventually be supported natively by PyTorch, and this - class can go away. - """ - return torchvision.ops.misc.interpolate(input, size, scale_factor, mode, align_corners) - - -def get_total_grad_norm(parameters, norm_type=2): - parameters = list(filter(lambda p: p.grad is not None, parameters)) - norm_type = float(norm_type) - device = parameters[0].grad.device - total_norm = torch.norm( - torch.stack([torch.norm(p.grad.detach(), norm_type).to(device) for p in parameters]), - norm_type, - ) - return total_norm - - -def inverse_sigmoid(x, eps=1e-5): - x = x.clamp(min=0, max=1) - x1 = x.clamp(min=eps) - x2 = (1 - x).clamp(min=eps) - return torch.log(x1 / x2) - - -def match_name_keywords(n, name_keywords): - out = False - for b in name_keywords: - if b in n: - out = True - break - return out - - -def get_param_dict(model, args, return_name=False, use_layerwise_decay=False): - # sanity check: a variable could not match backbone_names and linear_proj_names at the same time - for n, p in model.named_parameters(): - if match_name_keywords(n, args.lr_backbone_names) and match_name_keywords(n, args.lr_linear_proj_names): - raise ValueError - - param_dicts = [ - { - "params": [ - p if not return_name else n - for n, p in model.named_parameters() - if not match_name_keywords(n, args.lr_backbone_names) - and not match_name_keywords(n, args.lr_linear_proj_names) - and not match_name_keywords(n, args.wd_norm_names) - and p.requires_grad - ], - "lr": args.lr, - "weight_decay": args.weight_decay, - }, - { - "params": [ - p if not return_name else n - for n, p in model.named_parameters() - if match_name_keywords(n, args.lr_backbone_names) - and not match_name_keywords(n, args.lr_linear_proj_names) - and not match_name_keywords(n, args.wd_norm_names) - and p.requires_grad - ], - "lr": args.lr_backbone, - "weight_decay": args.weight_decay, - }, - { - "params": [ - p if not return_name else n - for n, p in model.named_parameters() - if not match_name_keywords(n, args.lr_backbone_names) - and match_name_keywords(n, args.lr_linear_proj_names) - and not match_name_keywords(n, args.wd_norm_names) - and p.requires_grad - ], - "lr": args.lr * args.lr_linear_proj_mult, - "weight_decay": args.weight_decay, - }, - { - "params": [ - p if not return_name else n - for n, p in model.named_parameters() - if not match_name_keywords(n, args.lr_backbone_names) - and not match_name_keywords(n, args.lr_linear_proj_names) - and match_name_keywords(n, args.wd_norm_names) - and p.requires_grad - ], - "lr": args.lr, - "weight_decay": args.weight_decay * args.wd_norm_mult, - }, - { - "params": [ - p if not return_name else n - for n, p in model.named_parameters() - if match_name_keywords(n, args.lr_backbone_names) - and not match_name_keywords(n, args.lr_linear_proj_names) - and match_name_keywords(n, args.wd_norm_names) - and p.requires_grad - ], - "lr": args.lr_backbone, - "weight_decay": args.weight_decay * args.wd_norm_mult, - }, - { - "params": [ - p if not return_name else n - for n, p in model.named_parameters() - if not match_name_keywords(n, args.lr_backbone_names) - and match_name_keywords(n, args.lr_linear_proj_names) - and match_name_keywords(n, args.wd_norm_names) - and p.requires_grad - ], - "lr": args.lr * args.lr_linear_proj_mult, - "weight_decay": args.weight_decay * args.wd_norm_mult, - }, - ] - return param_dicts - - -def _get_clones(module, N): - return nn.ModuleList([copy.deepcopy(module) for i in range(N)]) - - -def _get_activation_fn(activation): - """Return an activation function given a string""" - if activation == "relu": - return F.relu - if activation == "gelu": - return F.gelu - if activation == "glu": - return F.glu - raise RuntimeError(f"activation should be relu/gelu, not {activation}.") diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/helpers.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/helpers.py deleted file mode 100644 index 0868dde9bf585327b5b087d070e14b394a0a7f22..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/helpers.py +++ /dev/null @@ -1,64 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import logging -import os -from typing import Any - -import torch -from omegaconf import OmegaConf - -import dinov3.distributed -from dinov3.eval import results - -logger = logging.getLogger("dinov3") - -CONFIG_FILE_KEY = "config_file" -EVAL_CONFIG_FNAME = "eval_config.yaml" - - -def write_results(results_dict, output_dir, results_filename) -> None: - """Save only on main if cuda is available""" - if torch.cuda.is_available() and not dinov3.distributed.is_main_process(): - return - results_path = os.path.join(output_dir, results_filename) - logger.info(f"Saving results to {results_path}") - results.save_from_dict(results_dict=results_dict, results_path=results_path) - - -def args_dict_to_dataclass(eval_args: dict[str, object], config_dataclass, save_config: bool = True) -> tuple[Any, str]: - """ - eval_args : arguments passed to create the eval config. - `CONFIG_FILE_KEY` is a reserved name to load a set of parameters from a config file. - config_dataclass: a dataclass used to define the config arguments, types and default values - save_config : whether to save the config in a file named `EVAL_CONFIG_FNAME` in the output_dir - """ - if CONFIG_FILE_KEY in eval_args: - config_file = eval_args.pop(CONFIG_FILE_KEY) - eval_args_config = OmegaConf.merge(OmegaConf.load(config_file), OmegaConf.create(eval_args)) - else: - eval_args_config = OmegaConf.create(eval_args) - - structured_config = OmegaConf.merge(OmegaConf.structured(config_dataclass), eval_args_config) - logger.info(f"Evaluation Configuration:\n{OmegaConf.to_yaml(structured_config)}") - output_dir = structured_config.output_dir - - if save_config and dinov3.distributed.is_main_process(): - OmegaConf.save(config=structured_config, f=os.path.join(output_dir, EVAL_CONFIG_FNAME)) - - return OmegaConf.to_object(structured_config), output_dir - - -def cli_parser(argv: list[str]) -> tuple[dict[str, Any]]: - """ - a method to parse argv and output a dict of eval arguments, and model building arguments. - - `argv` can come from the command line directly, or from a subset of the command line arguments, - as in dinov3.run.submitit - - `output_dir` can either be passed as `output_dir=` or `--output-dir=` (to support dinov3.run.submitit) - """ - cli_eval_args_dict = OmegaConf.to_container(OmegaConf.from_cli(argv)) - if "output_dir" not in cli_eval_args_dict: - cli_eval_args_dict["output_dir"] = cli_eval_args_dict.pop("--output-dir", ".") - return cli_eval_args_dict diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/knn.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/knn.py deleted file mode 100644 index 8b994133cc5800c519bd22d5997cf0f23129da45..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/knn.py +++ /dev/null @@ -1,384 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import json -import logging -import os -import sys -import time -from dataclasses import dataclass, field -from functools import partial -from typing import Any, Dict, Optional, Tuple - -import torch -import torch.backends.cudnn as cudnn -from omegaconf import MISSING -from torch.nn.functional import one_hot, softmax - -import dinov3.distributed as distributed -from dinov3.data import SamplerType, make_data_loader, make_dataset -from dinov3.data.adapters import DatasetWithEnumeratedTargets -from dinov3.data.transforms import ( - CROP_DEFAULT_SIZE, - RESIZE_DEFAULT_SIZE, - get_target_transform, - make_classification_eval_transform, -) -from dinov3.distributed import gather_all_tensors -from dinov3.eval.data import ( - create_train_dataset_dict, - extract_features_for_dataset_dict, - get_num_classes, - pad_multilabel_and_collate, -) -from dinov3.eval.helpers import args_dict_to_dataclass, cli_parser, write_results -from dinov3.eval.metrics import ClassificationMetricType, build_classification_metric -from dinov3.eval.setup import ModelConfig, load_model_and_context -from dinov3.eval.utils import ModelWithNormalize, average_metrics, evaluate -from dinov3.eval.utils import save_results as default_save_results_func -from dinov3.run.init import job_context - -logger = logging.getLogger("dinov3") - - -RESULTS_FILENAME = "results-knn.csv" -MAIN_METRICS = [".* Top 1"] - - -@dataclass -class TrainConfig: - dataset: str = MISSING # train dataset path - batch_size: int = 256 # batch size for train set feature extraction - num_workers: int = 5 # number of workers for train set feature extraction - ks: Tuple[int, ...] = (10, 20, 100, 200) # values of k to evaluate - temperature: float = 0.07 - """ - Whether to skip the first nearest neighbor for each image in the test set. - Useful when training and testing on the same dataset split. - """ - skip_first_nn: bool = False - - -@dataclass -class EvalConfig: - test_dataset: str = MISSING # test dataset path - test_metric_type: ClassificationMetricType = ClassificationMetricType.MEAN_ACCURACY - batch_size: int | None = None # batch size for evaluation, None to use train batch size - num_workers: int = 5 # number of workers for evaluation - - -@dataclass -class TransformConfig: - resize_size: int = RESIZE_DEFAULT_SIZE - crop_size: int = CROP_DEFAULT_SIZE - - -@dataclass -class FewShotConfig: - enable: bool = False # whether to use few-shot evaluation - k_or_percent: Optional[float] = None # number of elements or % to take per class - n_tries: int = 1 # number of tries for few-shot evaluation - - -@dataclass -class KnnEvalConfig: - model: ModelConfig - train: TrainConfig = field(default_factory=TrainConfig) - eval: EvalConfig = field(default_factory=EvalConfig) - transform: TransformConfig = field(default_factory=TransformConfig) - few_shot: FewShotConfig = field(default_factory=FewShotConfig) - save_results: bool = False # save predictions and targets in the output directory - output_dir: str = "" - - -class KnnModule(torch.nn.Module): - """ - Gets knn of test features from all processes on a chunk of the train features - - Each rank gets a chunk of the train features as well as a chunk of the test features. - In `compute_neighbors`, for each rank one after the other, its chunk of test features - is sent to all devices, partial knns are computed with each chunk of train features - then collated back on the original device. - """ - - def __init__(self, *, train_features, train_labels, device, ks, T, num_classes=1000, skip_first_nn=False): - super().__init__() - - self.rank = distributed.get_rank() - self.world_size = distributed.get_world_size() - - self.device = device - self.train_features_rank_T = train_features.chunk(self.world_size)[self.rank].T.to(self.device) - # Labels can either be integers, or in a one-hot format - self.candidates = train_labels.chunk(self.world_size)[self.rank].unsqueeze(0).to(self.device) - - self.ks = ks - self.max_k = max(self.ks) + skip_first_nn - self.T = T - self.num_classes = num_classes - self.skip_first_nn = skip_first_nn - - if self.skip_first_nn: - logger.info("Skipping the first nearest neighbor of each element in the test dataset") - - def _get_knn_sims_and_labels(self, similarity, train_labels): - topk_sims, indices = similarity.topk(min(self.max_k, similarity.shape[1]), largest=True, sorted=True) - if len(train_labels.shape) == 3: # If the labels are in one_hot format - indices = indices.unsqueeze(2).expand(-1, -1, self.num_classes) # Orignally [bs, max_k] - neighbors_labels = torch.gather(train_labels, 1, indices) - return topk_sims, neighbors_labels - - def _similarity_for_rank(self, features_rank, source_rank): - """ - Broadcasts `features_rank` from `source_rank` and compute similarities - with the train features chunks from all ranks - """ - # Send the features from `source_rank` to all ranks - broadcast_shape = torch.tensor(features_rank.shape).to(self.device) - torch.distributed.broadcast(broadcast_shape, source_rank) - - broadcasted = features_rank - if self.rank != source_rank: - broadcasted = torch.zeros(*broadcast_shape, dtype=features_rank.dtype, device=self.device) - torch.distributed.broadcast(broadcasted, source_rank) - - # Compute the neighbors for `source_rank` among `train_features_rank_T` - similarity_rank = torch.mm(broadcasted, self.train_features_rank_T) - candidate_labels = self.candidates.expand(len(similarity_rank), *self.candidates.shape[1:]) - return self._get_knn_sims_and_labels(similarity_rank, candidate_labels) - - def compute_neighbors(self, features_rank): - """ - If we are on rank `rank`, we broadcast the test features to other ranks, compute similarities - with their chunks of the train features, then gather these partial similarities back on `rank` - """ - topk_sims_rank, neighbors_labels_rank = None, None - for rank in range(self.world_size): - partial_topk_sims, partial_neighbors_labels = self._similarity_for_rank(features_rank, rank) - gathered_topk_sims = torch.cat(gather_all_tensors(partial_topk_sims), dim=1) - gathered_neighbor_labels = torch.cat(gather_all_tensors(partial_neighbors_labels), dim=1) - if self.rank == rank: # Performing a second top-k to get k neighbors from the gathered k * world_size - topk_sims_rank, neighbors_labels_rank = self._get_knn_sims_and_labels( - gathered_topk_sims, gathered_neighbor_labels - ) - return topk_sims_rank, neighbors_labels_rank - - def forward(self, features_rank): - """ - Compute the results on all values of `self.ks` neighbors from the full `self.max_k` - """ - assert all(k <= self.max_k for k in self.ks) - - topk_sims, neighbors_labels = self.compute_neighbors(features_rank) - if self.skip_first_nn: - topk_sims, neighbors_labels = topk_sims[:, 1:], neighbors_labels[:, 1:] - batch_size = neighbors_labels.shape[0] - topk_sims_transform = softmax(topk_sims / self.T, 1) - voting_coefficient = topk_sims_transform.view(batch_size, -1, 1) - if len(neighbors_labels.shape) == 2: # If the labels are not yet one hot - neighbors_labels = one_hot(neighbors_labels, num_classes=self.num_classes) - matmul = torch.mul(neighbors_labels, voting_coefficient) - probas_for_k = {k: torch.sum(matmul[:, :k, :], 1) for k in self.ks} - return probas_for_k - - -class DictKeysModule(torch.nn.Module): - def __init__(self, keys): - super().__init__() - self.keys = keys - - def forward(self, features_dict, targets): - for k in self.keys: - features_dict = features_dict[k] - return {"preds": features_dict, "target": targets} - - -def make_transform(config: TransformConfig): - if config.resize_size / config.crop_size != 256 / 224: - logger.warning( - f"Default resize / crop ratio is 256 / 224, here we have {config.resize_size} / {config.crop_size}" - ) - transform = make_classification_eval_transform(resize_size=config.resize_size, crop_size=config.crop_size) - return transform - - -def make_test_data_loader(config: EvalConfig, transform): - # Create test data loader. Do not extract features in advance due to difficulties with multilabel datasets. - multilabel_collate_fn = config.test_metric_type == ClassificationMetricType.ANY_MATCH_ACCURACY - test_dataset = make_dataset( - dataset_str=config.test_dataset, - transform=transform, - target_transform=get_target_transform(config.test_dataset), - ) - assert isinstance(config.batch_size, int) # eval batch size has been replaced by train batch size if None - - return make_data_loader( - dataset=DatasetWithEnumeratedTargets(test_dataset, pad_dataset=True, num_replicas=distributed.get_world_size()), - batch_size=config.batch_size, - num_workers=config.num_workers, - sampler_type=SamplerType.DISTRIBUTED, - drop_last=False, - shuffle=False, - persistent_workers=True, - collate_fn=pad_multilabel_and_collate if multilabel_collate_fn else None, - ) - - -def eval_knn( - *, - model, - train_data_dict, - test_data_loader, - metric_collection, - knn_config: TrainConfig, - num_classes: int, - save_results_func=None, -): - logger.info("Start the k-NN classification.") - eval_metrics_dict: Dict[int, Dict[int, Dict[str, float]]] = {} # {k: {try: {metric_name: metric_value}}} - save_results = save_results_func is not None - device = torch.cuda.current_device() - partial_knn_module = partial( - KnnModule, - device=device, - num_classes=num_classes, - T=knn_config.temperature, - skip_first_nn=knn_config.skip_first_nn, - ) - - for try_ in train_data_dict.keys(): - train_features, train_labels = train_data_dict[try_]["train_features"], train_data_dict[try_]["train_labels"] - ks = sorted(set([el if el < len(train_features) else len(train_features) for el in knn_config.ks])) - knn_module = partial_knn_module(train_features=train_features, train_labels=train_labels, ks=ks) - postprocessors, metrics = {k: DictKeysModule([k]) for k in ks}, {k: metric_collection.clone() for k in ks} - _, eval_metrics, accumulated_results = evaluate( - torch.nn.Sequential(model, knn_module), - test_data_loader, - postprocessors, - metrics, - device, - accumulate_results=save_results, - ) - for k in ks: - if save_results: - if len(train_data_dict) > 1: - split_results_saver = partial(save_results_func, filename_suffix=f"try_{try_}_k_{k}") - else: - split_results_saver = partial(save_results_func, filename_suffix=f"k_{k}") - split_results_saver(**accumulated_results[k]) - - if k not in eval_metrics_dict: - eval_metrics_dict[k] = {} - eval_metrics_dict[k][try_] = {metric: v.item() * 100.0 for metric, v in eval_metrics[k].items()} - - if len(train_data_dict) > 1: - return {k: average_metrics(eval_metrics_dict[k]) for k in eval_metrics_dict.keys()} - - return {k: eval_metrics_dict[k][0] for k in eval_metrics_dict.keys()} - - -def _log_and_format_results_dict(input_results_dict, few_shot_n_tries: int) -> Dict[str, float]: - results_dict = {} - for knn_ in input_results_dict.keys(): - if few_shot_n_tries == 1: - top1 = input_results_dict[knn_]["top-1"] - results_dict[f"{knn_} Top 1"] = top1 - results_string = f"{knn_} NN classifier result: Top1: {top1:.2f}" - if "top-5" in input_results_dict[knn_]: - top5 = input_results_dict[knn_]["top-5"] - results_dict[f"{knn_} Top 5"] = top5 - results_string += f" Top5: {top5:.2f}" - else: - top1_mean, top1_std = input_results_dict[knn_]["top-1_mean"], input_results_dict[knn_]["top-1_std"] - results_dict[f"{knn_} Top 1"] = top1_mean - results_string = f"{knn_} NN classifier result: Top1 Avg: {top1_mean:.2f}, Top1 Std {top1_std:.2f}" - if "top-5_mean" in input_results_dict[knn_]: - top5_mean, top5_std = input_results_dict[knn_]["top-5_mean"], input_results_dict[knn_]["top-5_std"] - results_dict[f"{knn_} Top 5"] = top5_mean - results_string += f" Top5 Avg: {top5_mean:.2f}, Top5 Std {top5_std:.2f}" - logger.info(results_string) - return results_dict - - -def eval_knn_with_model(*, model: torch.nn.Module, autocast_dtype, config: KnnEvalConfig): - start = time.time() - cudnn.benchmark = True - - # Setting up datasets - transform = make_transform(config.transform) - train_dataset = make_dataset( - dataset_str=config.train.dataset, - transform=transform, - target_transform=get_target_transform(config.train.dataset), - ) - train_dataset_dict = create_train_dataset_dict( - train_dataset, - few_shot_eval=config.few_shot.enable, - few_shot_k_or_percent=config.few_shot.k_or_percent, - few_shot_n_tries=config.few_shot.n_tries, - ) - - # Setting up metrics - num_classes = get_num_classes(train_dataset) - metric_collection = build_classification_metric(config.eval.test_metric_type, num_classes=num_classes) - config.eval.batch_size = config.eval.batch_size or config.train.batch_size - test_data_loader = make_test_data_loader(config.eval, transform) - - # Setting up save results function - save_results_func = None - if config.save_results: - save_results_func = partial(default_save_results_func, output_dir=config.output_dir) - - model = ModelWithNormalize(model) - with torch.autocast("cuda", dtype=autocast_dtype): - logger.info("Extracting features for train set...") - train_data_dict = extract_features_for_dataset_dict( - model, train_dataset_dict, config.train.batch_size, config.train.num_workers, gather_on_cpu=True - ) - results_dict_knn = eval_knn( - model=model, - train_data_dict=train_data_dict, - test_data_loader=test_data_loader, - metric_collection=metric_collection, - knn_config=config.train, - num_classes=num_classes, - save_results_func=save_results_func, - ) - results_dict = _log_and_format_results_dict(results_dict_knn, config.few_shot.n_tries) - - # TODO: Remove as cleaner writers are used - metrics_file_path = os.path.join(config.output_dir, "results_eval_knn.json") - with open(metrics_file_path, "a") as f: - for k, v in results_dict.items(): - f.write(json.dumps({k: v}) + "\n") - - if distributed.is_enabled(): - torch.distributed.barrier() - logger.info(f"Knn evaluation done in {int(time.time() - start)}s") - return results_dict - - -def benchmark_launcher(eval_args: dict[str, object]) -> dict[str, Any]: - """Initialization of distributed and logging are preconditions for this method""" - dataclass_config, output_dir = args_dict_to_dataclass(eval_args=eval_args, config_dataclass=KnnEvalConfig) - model, model_context = load_model_and_context(dataclass_config.model, output_dir=output_dir) - results_dict = eval_knn_with_model( - model=model, config=dataclass_config, autocast_dtype=model_context["autocast_dtype"] - ) - write_results(results_dict, output_dir, RESULTS_FILENAME) - return results_dict - - -def main(argv=None): - if argv is None: - argv = sys.argv[1:] - eval_args = cli_parser(argv) - with job_context(output_dir=eval_args["output_dir"]): - benchmark_launcher(eval_args=eval_args) - return 0 - - -if __name__ == "__main__": - main() diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/linear.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/linear.py deleted file mode 100644 index e091d24398581bd40de913008a19767decb2c8aa..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/linear.py +++ /dev/null @@ -1,688 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import json -import logging -import os -import sys -import time -from dataclasses import dataclass, field -from enum import Enum -from functools import partial -from pathlib import Path -from typing import Any, Callable, Dict, Optional, Tuple - -import torch -import torch.backends.cudnn as cudnn -import torch.nn as nn -from omegaconf import MISSING -from torch.nn.parallel import DistributedDataParallel - -import dinov3.distributed as distributed -from dinov3.checkpointer import ( - CheckpointRetentionPolicy, - cleanup_checkpoint, - find_latest_checkpoint, - keep_last_n_checkpoints, -) -from dinov3.data import SamplerType, make_data_loader, make_dataset -from dinov3.data.adapters import DatasetWithEnumeratedTargets -from dinov3.data.transforms import ( - CROP_DEFAULT_SIZE, - RESIZE_DEFAULT_SIZE, - make_classification_eval_transform, - make_classification_train_transform, -) -from dinov3.eval.data import create_train_dataset_dict, get_num_classes, pad_multilabel_and_collate -from dinov3.eval.helpers import args_dict_to_dataclass, cli_parser, write_results -from dinov3.eval.metrics import ClassificationMetricType, build_classification_metric -from dinov3.eval.setup import ModelConfig, load_model_and_context -from dinov3.eval.utils import LossType, ModelWithIntermediateLayers, average_metrics, evaluate -from dinov3.eval.utils import save_results as default_save_results_func -from dinov3.logging import MetricLogger, SmoothedValue -from dinov3.run.init import job_context - -logger = logging.getLogger("dinov3") - -RESULTS_FILENAME = "results-linear.csv" -# Can be several keys, depending on if multiple test sets are chosen and if doing few-shot -MAIN_METRICS = [".*_accuracy(_mean)?"] - - -class OptimizerType(Enum): - SGD = "sgd" - ADAMW = "adamw" - - def get_optimizer(self, optim_param_groups): - if self == OptimizerType.ADAMW: - optimizer = torch.optim.AdamW(optim_param_groups, weight_decay=0) - else: - optimizer = torch.optim.SGD(optim_param_groups, momentum=0.9, weight_decay=0) - return optimizer - - -class SchedulerType(Enum): - COSINE_ANNEALING = "cosine_annealing" - ONE_CYCLE = "one_cycle" - - def get_scheduler(self, optimizer, optim_param_groups, epoch_length, epochs, max_iter): - if self == SchedulerType.ONE_CYCLE: - lr_list = [optim_param_groups[i]["lr"] for i in range(len(optim_param_groups))] - scheduler = torch.optim.lr_scheduler.OneCycleLR( - optimizer, max_lr=lr_list, steps_per_epoch=epoch_length, epochs=epochs - ) - else: - scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, max_iter, eta_min=0) - return scheduler - - -_DEFAULT_LR_LIST: Tuple[float, ...] = (1e-5, 2e-5, 5e-5, 1e-4, 2e-4, 5e-4, 1e-3, 2e-3, 5e-3, 1e-2, 2e-2, 5e-2, 0.1) - - -@dataclass -class TrainConfig: - dataset: str = MISSING # train dataset path - val_dataset: str = MISSING # val dataset path - val_metric_type: ClassificationMetricType = ClassificationMetricType.MEAN_ACCURACY - batch_size: int = 128 # batch size (per GPU) - num_workers: int = 8 - # Linear Head Parameters - learning_rates: Tuple[float, ...] = _DEFAULT_LR_LIST # learning rates to grid search - n_last_blocks_list: Tuple[int] = (1,) # number of backbone last blocks used for the linear classifier - loss_type: LossType = LossType.CROSS_ENTROPY - optimizer_type: OptimizerType = OptimizerType.SGD - scheduler_type: SchedulerType = SchedulerType.COSINE_ANNEALING - epochs: int = 10 # number of training epochs - epoch_length: int = 1250 # length of an epoch in number of iterations - save_checkpoint_iterations: int | None = ( - None # number of iterations between two checkpoint saves (default: one epoch) - ) - eval_period_iterations: int | None = None # number of iterations between two evaluations (default: one epoch) - checkpoint_retention_policy: CheckpointRetentionPolicy = CheckpointRetentionPolicy.NONE # keep checkpoints or not - resume: bool = True # whether to resume from existing checkpoints - classifier_fpath: Optional[str] = None # path to a file containing pretrained linear classifiers - - -@dataclass -class EvalConfig: - test_datasets: Tuple[str, ...] = () # additional test dataset paths - test_metric_types: Tuple[ClassificationMetricType, ...] = () - batch_size: int = 256 # batch size (per GPU) - num_workers: int = 8 - - -@dataclass -class TransformConfig: - resize_size: int = RESIZE_DEFAULT_SIZE - crop_size: int = CROP_DEFAULT_SIZE - - -@dataclass -class FewShotConfig: - enable: bool = False # whether to use few-shot evaluation - k_or_percent: Optional[float] = None # number of elements or % to take per class - n_tries: int = 1 # number of tries for few-shot evaluation - - -@dataclass -class LinearEvalConfig: - model: ModelConfig - train: TrainConfig = field(default_factory=TrainConfig) - eval: EvalConfig = field(default_factory=EvalConfig) - transform: TransformConfig = field(default_factory=TransformConfig) - few_shot: FewShotConfig = field(default_factory=FewShotConfig) - save_results: bool = False # save predictions and targets in the output directory - output_dir: str = "" - - -def has_ddp_wrapper(m: nn.Module) -> bool: - return isinstance(m, DistributedDataParallel) - - -def remove_ddp_wrapper(m: nn.Module) -> nn.Module: - return m.module if has_ddp_wrapper(m) else m - - -def create_linear_input(x_tokens_list, use_n_blocks, use_avgpool): - intermediate_output = x_tokens_list[-use_n_blocks:] - output = torch.cat([class_token for _, class_token in intermediate_output], dim=-1) - if use_avgpool: - output = torch.cat( - ( - output, - torch.mean(intermediate_output[-1][0], dim=1), # patch tokens - ), - dim=-1, - ) - output = output.reshape(output.shape[0], -1) - return output.float() - - -class LinearClassifier(nn.Module): - """Linear layer to train on top of frozen features""" - - def __init__(self, out_dim, use_n_blocks, use_avgpool, num_classes=1000): - super().__init__() - self.out_dim = out_dim - self.use_n_blocks = use_n_blocks - self.use_avgpool = use_avgpool - self.num_classes = num_classes - self.linear = nn.Linear(out_dim, num_classes) - self.linear.weight.data.normal_(mean=0.0, std=0.01) - self.linear.bias.data.zero_() - - def forward(self, x_tokens_list): - output = create_linear_input(x_tokens_list, self.use_n_blocks, self.use_avgpool) - return self.linear(output) - - -class AllClassifiers(nn.Module): - def __init__(self, classifiers_dict): - super().__init__() - self.classifiers_dict = nn.ModuleDict() - self.classifiers_dict.update(classifiers_dict) - - def forward(self, inputs): - return {k: v.forward(inputs) for k, v in self.classifiers_dict.items()} - - def __len__(self): - return len(self.classifiers_dict) - - -class LinearPostprocessor(nn.Module): - def __init__(self, linear_classifier, class_mapping=None): - super().__init__() - self.linear_classifier = linear_classifier - self.register_buffer("class_mapping", None if class_mapping is None else torch.LongTensor(class_mapping)) - - def forward(self, samples, targets): - preds = self.linear_classifier(samples) - return { - "preds": preds[:, self.class_mapping] if self.class_mapping is not None else preds, - "target": targets, - } - - -def scale_lr(learning_rates, batch_size): - return learning_rates * (batch_size * distributed.get_world_size()) / 256.0 - - -def setup_linear_classifiers(sample_output, n_last_blocks_list, learning_rates, batch_size, num_classes=1000): - linear_classifiers_dict = nn.ModuleDict() - optim_param_groups = [] - for n in n_last_blocks_list: - for avgpool in [True]: - for _lr in learning_rates: - lr = scale_lr(_lr, batch_size) - out_dim = create_linear_input(sample_output, use_n_blocks=n, use_avgpool=avgpool).shape[1] - linear_classifier = LinearClassifier( - out_dim, use_n_blocks=n, use_avgpool=avgpool, num_classes=num_classes - ) - linear_classifier = linear_classifier.cuda() - linear_classifiers_dict[ - f"classifier_{n}_blocks_avgpool_{avgpool}_lr_{lr:.5f}".replace(".", "_") - ] = linear_classifier - optim_param_groups.append({"params": linear_classifier.parameters(), "lr": lr}) - - linear_classifiers = AllClassifiers(linear_classifiers_dict) - if distributed.is_enabled(): - linear_classifiers = nn.parallel.DistributedDataParallel(linear_classifiers) - - return linear_classifiers, optim_param_groups - - -def make_eval_transform(config: TransformConfig): - if config.resize_size / config.crop_size != 256 / 224: - logger.warning( - f"Default resize / crop ratio is 256 / 224, here we have {config.resize_size} / {config.crop_size}" - ) - transform = make_classification_eval_transform(resize_size=config.resize_size, crop_size=config.crop_size) - return transform - - -def make_eval_data_loader( - *, - test_dataset_str, - transform_config, - batch_size, - num_workers, - metric_type, -): - transform = make_eval_transform(transform_config) - test_dataset = make_dataset(dataset_str=test_dataset_str, transform=transform) - - class_mapping = None - if hasattr(test_dataset, "get_imagenet_class_mapping"): - class_mapping = test_dataset.get_imagenet_class_mapping() - - test_data_loader = make_data_loader( - dataset=DatasetWithEnumeratedTargets(test_dataset, pad_dataset=True, num_replicas=distributed.get_world_size()), - batch_size=batch_size, - num_workers=num_workers, - sampler_type=SamplerType.DISTRIBUTED, - drop_last=False, - shuffle=False, - persistent_workers=False, - collate_fn=pad_multilabel_and_collate if metric_type == ClassificationMetricType.ANY_MATCH_ACCURACY else None, - ) - return test_data_loader, class_mapping - - -@dataclass -class Evaluator: - batch_size: int - num_workers: int - transform_config: TransformConfig - dataset_str: str - metric_type: ClassificationMetricType - metrics_file_path: str - training_num_classes: int - save_results_func: Optional[Callable] - - def __post_init__(self): - self.data_loader, self.class_mapping = make_eval_data_loader( - test_dataset_str=self.dataset_str, - batch_size=self.batch_size, - num_workers=self.num_workers, - transform_config=self.transform_config, - metric_type=self.metric_type, - ) - self.main_metric_name = f"{self.dataset_str}_accuracy" - - @torch.no_grad() - def _evaluate_linear_classifiers( - self, - *, - feature_model, - linear_classifiers, - iteration, - prefixstring="", - best_classifier_on_val=None, - accumulate_results=False, - ) -> Tuple[Dict[str, Any], Optional[Dict[str, torch.Tensor]]]: - logger.info("running validation !") - - num_classes = len(self.class_mapping) if self.class_mapping is not None else self.training_num_classes - metric = build_classification_metric(self.metric_type, num_classes=num_classes) - postprocessors = { - k: LinearPostprocessor(v, self.class_mapping) for k, v in linear_classifiers.classifiers_dict.items() - } - metrics = {k: metric.clone() for k in linear_classifiers.classifiers_dict} - - _, results_dict_temp, accumulated_results = evaluate( - feature_model, - self.data_loader, - postprocessors, - metrics, - torch.cuda.current_device(), - accumulate_results=accumulate_results, - ) - - logger.info("") - results_dict = {} - max_accuracy = 0 - best_classifier = "" - for _, (classifier_string, metric) in enumerate(results_dict_temp.items()): - logger.info(f"{prefixstring} -- Classifier: {classifier_string} * {metric}") - if ( - best_classifier_on_val is None and metric["top-1"].item() > max_accuracy - ) or classifier_string == best_classifier_on_val: - max_accuracy = metric["top-1"].item() - best_classifier = classifier_string - - results_dict["best_classifier"] = {"name": best_classifier, "accuracy": max_accuracy} - - logger.info(f"best classifier: {results_dict['best_classifier']}") - - accumulated_best_results = None - if accumulated_results is not None: - accumulated_best_results = accumulated_results[best_classifier] - - if distributed.is_main_process(): - with open(self.metrics_file_path, "a") as f: - f.write(f"iter: {iteration}\n") - for k, v in results_dict.items(): - f.write(json.dumps({k: v}) + "\n") - f.write("\n") - - return results_dict, accumulated_best_results - - def evaluate_and_maybe_save( - self, - feature_model, - linear_classifiers, - iteration: int, - best_classifier_on_val: Optional[Any] = None, - save_filename_suffix: str = "", - prefixstring: str = "", - ): - logger.info(f"Testing on {self.dataset_str}") - save_results = self.save_results_func is not None - full_results_dict, accumulated_best_results = self._evaluate_linear_classifiers( - feature_model=feature_model, - linear_classifiers=remove_ddp_wrapper(linear_classifiers), - iteration=iteration, - prefixstring=prefixstring, - best_classifier_on_val=best_classifier_on_val, - accumulate_results=save_results, - ) - if self.save_results_func is not None: - self.save_results_func( - filename_suffix=f"{self.dataset_str}{save_filename_suffix}", **accumulated_best_results - ) - - results_dict = { - self.main_metric_name: 100.0 * full_results_dict["best_classifier"]["accuracy"], - "best_classifier": full_results_dict["best_classifier"]["name"], - } - return results_dict - - -def make_evaluators( - eval_config: EvalConfig, - val_metric_type: ClassificationMetricType, - val_dataset: str, - transform_config: TransformConfig, - metrics_file_path: str, - training_num_classes: int, - save_results_func: Optional[Callable], -): - test_metric_types = eval_config.test_metric_types - if len(test_metric_types) == 0: - test_metric_types = (val_metric_type,) * len(eval_config.test_datasets) - else: - assert len(test_metric_types) == len(eval_config.test_datasets) - val_evaluator, *test_evaluators = [ - Evaluator( - dataset_str=dataset_str, - batch_size=eval_config.batch_size, - num_workers=eval_config.num_workers, - transform_config=transform_config, - metric_type=metric_type, - metrics_file_path=metrics_file_path, - training_num_classes=training_num_classes, - save_results_func=save_results_func, - ) - for dataset_str, metric_type in zip( - (val_dataset,) + tuple(eval_config.test_datasets), - (val_metric_type,) + tuple(test_metric_types), - ) - ] - return val_evaluator, test_evaluators - - -def setup_linear_training( - *, - config: TrainConfig, - sample_output: torch.Tensor, - training_num_classes: int, - checkpoint_output_dir: str, -): - linear_classifiers, optim_param_groups = setup_linear_classifiers( - sample_output, - config.n_last_blocks_list, - config.learning_rates, - config.batch_size, - training_num_classes, - ) - max_iter = config.epochs * config.epoch_length - optimizer = config.optimizer_type.get_optimizer(optim_param_groups=optim_param_groups) - scheduler = config.scheduler_type.get_scheduler( - optimizer=optimizer, - optim_param_groups=optim_param_groups, - epoch_length=config.epoch_length, - epochs=config.epochs, - max_iter=max_iter, - ) - start_iter = 0 - best_accuracy = -1 - if config.resume and ( - last_checkpoint_dir := find_latest_checkpoint(config.classifier_fpath or checkpoint_output_dir) - ): - logger.info(f"Checkpoint found {last_checkpoint_dir}") - checkpoint = torch.load(last_checkpoint_dir / "checkpoint.pth") - start_iter = checkpoint.get("iteration", -1) + 1 - best_accuracy = checkpoint.get("best_accuracy", -1) - linear_classifiers.load_state_dict(checkpoint["linear_classifiers"]) - optimizer.load_state_dict(checkpoint["optimizer"]) - - if config.loss_type == LossType.BINARY_CROSS_ENTROPY: - criterion = nn.BCEWithLogitsLoss() - else: - criterion = nn.CrossEntropyLoss() - - return ( - linear_classifiers, - start_iter, - max_iter, - criterion, - optimizer, - scheduler, - best_accuracy, - ) - - -def train_linear_classifiers( - *, - feature_model, - train_dataset, - train_config: TrainConfig, - training_num_classes: int, - val_evaluator: Evaluator, - checkpoint_output_dir: str, -): - (linear_classifiers, start_iter, max_iter, criterion, optimizer, scheduler, best_accuracy,) = setup_linear_training( - config=train_config, - sample_output=feature_model(train_dataset[0][0].unsqueeze(0).cuda()), - training_num_classes=training_num_classes, - checkpoint_output_dir=checkpoint_output_dir, - ) - checkpoint_period = train_config.save_checkpoint_iterations or train_config.epoch_length - eval_period = train_config.eval_period_iterations or train_config.epoch_length - - sampler_type = SamplerType.INFINITE - train_data_loader = make_data_loader( - dataset=train_dataset, - batch_size=train_config.batch_size, - num_workers=train_config.num_workers, - shuffle=True, - seed=0, - sampler_type=sampler_type, - sampler_advance=start_iter, - drop_last=True, - persistent_workers=True, - ) - - iteration = start_iter - logger.info("Starting training from iteration {}".format(start_iter)) - metric_logger = MetricLogger(delimiter=" ") - metric_logger.add_meter("lr", SmoothedValue(window_size=1, fmt="{value:.6g}")) - header = "Training" - for data, labels in metric_logger.log_every( - train_data_loader, - 10, - header, - max_iter, - start_iter, - ): - data = data.cuda(non_blocking=True) - labels = labels.cuda(non_blocking=True) - - features = feature_model(data) - outputs = linear_classifiers(features) - - if len(labels.shape) > 1: - labels = labels.float() - losses = {f"loss_{k}": criterion(v, labels) for k, v in outputs.items()} - loss = sum(losses.values()) - - # compute the gradients - optimizer.zero_grad() - loss.backward() - - # step - optimizer.step() - scheduler.step() - - # log - if iteration % 10 == 0: - torch.cuda.synchronize() - metric_logger.update(loss=loss.item()) - metric_logger.update(lr=optimizer.param_groups[0]["lr"]) - - # Checkpointing - is_last_iteration = (iteration + 1) == max_iter - is_ckpt_iteration = ((iteration + 1) % checkpoint_period == 0) or is_last_iteration - if is_ckpt_iteration: - ckpt_dir = Path(checkpoint_output_dir).expanduser() - if distributed.is_subgroup_main_process(): - ckpt_sub_dir = "final" if is_last_iteration else str(iteration) - (ckpt_dir / ckpt_sub_dir).mkdir(parents=True, exist_ok=True) - checkpoint = { - "iteration": iteration, - "linear_classifiers": linear_classifiers.state_dict(), - "optimizer": optimizer.state_dict(), - "best_accuracy": best_accuracy, - } - torch.save(checkpoint, ckpt_dir / ckpt_sub_dir / "checkpoint.pth") - keep_last_n_checkpoints(ckpt_dir, train_config.checkpoint_retention_policy.max_to_keep) - - if eval_period > 0 and (iteration + 1) % eval_period == 0 and iteration != max_iter - 1: - val_results_dict = val_evaluator.evaluate_and_maybe_save( - feature_model=feature_model, - linear_classifiers=linear_classifiers, - prefixstring=f"ITER: {iteration}", - iteration=iteration, - ) - val_accuracy = val_results_dict[val_evaluator.main_metric_name] - if val_accuracy >= best_accuracy: - best_accuracy = val_accuracy - (ckpt_dir / "best").mkdir(parents=True, exist_ok=True) - checkpoint = { - "iteration": iteration, - "linear_classifiers": linear_classifiers.state_dict(), - "optimizer": optimizer.state_dict(), - "best_accuracy": best_accuracy, - } - torch.save(checkpoint, ckpt_dir / "best" / "checkpoint.pth") - torch.distributed.barrier() - - iteration = iteration + 1 - - return feature_model, linear_classifiers, iteration - - -def make_train_transform(config: TransformConfig): - train_transform = make_classification_train_transform(crop_size=config.crop_size) - return train_transform - - -def make_train_dataset(train_dataset: str, transform_config: TransformConfig): - train_transform = make_train_transform(transform_config) - return make_dataset(dataset_str=train_dataset, transform=train_transform) - - -def eval_linear_with_model(*, model: torch.nn.Module, autocast_dtype, config: LinearEvalConfig): - start = time.time() - cudnn.benchmark = True - - train_dataset = make_train_dataset(config.train.dataset, config.transform) - training_num_classes = get_num_classes(train_dataset) - train_dataset_dict = create_train_dataset_dict( - train_dataset, - few_shot_eval=config.few_shot.enable, - few_shot_k_or_percent=config.few_shot.k_or_percent, - few_shot_n_tries=config.few_shot.n_tries, - ) - n_last_blocks = max(config.train.n_last_blocks_list) - autocast_ctx = partial(torch.autocast, device_type="cuda", enabled=True, dtype=autocast_dtype) - feature_model = ModelWithIntermediateLayers(model, n_last_blocks, autocast_ctx) - - save_results_func = None - if config.save_results: - save_results_func = partial(default_save_results_func, output_dir=config.output_dir) - - metrics_file_path = os.path.join(config.output_dir, "results_eval_linear.json") - val_evaluator, test_evaluators = make_evaluators( - eval_config=config.eval, - val_metric_type=config.train.val_metric_type, - val_dataset=config.train.val_dataset, - transform_config=config.transform, - metrics_file_path=metrics_file_path, - training_num_classes=training_num_classes, - save_results_func=save_results_func, - ) - results_dict = {} - checkpoint_output_dirs: list = [] - for _try in train_dataset_dict.keys(): - if len(train_dataset_dict) > 1: - checkpoint_output_dir = os.path.join(config.output_dir, f"checkpoints_{_try}") - save_filename_suffix = f"_{_try}" - else: - checkpoint_output_dir = os.path.join(config.output_dir, "checkpoints") - save_filename_suffix = "" - os.makedirs(checkpoint_output_dir, exist_ok=True) - - feature_model, linear_classifiers, iteration = train_linear_classifiers( - feature_model=feature_model, - train_dataset=train_dataset_dict[_try], - train_config=config.train, - training_num_classes=training_num_classes, - val_evaluator=val_evaluator, - checkpoint_output_dir=checkpoint_output_dir, - ) - checkpoint_output_dirs.append(checkpoint_output_dir) - results_dict[_try] = val_evaluator.evaluate_and_maybe_save( - feature_model=feature_model, - linear_classifiers=linear_classifiers, - iteration=iteration, - save_filename_suffix=save_filename_suffix, - ) - for test_evaluator in test_evaluators: - eval_results_dict = test_evaluator.evaluate_and_maybe_save( - feature_model=feature_model, - linear_classifiers=linear_classifiers, - iteration=iteration, - best_classifier_on_val=results_dict[_try]["best_classifier"], - save_filename_suffix=save_filename_suffix, - ) - results_dict[_try] = {**eval_results_dict, **results_dict[_try]} - - if len(train_dataset_dict) > 1: - results_dict = average_metrics(results_dict, ignore_keys=["best_classifier"]) - else: - results_dict = {**results_dict[_try]} - - for checkpoint_output_dir in checkpoint_output_dirs: - if distributed.is_subgroup_main_process(): - cleanup_checkpoint(checkpoint_output_dir, config.train.checkpoint_retention_policy) - - logger.info("Test Results Dict " + str(results_dict)) - logger.info(f"Linear evaluation done in {int(time.time() - start)}s") - return results_dict - - -def benchmark_launcher(eval_args: dict[str, object]) -> dict[str, Any]: - """Initialization of distributed and logging are preconditions for this method""" - dataclass_config, output_dir = args_dict_to_dataclass(eval_args=eval_args, config_dataclass=LinearEvalConfig) - model, model_context = load_model_and_context(dataclass_config.model, output_dir=output_dir) - results_dict = eval_linear_with_model( - model=model, config=dataclass_config, autocast_dtype=model_context["autocast_dtype"] - ) - write_results(results_dict, output_dir, RESULTS_FILENAME) - return results_dict - - -def main(argv=None): - if argv is None: - argv = sys.argv[1:] - eval_args = cli_parser(argv) - with job_context(output_dir=eval_args["output_dir"]): - benchmark_launcher(eval_args=eval_args) - return 0 - - -if __name__ == "__main__": - main() diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/log_regression.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/log_regression.py deleted file mode 100644 index e1a3e7c8100c539ff71a13f713eedd7bb739810a..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/log_regression.py +++ /dev/null @@ -1,422 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import logging -import sys -import time -from dataclasses import dataclass, field -from functools import partial -from typing import Any, Dict, List, Optional - -import torch -import torch.backends.cudnn as cudnn -import torch.distributed -from omegaconf import MISSING -from torch import nn -from torch.utils.data import TensorDataset -from torchmetrics import MetricTracker - -from dinov3.data import SamplerType, make_data_loader, make_dataset -from dinov3.data.adapters import DatasetWithEnumeratedTargets -from dinov3.data.transforms import CROP_DEFAULT_SIZE, get_target_transform, make_classification_eval_transform -from dinov3.distributed import get_rank, get_world_size -from dinov3.eval.data import ( - create_train_dataset_dict, - extract_features_for_dataset_dict, - get_num_classes, - split_train_val_datasets, -) -from dinov3.eval.helpers import args_dict_to_dataclass, cli_parser, write_results -from dinov3.eval.metrics import ClassificationMetricType, build_classification_metric -from dinov3.eval.setup import ModelConfig, load_model_and_context -from dinov3.eval.utils import average_metrics, evaluate, extract_features -from dinov3.eval.utils import save_results as default_save_results_func -from dinov3.run.init import job_context -from dinov3.utils.dtype import as_torch_dtype - -logger = logging.getLogger("dinov3") - - -RESULTS_FILENAME = "results-log-regression.csv" -MAIN_METRICS = ["top-1(_mean)?"] - - -try: - from sklearnex import patch_sklearn - - patch_sklearn() -except ImportError: - logger.warning("Can't import sklearnex. If installed, that speeds up scikit-learn 10-100x") - -try: - from sklearn.linear_model import LogisticRegression as sklearnLogisticRegression - from sklearn.multiclass import OneVsRestClassifier -except ImportError: - logger.warning("Can't import scikit-learn. This is necessary for evaluating log regression") - raise ImportError - - -C_POWER_RANGE = torch.linspace(-6, 5, 45) -_CPU_DEVICE = torch.device("cpu") - - -@dataclass -class TrainConfig: - dataset: str = MISSING # train dataset path - val_dataset: Optional[str] = None # val dataset path. If None, choose hyperparameters on 10% of the train set. - val_metric_type: ClassificationMetricType = ClassificationMetricType.MEAN_ACCURACY - batch_size: int = 256 # batch size for train and val set feature extraction - num_workers: int = 5 # number of workers for train and val set feature extraction - tol: float = 1e-12 # tolerance in logistic regression - train_features_device: str = "cpu" # device to gather train features (cpu, cuda, cuda:0, etc.) - train_dtype: str = "float64" # data type to convert the train features to - max_train_iters: int = 1_000 # maximum number of train iterations in logistic regression - - -@dataclass -class EvalConfig: - test_dataset: str = MISSING # test dataset path - batch_size: int | None = None # use train.batch_size if None - num_workers: int = 5 - test_metric_type: Optional[ClassificationMetricType] = None - - -@dataclass -class TransformConfig: - resize_size: int = CROP_DEFAULT_SIZE - crop_size: int = CROP_DEFAULT_SIZE - - -@dataclass -class FewShotConfig: - enable: bool = False # whether to use few-shot evaluation - k_or_percent: Optional[float] = None # number of elements or % to take per class - n_tries: int = 1 # number of tries for few-shot evaluation - - -@dataclass -class LogregEvalConfig: - model: ModelConfig - train: TrainConfig = field(default_factory=TrainConfig) - eval: EvalConfig = field(default_factory=EvalConfig) - transform: TransformConfig = field(default_factory=TransformConfig) - few_shot: FewShotConfig = field(default_factory=FewShotConfig) - save_results: bool = False # save predictions and targets in the output directory - output_dir: str = "" - - -class LogRegModule(nn.Module): - def __init__(self, C, multi_label=False, logreg_config=TrainConfig): - super().__init__() - self.dtype = as_torch_dtype(logreg_config.train_dtype) - self.device = torch.device(logreg_config.train_features_device) - assert self.device == _CPU_DEVICE, f"SKLearn can only work on CPU device, got {self.device}" - self.estimator = sklearnLogisticRegression( - penalty="l2", - solver="lbfgs", - C=C, - max_iter=logreg_config.max_train_iters, - n_jobs=-1, - tol=logreg_config.tol, - ) - if multi_label: - self.estimator = OneVsRestClassifier(self.estimator, n_jobs=-1) - - def forward(self, samples, targets): - samples_device = samples.device - samples = samples.to(dtype=self.dtype, device=self.device) - if self.device == _CPU_DEVICE: - samples = samples.numpy() - probas = self.estimator.predict_proba(samples) - return {"preds": torch.from_numpy(probas).to(samples_device), "target": targets} - - def fit(self, train_features, train_labels): - train_features = train_features.to(dtype=self.dtype, device=self.device) - train_labels = train_labels.to(dtype=self.dtype, device=self.device) - if self.device == _CPU_DEVICE: - # both cuml and sklearn only work with numpy arrays on CPU - train_features = train_features.numpy() - train_labels = train_labels.numpy() - self.estimator.fit(train_features, train_labels) - - -def evaluate_logreg_model(*, logreg_model, test_metric, test_data_loader, save_results_func=None): - key = "metrics" # We need only one key as we have only one metric - postprocessors, metrics = {key: logreg_model}, {key: test_metric} - _, eval_metrics, accumulated_results = evaluate( - nn.Identity(), - test_data_loader, - postprocessors, - metrics, - torch.cuda.current_device(), - accumulate_results=save_results_func is not None, - ) - if save_results_func is not None: - save_results_func(**accumulated_results[key]) - return eval_metrics - - -def train_for_C(*, C, train_features, train_labels, logreg_config: TrainConfig): - logreg_model = LogRegModule(C, multi_label=len(train_labels.shape) > 1, logreg_config=logreg_config) - logreg_model.fit(train_features, train_labels) - return logreg_model - - -def sweep_C_values( - *, - train_features, - train_labels, - val_data_loader, - val_metric, - logreg_config: TrainConfig, -): - metric_tracker = MetricTracker(val_metric, maximize=True) - ALL_C = 10**C_POWER_RANGE - logreg_models: Dict[float, Any] = {} - - train_features_device = torch.device(logreg_config.train_features_device) - train_dtype = as_torch_dtype(logreg_config.train_dtype) - train_features = train_features.to(dtype=train_dtype, device=train_features_device) - train_labels = train_labels.to(device=train_features_device) - - for i in range(get_rank(), len(ALL_C), get_world_size()): - C = ALL_C[i].item() - logger.info( - f"Training for C = {C:.4g}, dtype={train_dtype}, " - f"features: {train_features.shape}, {train_features.dtype}, " - f"labels: {train_labels.shape}, {train_labels.dtype}" - ) - logreg_models[C] = train_for_C( - C=C, - train_features=train_features, - train_labels=train_labels, - logreg_config=logreg_config, - ) - - gather_list: List[Dict[float, Any]] = [{} for _ in range(get_world_size())] - torch.distributed.all_gather_object(gather_list, logreg_models) - - for logreg_dict in gather_list: - logreg_models.update(logreg_dict) - gather_list.clear() - - for i in range(len(ALL_C)): - metric_tracker.increment() - C = ALL_C[i].item() - evals = evaluate_logreg_model( - logreg_model=logreg_models.pop(C), - test_metric=metric_tracker, - test_data_loader=val_data_loader, - ) - logger.info(f"Trained for C = {C:.4g}, accuracies = {evals}") - best_stats, which_epoch = metric_tracker.best_metric(return_step=True) - best_stats_100 = {k: 100.0 * v for k, v in best_stats.items()} - if which_epoch["top-1"] == i: - best_C = C - logger.info(f"Sweep best {best_stats_100}, best C = {best_C:.4g}") - - return best_stats, best_C - - -def make_logreg_data_loader(batch_size: int, num_workers: int, features: torch.Tensor, labels: torch.Tensor): - return make_data_loader( - dataset=DatasetWithEnumeratedTargets( - TensorDataset(features, labels), pad_dataset=True, num_replicas=get_world_size() - ), - batch_size=batch_size, - num_workers=num_workers, - sampler_type=SamplerType.DISTRIBUTED, - drop_last=False, - shuffle=False, - ) - - -def get_best_logreg_with_features( - *, - train_features: torch.Tensor, - train_labels: torch.Tensor, - val_features: torch.Tensor, - val_labels: torch.Tensor, - val_metric, - concatenate_train_val: bool, - train_config: TrainConfig, -): - val_data_loader = make_logreg_data_loader( - train_config.batch_size, train_config.num_workers, val_features, val_labels - ) - _, best_C_t = sweep_C_values( - train_features=train_features, - train_labels=train_labels, - val_data_loader=val_data_loader, - val_metric=val_metric, - logreg_config=train_config, - ) - if concatenate_train_val: - logger.info("Best parameter found, concatenating features") - train_features = torch.cat((train_features, val_features)) - train_labels = torch.cat((train_labels, val_labels)) - - logger.info("Training final model") - - logreg_model = train_for_C( - C=best_C_t, - logreg_config=train_config, - train_features=train_features, - train_labels=train_labels, - ) - return logreg_model - - -def make_transform(config: TransformConfig): - if config.resize_size / config.crop_size != 1: - logger.warning(f"Default resize / crop ratio is 1, here we have {config.resize_size} / {config.crop_size}") - transform = make_classification_eval_transform(resize_size=config.resize_size, crop_size=config.crop_size) - return transform - - -def make_train_val_datasets(train_config: TrainConfig, few_shot_config: FewShotConfig, transform): - train_dataset = make_dataset( - dataset_str=train_config.dataset, - transform=transform, - target_transform=get_target_transform(train_config.dataset), - ) - if train_config.val_dataset is not None: - val_dataset = make_dataset( - dataset_str=train_config.val_dataset, - transform=transform, - target_transform=get_target_transform(train_config.val_dataset), - ) - else: - split_percentage = 0.01 if few_shot_config.enable else 0.1 - train_dataset, val_dataset = split_train_val_datasets(train_dataset, split_percentage=split_percentage) - - train_dataset_dict = create_train_dataset_dict( - train_dataset, - few_shot_eval=few_shot_config.enable, - few_shot_k_or_percent=few_shot_config.k_or_percent, - few_shot_n_tries=few_shot_config.n_tries, - ) - num_classes = get_num_classes(train_dataset) - return train_dataset_dict, val_dataset, num_classes - - -def make_test_dataset_and_data_loader(model, config: EvalConfig, transform, gather_on_cpu: bool): - test_dataset = make_dataset( - dataset_str=config.test_dataset, - transform=transform, - target_transform=get_target_transform(config.test_dataset), - ) - test_features, test_labels = extract_features( - model, test_dataset, config.batch_size, config.num_workers, gather_on_cpu=gather_on_cpu - ) - assert isinstance(config.batch_size, int) # eval batch size has been replaced by train batch size if None - test_data_loader = make_logreg_data_loader(config.batch_size, config.num_workers, test_features, test_labels) - return test_dataset, test_data_loader - - -def eval_log_regression_with_model(*, model: torch.nn.Module, autocast_dtype, config: LogregEvalConfig): - """ - Implements the "standard" process for log regression evaluation: - The value of C is chosen by training on train_dataset and evaluating on - val_dataset. Then, the final model is trained on a concatenation of - train_dataset and val_dataset, and is evaluated on test_dataset. - If there is no val_dataset, the value of C is the one that yields - the best results on a random 10% subset of the train dataset - """ - start = time.time() - cudnn.benchmark = True - - transform = make_transform(config.transform) - config.eval.batch_size = config.eval.batch_size or config.train.batch_size # use train batch size for eval if None - - # Setting up train and val datasets - train_dataset_dict, val_dataset, num_classes = make_train_val_datasets(config.train, config.few_shot, transform) - - # Extracting features - with torch.autocast("cuda", dtype=autocast_dtype): - gather_on_cpu = torch.device(config.train.train_features_device) == _CPU_DEVICE - train_data_dict = extract_features_for_dataset_dict( - model, train_dataset_dict, config.train.batch_size, config.train.num_workers, gather_on_cpu=gather_on_cpu - ) - logger.info("Choosing hyperparameters on the val dataset") - val_features, val_labels = extract_features( - model, val_dataset, config.train.batch_size, config.train.num_workers, gather_on_cpu=gather_on_cpu - ) - test_dataset, test_data_loader = make_test_dataset_and_data_loader(model, config.eval, transform, gather_on_cpu) - - # Moves the model to cpu in-place. Deleting the variable would only delete a reference and not free any space. - model.cpu() # all features are extracted, we won't use the backbone anymore - torch.cuda.empty_cache() - - # Setting up metrics - val_metric = build_classification_metric(config.train.val_metric_type, num_classes=num_classes, dataset=val_dataset) - test_metric_type = config.eval.test_metric_type or config.train.val_metric_type - test_metric = build_classification_metric(test_metric_type, num_classes=num_classes, dataset=test_dataset) - - # Setting up save results function - save_results_func = None - if config.save_results: - save_results_func = partial(default_save_results_func, output_dir=config.output_dir) - - results_dict = {} - for _try in train_data_dict.keys(): - logreg_model = get_best_logreg_with_features( - train_features=train_data_dict[_try]["train_features"], - train_labels=train_data_dict[_try]["train_labels"], - val_features=val_features, - val_labels=val_labels, - val_metric=val_metric, - concatenate_train_val=not config.few_shot.enable, - train_config=config.train, - ) - if len(train_data_dict) > 1 and save_results_func is not None: # add suffix - split_results_saver = partial(save_results_func, filename_suffix=str(_try)) - else: - split_results_saver = save_results_func # type: ignore - - eval_metrics = evaluate_logreg_model( - logreg_model=logreg_model, - test_metric=test_metric.clone(), - test_data_loader=test_data_loader, - save_results_func=split_results_saver, - ) - results_dict[_try] = {k: v.item() * 100.0 for k, v in eval_metrics["metrics"].items()} - - if len(train_data_dict) > 1: - results_dict = average_metrics(results_dict) - else: - results_dict = {**results_dict[_try]} - - logger.info(f"Log regression evaluation done in {int(time.time() - start)}s") - logger.info("Training of the supervised logistic regression on frozen features completed.") - results_string = "\n".join([f"{k}: {results_dict[k]:.4g}" for k in sorted(results_dict.keys())]) - logger.info("Results:\n" + results_string) - - torch.distributed.barrier() - return results_dict - - -def benchmark_launcher(eval_args: dict[str, object]) -> dict[str, Any]: - """Initialization of distributed and logging are preconditions for this method""" - dataclass_config, output_dir = args_dict_to_dataclass(eval_args=eval_args, config_dataclass=LogregEvalConfig) - model, model_context = load_model_and_context(dataclass_config.model, output_dir=output_dir) - results_dict = eval_log_regression_with_model( - model=model, config=dataclass_config, autocast_dtype=model_context["autocast_dtype"] - ) - write_results(results_dict, output_dir, RESULTS_FILENAME) - return results_dict - - -def main(argv=None): - if argv is None: - argv = sys.argv[1:] - eval_args = cli_parser(argv) - with job_context(output_dir=eval_args["output_dir"]): - benchmark_launcher(eval_args=eval_args) - return 0 - - -if __name__ == "__main__": - main() diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/metrics/__init__.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/metrics/__init__.py deleted file mode 100644 index 7d76da62937bd31345524fd683bd964757651d55..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/metrics/__init__.py +++ /dev/null @@ -1,14 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -from .classification import ( - AveragingMethod, - ClassificationMetricType, - MacroAveragedMeanReciprocalRank, - MeanAveragePrecisionVOC2007, - accuracy, - build_classification_metric, - build_topk_accuracy_metric, -) diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/metrics/classification.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/metrics/classification.py deleted file mode 100644 index 4a6ee25c24827005f6890c486c0eb16b43a5cd65..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/metrics/classification.py +++ /dev/null @@ -1,325 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import logging -from enum import Enum -from typing import Any, Dict, Optional - -import numpy as np -import torch -from torch import Tensor -from torchmetrics import Metric, MetricCollection -from torchmetrics.classification import ( - MulticlassAccuracy, - MulticlassAUROC, - MulticlassF1Score, - MulticlassRecall, - MultilabelAveragePrecision, - MultilabelF1Score, - MultilabelPrecisionRecallCurve, -) -from torchmetrics.utilities.data import dim_zero_cat, select_topk - -from .imagenet_c import ImageNet_C_Metric - -logger = logging.getLogger("dinov3") - - -class ClassificationMetricType(Enum): - AUROC = "auroc" - MEAN_ACCURACY = "mean_accuracy" - MEAN_RECALL = "mean_recall" - MEAN_PER_CLASS_ACCURACY = "mean_per_class_accuracy" - MEAN_PER_CLASS_RECALL = "mean_per_class_recall" - PER_CLASS_ACCURACY = "per_class_accuracy" - MEAN_AVERAGE_PRECISION_VOC_2007 = "map_voc2007" - ANY_MATCH_ACCURACY = "any_match_accuracy" - GROUPBY_ANY_MATCH_ACCURACY_1 = "groupby_any_match_accuracy_1" - GROUPBY_ANY_MATCH_ACCURACY_5 = "groupby_any_match_accuracy_5" - MEAN_MULTICLASS_F1 = "mean_multiclass_f1" - MEAN_PER_CLASS_MULTICLASS_F1 = "mean_per_class_multiclass_f1" - MEAN_MULTILABEL_F1 = "mean_multilabel_f1" - MEAN_PER_CLASS_MULTILABEL_F1 = "mean_per_class_multilabel_f1" - IMAGENET_C_METRIC = "imagenet_c_metric" - MACRO_AVERAGED_MEAN_RECIPROCAL_RANK = "macro_averaged_mean_reciprocal_rank" - MACRO_MULTILABEL_AVERAGE_PRECISION = "macro_multilabel_average_precision" - - @property - def averaging_method(self): - return getattr(AveragingMethod, self.name, None) - - @property - def is_topk_accuracy_metric(self): - return self.value in ("mean_accuracy", "mean_per_class_accuracy", "per_class_accuracy") - - @property - def is_topk_recall_metric(self): - return self.value in ("mean_recall", "mean_per_class_recall") - - @property - def is_multilabel(self): - return self.value in ( - "map_voc2007", - "any_match_accuracy", - "groupby_any_match_accuracy_1", - "groupby_any_match_accuracy_5", - "mean_multilabel_f1", - "mean_per_class_multilabel_f1", - ) - - def __str__(self): - return self.value - - -class AveragingMethod(Enum): - MEAN_ACCURACY = "micro" - MEAN_RECALL = "micro" - MEAN_PER_CLASS_ACCURACY = "macro" - MEAN_PER_CLASS_RECALL = "macro" - PER_CLASS_ACCURACY = "none" - MEAN_MULTICLASS_F1 = "micro" - MEAN_PER_CLASS_MULTICLASS_F1 = "macro" - MEAN_MULTILABEL_F1 = "micro" - MEAN_PER_CLASS_MULTILABEL_F1 = "macro" - - def __str__(self): - return self.value - - -def _make_default_ks(num_classes: int): - return (1, 5) if num_classes >= 5 else (1,) - - -def build_classification_metric( - metric_type: ClassificationMetricType, *, num_classes: int, ks: Optional[tuple] = None, dataset=None -): - if metric_type.is_topk_accuracy_metric: - ks = ks or _make_default_ks(num_classes) - return build_topk_accuracy_metric(average_type=metric_type.averaging_method, num_classes=num_classes, ks=ks) - elif metric_type.is_topk_recall_metric: - ks = ks or _make_default_ks(num_classes) - return build_topk_recall_metric(average_type=metric_type.averaging_method, num_classes=num_classes, ks=ks) - elif metric_type == ClassificationMetricType.MEAN_AVERAGE_PRECISION_VOC_2007: - assert ks is None - map_voc2007 = MeanAveragePrecisionVOC2007(num_labels=int(num_classes)) - return MetricCollection({"top-1": map_voc2007}) - elif metric_type == ClassificationMetricType.ANY_MATCH_ACCURACY: - ks = ks or _make_default_ks(num_classes) - return build_topk_any_match_accuracy_metric(num_classes=num_classes, ks=ks) - elif metric_type == ClassificationMetricType.GROUPBY_ANY_MATCH_ACCURACY_1: - return GroupByAnyMatchAccuracy(top_k=1, num_classes=int(num_classes), dataset=dataset) - elif metric_type == ClassificationMetricType.GROUPBY_ANY_MATCH_ACCURACY_5: - return GroupByAnyMatchAccuracy(top_k=5, num_classes=int(num_classes), dataset=dataset) - elif metric_type == ClassificationMetricType.IMAGENET_C_METRIC: - return ImageNet_C_Metric() - elif metric_type == ClassificationMetricType.AUROC: - return MetricCollection({"top-1": MulticlassAUROC(num_classes=int(num_classes))}) - elif metric_type == ClassificationMetricType.MACRO_MULTILABEL_AVERAGE_PRECISION: - return MetricCollection({"top-1": MultilabelAveragePrecision(num_labels=int(num_classes), average="macro")}) - - elif metric_type in ( - ClassificationMetricType.MEAN_MULTICLASS_F1, - ClassificationMetricType.MEAN_PER_CLASS_MULTICLASS_F1, - ): - return MetricCollection( - {"top-1": MulticlassF1Score(num_classes=int(num_classes), average=metric_type.averaging_method.value)} - ) - elif metric_type in ( - ClassificationMetricType.MEAN_MULTILABEL_F1, - ClassificationMetricType.MEAN_PER_CLASS_MULTILABEL_F1, - ): - return MetricCollection( - {"top-1": MultilabelF1Score(num_labels=int(num_classes), average=metric_type.averaging_method.value)} - ) - elif metric_type == ClassificationMetricType.MACRO_AVERAGED_MEAN_RECIPROCAL_RANK: - return MetricCollection({"top-1": MacroAveragedMeanReciprocalRank(num_classes=int(num_classes))}) - raise ValueError(f"Unknown metric type {metric_type}") - - -def build_topk_accuracy_metric(average_type: AveragingMethod, num_classes: int, ks: tuple = (1, 5)): - metrics: Dict[str, Metric] = { - f"top-{k}": MulticlassAccuracy(top_k=k, num_classes=int(num_classes), average=average_type.value) for k in ks - } - return MetricCollection(metrics) - - -def build_topk_recall_metric(average_type: AveragingMethod, num_classes: int, ks: tuple = (1, 5)): - metrics: Dict[str, Metric] = { - f"top-{k}": MulticlassRecall(top_k=k, num_classes=int(num_classes), average=average_type.value) for k in ks - } - return MetricCollection(metrics) - - -def build_topk_any_match_accuracy_metric(num_classes: int, ks: tuple = (1, 5)): - metrics: Dict[str, Metric] = {f"top-{k}": AnyMatchAccuracy(top_k=k, num_classes=int(num_classes)) for k in ks} - return MetricCollection(metrics) - - -class MeanAveragePrecisionVOC2007(MultilabelPrecisionRecallCurve): - """ - VOC2007 11-points mAP Evaluation defined on page 11 of - The PASCAL Visual Object Classes (VOC) Challenge (Everingham et al., 2010) - """ - - def __init__(self, *args, recall_level_count: int = 11, **kwargs): - super().__init__(*args, **kwargs) - self.recall_thresholds = torch.linspace(0, 1, recall_level_count) - - def compute(self): - precision, recall, _ = super().compute() - interpolated_precisions = torch.stack( - [torch.max(precision[i][recall[i] >= r]) for r in self.recall_thresholds for i in range(len(precision))] - ) - return torch.mean(interpolated_precisions) - - -class AnyMatchAccuracy(Metric): - """ - This computes an accuracy where an element is considered correctly - predicted if one of the predictions is in a list of targets - """ - - is_differentiable: bool = False - higher_is_better: Optional[bool] = None - full_state_update: bool = False - - def __init__( - self, - num_classes: int, - top_k: int = 1, - **kwargs: Any, - ) -> None: - super().__init__(**kwargs) - self.num_classes = num_classes - self.top_k = top_k - self.add_state("tp", [], dist_reduce_fx="cat") - - def update(self, preds: Tensor, target: Tensor) -> None: # type: ignore - # preds [B, D] - # target [B, A] - # preds_oh [B, D] with 0 and 1 - # select top K highest probabilities, use one hot representation - preds_oh = select_topk(preds, self.top_k) - # target_oh [B, D + 1] with 0 and 1 - target_oh = torch.zeros((preds_oh.shape[0], preds_oh.shape[1] + 1), device=target.device, dtype=torch.int32) - target = target.long() - # for undefined targets (-1) use a fake value `num_classes` - target[target == -1] = self.num_classes - # fill targets, use one hot representation - target_oh.scatter_(1, target, 1) - # target_oh [B, D] (remove the fake target at index `num_classes`) - target_oh = target_oh[:, :-1] - # tp [B] with 0 and 1 - tp = (preds_oh * target_oh == 1).sum(dim=1) - # at least one match between prediction and target - tp.clip_(max=1) - # ignore instances where no targets are defined - mask = target_oh.sum(dim=1) > 0 - tp = tp[mask] - self.tp.append(tp) # type: ignore - - def compute(self) -> Tensor: - tp = dim_zero_cat(self.tp) # type: ignore - return tp.float().mean() - - -class GroupByAnyMatchAccuracy(AnyMatchAccuracy): - def __init__( - self, - dataset, - **kwargs: Any, - ) -> None: - super().__init__(**kwargs) - assert hasattr(dataset, "get_groupby_labels"), "The dataset should have a `get_groupby_labels` method" - self._groupby_labels: Dict[str, np.ndarray] = dataset.get_groupby_labels() - assert hasattr(dataset, "get_mapped_targets"), "The dataset should have a `get_mapped_targets` method" - self._mapped_targets: torch.Tensor = torch.from_numpy(dataset.get_mapped_targets()) - self.add_state("indices", [], dist_reduce_fx="cat") - - def update(self, preds: Tensor, target: Tensor) -> None: - self.indices.append(target) # target are indices in this case - super().update(preds, self._mapped_targets[target.tolist()].to(preds.device)) - - def groupby_metric(self, variable: np.ndarray, indices: np.ndarray, tp: torch.Tensor) -> Dict[Any, Tensor]: - groubpy_dict = {} - for v in set(variable): - index = np.where(variable[indices] == v)[0] - groubpy_dict[v] = tp[index].mean() - return groubpy_dict - - def compute(self) -> Tensor: - tp = dim_zero_cat(self.tp).float() # type: ignore - indices = dim_zero_cat(self.indices).cpu().numpy() # type: ignore - global_score = tp.mean() - results_dict = {"top-1": global_score} - for label_name, label_value in self._groupby_labels.items(): - groupby_results = self.groupby_metric(label_value, indices, tp) - printable_results = {k: f"{100. * v.item():.4g}" for k, v in groupby_results.items()} - logger.info(f"Scores by {label_name} {printable_results}\n") - results_dict = {**results_dict, **groupby_results} - return results_dict - - -class MacroAveragedMeanReciprocalRank(Metric): - """ - This computes the macro average mean reciprocal rank metric. - Rank is defined as the position at which the target label is found when - we sort the prediction scores from most probable label to least probable - The reciprocal of the rank (1 / rank) which lies in [0, 1] gives a measure on how well the model does. - the higher the rank the better the model. The reciprocal rank of each sample is aggregated by the target - label and we sum those aggregates groupby the target labels. This quantity is divided by the number of - samples per label which gives as per label or macro reciprocal rank performance. This per label metric is - avergaed across all the labels to get the macro averaged mean reciprocal rank metric. This metric is - useful when we have label imbalance and we want to give equal importance to rare labels as well as frequent labels. - """ - - is_differentiable: bool = False - higher_is_better: Optional[bool] = None - full_state_update: bool = False - - def __init__( - self, - num_classes: int, - **kwargs: Any, - ) -> None: - super().__init__(**kwargs) - self.num_classes = num_classes - self.add_state("per_class_mrr", default=torch.zeros(self.num_classes, dtype=torch.float), dist_reduce_fx="sum") - self.add_state( - "per_class_num_samples", default=torch.zeros(self.num_classes, dtype=torch.float), dist_reduce_fx="sum" - ) - - def update(self, preds: Tensor, target: torch.LongTensor) -> None: # type: ignore - # preds: FloatTensor [B, num_classes] - # target: LongTensor [B] target labels - # ranks: [] - rank_scores = 1 / (preds >= preds.gather(1, target[:, None].expand_as(preds))).sum(dim=1) - - unique_targets = target.unique().tolist() - target_remap = {key: val for val, key in enumerate(unique_targets)} - target_inv_remap = {val: key for val, key in enumerate(unique_targets)} - remaped_targets = torch.LongTensor(list(map(target_remap.get, target.tolist()))).to(target.device) - unique_remaped_targets, remaped_target_count = remaped_targets.unique(sorted=True, return_counts=True) - sum_rank_scores = torch.zeros_like(unique_remaped_targets, dtype=torch.float).scatter_add_( - 0, remaped_targets, rank_scores - ) - unique_targets = torch.LongTensor(list(map(target_inv_remap.get, unique_remaped_targets.tolist()))).to( - target.device - ) - self.per_class_mrr.index_add_(0, unique_targets, sum_rank_scores) - self.per_class_num_samples.index_add_(0, unique_targets, remaped_target_count.float()) - - def compute(self) -> Tensor: - return (self.per_class_mrr / (self.per_class_num_samples + 1e-6)).mean() - - -def accuracy(output, target, topk=(1,)): - """Computes the accuracy over the k top predictions for the specified values of k""" - maxk = max(topk) - batch_size = target.size(0) - _, pred = output.topk(maxk, 1, True, True) - pred = pred.t() - correct = pred.eq(target.reshape(1, -1).expand_as(pred)) - return [correct[:k].reshape(-1).float().sum(0) * 100.0 / batch_size for k in topk] diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/metrics/imagenet_c.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/metrics/imagenet_c.py deleted file mode 100644 index 5ab201d24db418c4256738b2a6c7d8c307a47bba..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/metrics/imagenet_c.py +++ /dev/null @@ -1,225 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import logging -from typing import Any, Dict, Optional - -import numpy as np -import torch -from torch import Tensor -from torchmetrics import Metric - -logger = logging.getLogger("dinov3") - - -# corruption type (str) -> level (int) -> score (float) -Scores = Dict[str, Dict[int, float]] -# corruption type (str) -> score (float) -AverageScores = Dict[str, float] - - -ALEXNET_INVERSE_SCORES: Scores = { - "GAUSSIAN_NOISE": { - 1: 0.69528, - 2: 0.82542, - 3: 0.93554, - 4: 0.98138, - 5: 0.99452, - }, - "SHOT_NOISE": { - 1: 0.71224, - 2: 0.85108, - 3: 0.93574, - 4: 0.98182, - 5: 0.99146, - }, - "IMPULSE_NOISE": { - 1: 0.78374, - 2: 0.89808, - 3: 0.9487, - 4: 0.9872, - 5: 0.99548, - }, - "DEFOCUS_BLUR": { - 1: 0.656239999999999, - 2: 0.73202, - 3: 0.85036, - 4: 0.91364, - 5: 0.94714, - }, - "GLASS_BLUR": { - 1: 0.64308, - 2: 0.75054, - 3: 0.88806, - 4: 0.91622, - 5: 0.93344, - }, - "MOTION_BLUR": { - 1: 0.5843, - 2: 0.70048, - 3: 0.82108, - 4: 0.8975, - 5: 0.92638, - }, - "ZOOM_BLUR": { - 1: 0.70008, - 2: 0.769919999999999, - 3: 0.80784, - 4: 0.84198, - 5: 0.87198, - }, - "SNOW": { - 1: 0.71726, - 2: 0.88392, - 3: 0.86468, - 4: 0.9187, - 5: 0.94952, - }, - "FROST": { - 1: 0.6139, - 2: 0.797339999999999, - 3: 0.8879, - 4: 0.89942, - 5: 0.9343, - }, - "FOG": { - 1: 0.67474, - 2: 0.7605, - 3: 0.84378, - 4: 0.8726, - 5: 0.945, - }, - "BRIGHTNESS": { - 1: 0.4514, - 2: 0.48502, - 3: 0.54048, - 4: 0.62166, - 5: 0.724399999999999, - }, - "CONTRAST": { - 1: 0.64548, - 2: 0.7615, - 3: 0.88874, - 4: 0.9776, - 5: 0.9927, - }, - "ELASTIC_TRANSFORM": { - 1: 0.52596, - 2: 0.70116, - 3: 0.55686, - 4: 0.64076, - 5: 0.80554, - }, - "PIXELATE": { - 1: 0.52218, - 2: 0.5462, - 3: 0.737279999999999, - 4: 0.87092, - 5: 0.91262, - }, - "JPEG_COMPRESSION": { - 1: 0.510019999999999, - 2: 0.54718, - 3: 0.57294, - 4: 0.654579999999999, - 5: 0.74778, - }, - "SPECKLE_NOISE": { - 1: 0.66192, - 2: 0.7444, - 3: 0.90246, - 4: 0.94548, - 5: 0.97268, - }, - "GAUSSIAN_BLUR": { - 1: 0.54732, - 2: 0.70444, - 3: 0.82574, - 4: 0.89864, - 5: 0.9594, - }, - "SPATTER": { - 1: 0.47196, - 2: 0.621939999999999, - 3: 0.75052, - 4: 0.84132, - 5: 0.90182, - }, - "SATURATE": { - 1: 0.59342, - 2: 0.65514, - 3: 0.51174, - 4: 0.70834, - 5: 0.8226, - }, -} - -N_LEVELS = 5 -CORRUPTION_LEVEL_TO_ID = { - (k, level): i * N_LEVELS + level - 1 - for i, k in enumerate(sorted(ALEXNET_INVERSE_SCORES.keys())) - for level in range(1, 1 + N_LEVELS) -} -ID_TO_CORRUPTION_LEVEL = {i: k for k, i in CORRUPTION_LEVEL_TO_ID.items()} - - -def compute_relative_average_scores(scores: Scores, inv_scores_ref: Scores = ALEXNET_INVERSE_SCORES) -> AverageScores: - rel_scores = {} - for corruption_type in inv_scores_ref.keys(): - if corruption_type not in scores: - logger.info(f"No results for split {corruption_type}") - continue - inv_scores_for_type = [] - inv_scores_ref_for_type = [] - for level in range(1, 1 + N_LEVELS): - if level not in scores[corruption_type]: - continue - # append inverse score (confusion error) - inv_scores_for_type.append(1.0 - scores[corruption_type][level]) - inv_scores_ref_for_type.append(inv_scores_ref[corruption_type][level]) - rel_scores[corruption_type] = np.mean(inv_scores_for_type) / np.mean(inv_scores_ref_for_type) - - mce_score = np.mean([v for _, v in rel_scores.items()]) - return mce_score - - -class ImageNet_C_Metric(Metric): - - is_differentiable: bool = False - higher_is_better: Optional[bool] = False - full_state_update: bool = False - - def __init__(self, **kwargs: Any) -> None: - super().__init__(**kwargs) - - self.add_state("tp", torch.zeros(len(CORRUPTION_LEVEL_TO_ID)), dist_reduce_fx="sum") - self.add_state("total", torch.zeros(len(CORRUPTION_LEVEL_TO_ID)), dist_reduce_fx="sum") - - def update(self, preds: Tensor, target: Tensor) -> None: - from large_vision_dataset.datasets.image_net_c import CORRUPTION_TYPES - - target_labels, corruption_types, levels = target.unbind(1) - tps = torch.argmax(preds, dim=1) == target_labels.to(preds.device) - index = torch.tensor( - [ - CORRUPTION_LEVEL_TO_ID[(CORRUPTION_TYPES[ct].upper(), level.item())] - for ct, level in zip(corruption_types, levels) - ], - device=preds.device, - ) - self.total += torch.bincount(index, minlength=len(CORRUPTION_LEVEL_TO_ID)) - self.tp += torch.bincount(index, weights=tps, minlength=len(CORRUPTION_LEVEL_TO_ID)) - - def compute(self) -> Tensor: - flattened_scores = (self.tp / self.total).float().cpu().numpy() - scores: Scores = {} - for i, score in enumerate(flattened_scores): - corruption_type, level = ID_TO_CORRUPTION_LEVEL[i] - if corruption_type not in scores: - scores[corruption_type] = {} - scores[corruption_type][level] = score - - mce_score = compute_relative_average_scores(scores) - return {"top-1": torch.tensor(mce_score, device=self.tp.device)} diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/results.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/results.py deleted file mode 100644 index cc8448197d73597dd031ebf110b6630936a0684b..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/results.py +++ /dev/null @@ -1,248 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import json -import logging -import os -from contextlib import nullcontext -from enum import Enum -from os import PathLike -from typing import IO, Any, Callable, Dict, List, Optional, Sequence, Union - -import pandas as pd -import yaml # type: ignore - -logger = logging.getLogger("dinov3") - - -# This type represents a list of results, e.g. baselines for an evaluation. -Results: Any = pd.DataFrame - -try: - import openpyxl # noqa: 401 - - HAS_OPENPYXL = True -except ImportError: - HAS_OPENPYXL = False - logger.warning("can't import openpyxl package") - - -PathOrFileLikeObject = Union[str, PathLike, IO] - - -class FileFormat(Enum): - CSV = "csv" - JSON_LINES = "json-lines" - EXCEL = "excel" - YAML = "yaml" - - @staticmethod - def guess(path: Union[str, PathLike]) -> "FileFormat": - _, ext = os.path.splitext(path) - supported_exts = { - ".csv": FileFormat.CSV, - ".jsonl": FileFormat.JSON_LINES, - ".excel": FileFormat.EXCEL, - ".yaml": FileFormat.YAML, - } - if ext not in supported_exts: - raise ValueError(f"Passed path has extension {ext}, only {list(supported_exts.keys())} are supported.") - return supported_exts[ext] - - -_INT_DTYPES = [ - pd.Int8Dtype(), - pd.Int16Dtype(), - pd.Int32Dtype(), - pd.UInt8Dtype(), - pd.UInt16Dtype(), - pd.UInt32Dtype(), - pd.Int64Dtype(), -] - -_FLOAT_DTYPES = [ - pd.Float32Dtype(), - pd.Float64Dtype(), -] - -_TO_STRING_DTYPES = [ - pd.BooleanDtype(), -] - -_VALID_DTYPES = [ - pd.StringDtype(), - pd.Int64Dtype(), - pd.Float64Dtype(), -] - - -def _map_dtypes(results: Results) -> Results: - results = results.convert_dtypes( - infer_objects=True, - convert_string=True, - convert_integer=True, - convert_boolean=True, - convert_floating=True, - ) - for column_name in results.columns: - if results.dtypes[column_name] in _INT_DTYPES: - results[column_name] = results[column_name].astype(pd.Int64Dtype()) - elif results.dtypes[column_name] in _FLOAT_DTYPES: - results[column_name] = results[column_name].astype(pd.Float64Dtype()) - elif results.dtypes[column_name] in _TO_STRING_DTYPES: - results[column_name] = results[column_name].astype(pd.StringDtype()) - - return results - - -def _validate_column(results: Results, *, name: str, dtype: Union[str, type]) -> bool: - try: - loc = results.columns.get_loc(name) - except KeyError: - return False - return results.dtypes[loc] == dtype - - -def _validate(results: Results) -> bool: - for column_name in results.columns: - dtype = results.dtypes[column_name] - if dtype not in _VALID_DTYPES: - return False - - return True - - -def _assert_valid_dtypes(results: Results) -> None: - assert _validate(results), f"All dtypes from {results.dtypes} must be in {_VALID_DTYPES}" - - -Scalar = Union[str, int, float] - - -def _map_scalar(x: Scalar) -> List[Scalar]: - return [x] - - -def _map_scalar_list(x: List[Scalar]) -> List[Scalar]: - return x - - -def make(data: Dict[str, Union[str, int, float]]) -> Results: - """Construct results from a dictionary of scalars or lists of scalars.""" - - map_value: Callable[..., List[Scalar]] - if all((isinstance(value, Sequence) for key, value in data.items())): - map_value = _map_scalar_list - else: - map_value = _map_scalar - results = pd.DataFrame({key: map_value(value) for key, value in data.items()}) - results = _map_dtypes(results) - _assert_valid_dtypes(results) - return results - - -def vstack(*results_sequence: Sequence[Results]) -> Results: - """Concatenate (vertically) results.""" - - return pd.concat(results_sequence, axis=0, ignore_index=True) - - -def load(f: PathOrFileLikeObject, file_format: Optional[FileFormat] = None) -> Results: - """Load results from a file via a path-like object or from a file-like object.""" - - if isinstance(f, (str, PathLike)): - file_format = FileFormat.guess(f) - elif file_format is None: - raise ValueError("No file format specified for file-like object") - - assert file_format is not None - if file_format == FileFormat.CSV: - results = pd.read_csv(f, sep=",", na_values="", header=0) - elif file_format == FileFormat.JSON_LINES: - results = pd.read_json(f, lines=True) - elif file_format == FileFormat.EXCEL: - results = pd.read_excel(f) - elif file_format == FileFormat.YAML: - with open(f) as file: # type: ignore - results = pd.DataFrame.from_dict(yaml.safe_load(file), orient="index") - else: - raise ValueError("Unsupported file format: {file_format}") - - results = _map_dtypes(results) - _assert_valid_dtypes(results) - return results - - -def load_collection(f: PathOrFileLikeObject) -> Dict[str, Results]: - """Load a collection of results from a file via a path-like object or from a file-like object.""" - - results_collection = pd.read_excel(f, sheet_name=None) - - for sheet_name, results in results_collection.items(): - results = _map_dtypes(results) - _assert_valid_dtypes(results) - results_collection[sheet_name] = results - return results_collection - - -def save( - results: Sequence[Results], - f: PathOrFileLikeObject, - file_format: Optional[FileFormat] = None, -) -> None: - """Save results to a file via a path-like object or to a file-like object.""" - - _assert_valid_dtypes(results) - - if isinstance(f, (str, PathLike)): - file_format = FileFormat.guess(f) - elif file_format is None: - raise ValueError("No file format specified for file-like object") - - assert file_format is not None - if file_format == FileFormat.CSV: - results.to_csv(f, index=False, header=True, sep=",", na_rep="") # type: ignore - elif file_format == FileFormat.JSON_LINES: - # NOTE: pandas escapes '/' characters - s = results.to_json(orient="records", lines=True, indent=None) # type: ignore - if isinstance(f, (str, PathLike)): - context = open(f, "w") # type: ignore - else: - context = nullcontext(enter_result=f) # type: ignore - with context as f: - for line in s.splitlines(): - line = json.dumps(json.loads(line), separators=(",", ":")) - f.write(line + "\n") - elif file_format == FileFormat.EXCEL: - results.to_excel(f, header=True, index=False, na_rep="") # type: ignore - elif file_format == FileFormat.YAML: - with open(f, "w") as fp: # type: ignore - yaml.safe_dump(results.to_dict(orient="index"), fp, default_flow_style=False) # type: ignore - else: - raise ValueError("Unsupported file format: {file_format}") - - -def save_from_dict( - results_dict: Dict[str, Union[str, int, float]], - results_path: PathOrFileLikeObject, -) -> None: - results = make(results_dict) - save(results, results_path) - - -def save_collection( - results_collection: Dict[str, Results], - f: PathOrFileLikeObject, -) -> None: - """Save a collection of results to a file via a path-like object or to a file-like object.""" - - if not HAS_OPENPYXL: - logger.warning("openpyxl need to be installed, passing...") - return - - with pd.ExcelWriter(f, engine="openpyxl", mode="w") as writer: - for sheet_name, results in results_collection.items(): - _assert_valid_dtypes(results) - results.to_excel(writer, sheet_name=sheet_name, header=True, index=False, na_rep="") diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/config.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/config.py deleted file mode 100644 index 70db1260a220a3808f160095dda97b845af600f8..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/config.py +++ /dev/null @@ -1,125 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -from dataclasses import dataclass, field -from enum import Enum -from omegaconf import MISSING -from typing import Any - -import torch - -from dinov3.data.transforms import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD -from dinov3.eval.segmentation.models import BackboneLayersSet -from dinov3.eval.setup import ModelConfig - - -DEFAULT_MEAN = tuple(mean * 255 for mean in IMAGENET_DEFAULT_MEAN) -DEFAULT_STD = tuple(std * 255 for std in IMAGENET_DEFAULT_STD) - - -class ModelDtype(Enum): - FLOAT32 = "float32" - BFLOAT16 = "bfloat16" - - @property - def autocast_dtype(self): - return { - ModelDtype.BFLOAT16: torch.bfloat16, - ModelDtype.FLOAT32: torch.float32, - }[self] - - -@dataclass -class OptimizerConfig: - lr: float = 1e-3 - beta1: float = 0.9 - beta2: float = 0.999 - weight_decay: float = 1e-2 - gradient_clip: float = 35.0 - - -@dataclass -class SchedulerConfig: - type: str = "WarmupOneCycleLR" - total_iter: int = 40_000 # Total number of iterations for training - constructor_kwargs: dict[str, Any] = field(default_factory=dict) - - -@dataclass -class DatasetConfig: - root: str = MISSING # Path to the dataset folder - train: str = "" # Dataset descriptor, e.g. "ADE20K:split=TRAIN" - val: str = "" - - -@dataclass -class DecoderConfig: - type: str = "m2f" # Decoder type must be one of [linear, m2f] - backbone_out_layers: BackboneLayersSet = BackboneLayersSet.LAST - use_batchnorm: bool = True - use_cls_token: bool = False - use_backbone_norm: bool = True # Uses the backbone's output normalization on all layers - num_classes: int = 150 # Number of segmentation classes - hidden_dim: int = 2048 # Hidden dimension, only used for M2F head - - -@dataclass -class TrainConfig: - diceloss_weight: float = 0.0 - celoss_weight: float = 1.0 - - -@dataclass -class TrainTransformConfig: - img_size: Any = None - random_img_size_ratio_range: tuple[float] | None = None - crop_size: tuple[int] | None = None - flip_prob: float = 0.0 - - -@dataclass -class EvalTransformConfig: - img_size: Any = None - tta_ratios: tuple[float] = (1.0,) - - -@dataclass -class TransformConfig: - train: TrainTransformConfig = field(default_factory=TrainTransformConfig) - eval: EvalTransformConfig = field(default_factory=EvalTransformConfig) - mean: tuple[float] = DEFAULT_MEAN - std: tuple[float] = DEFAULT_STD - - -@dataclass -class EvalConfig: - compute_metric_per_image: bool = False - reduce_zero_label: bool = True # For ADE20K, ignores 0 label (=background/unlabeled) - mode: str = "slide" - crop_size: int | None = 512 - stride: int | None = 341 - eval_interval: int = 40000 - use_tta: bool = False # apply test-time augmentation at evaluation time - - -@dataclass -class SegmentationConfig: - model: ModelConfig | None = None # config of the DINOv3 backbone - bs: int = 2 - n_gpus: int = 8 - num_workers: int = 6 # number of workers to use / GPU - model_dtype: ModelDtype = ModelDtype.FLOAT32 - seed: int = 100 - datasets: DatasetConfig = field(default_factory=DatasetConfig) - metric_to_save: str = "mIoU" # Name of the metric to save - decoder_head: DecoderConfig = field(default_factory=DecoderConfig) - scheduler: SchedulerConfig = field(default_factory=SchedulerConfig) - optimizer: OptimizerConfig = field(default_factory=OptimizerConfig) - transforms: TransformConfig = field(default_factory=TransformConfig) - train: TrainConfig = field(default_factory=TrainConfig) - eval: EvalConfig = field(default_factory=EvalConfig) - # Additional Parameters - output_dir: str | None = None - load_from: str | None = None # path to .pt checkpoint to resume training from or evaluate from diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/configs/config-ade20k-linear-training.yaml b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/configs/config-ade20k-linear-training.yaml deleted file mode 100644 index 93e9bc82b6566aba4cb1706f8073089fee6620d9..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/configs/config-ade20k-linear-training.yaml +++ /dev/null @@ -1,53 +0,0 @@ -# Config for ade20k, linear training - -bs: 2 -n_gpus: 8 -metric_to_save: 'mIoU' -model_dtype: FLOAT32 -scheduler: - total_iter: 40000 - type: 'WarmupOneCycleLR' - constructor_kwargs: - warmup_iters: 1500 - warmup_ratio: 1e-6 - final_div_factor: .inf - pct_start: 0 - anneal_strategy: 'cos' - use_beta1: False - update_momentum: False -optimizer: - lr : 1e-3 - beta1: 0.9 - beta2: 0.999 - weight_decay: 1e-3 - gradient_clip: 'inf' -datasets: - root: "" # Path to the ADE20K dataset - train: "ADE20K:split=TRAIN" - val: "ADE20K:split=VAL" -train: - diceloss_weight: 0.0 - celoss_weight: 1.0 -decoder_head: - type: "linear" - backbone_out_layers: LAST - use_cls_token: False - use_batchnorm: True - use_backbone_norm: True - num_classes: 150 -transforms: - train: - img_size: 512 - random_img_size_ratio_range: [0.5, 2.0] - crop_size: [512, 512] - flip_prob: 0.5 - eval: - img_size: 512 -eval: - compute_metric_per_image: False - reduce_zero_label: True - mode: "slide" - crop_size: 512 - stride: 341 - eval_interval: 5000 - use_tta: False diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/configs/config-ade20k-m2f-inference.yaml b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/configs/config-ade20k-m2f-inference.yaml deleted file mode 100644 index d8d8a8ca485978486ea5faea69007d252f86a701..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/configs/config-ade20k-m2f-inference.yaml +++ /dev/null @@ -1,21 +0,0 @@ -# Config for ade20k, M2F inference - -metric_to_save: 'mIoU' -datasets: - root: "" # Path to the ADE20K dataset - val: "ADE20K:split=VAL" -decoder_head: - type: "m2f" - backbone_out_layers: FOUR_EVEN_INTERVALS - num_classes: 150 -transforms: - eval: - img_size: 896 - tta_ratios: [0.9, 0.95, 1.0, 1.05, 1.1] -eval: - compute_metric_per_image: False - reduce_zero_label: True - mode: "slide" - crop_size: 896 - stride: 596 - use_tta: True diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/eval.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/eval.py deleted file mode 100644 index c213f19031688ead18360dfcb2bf0144f02bf41d..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/eval.py +++ /dev/null @@ -1,144 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -from functools import partial -import logging - -import torch - -import dinov3.distributed as distributed -from dinov3.data import DatasetWithEnumeratedTargets, SamplerType, make_data_loader, make_dataset -from dinov3.eval.segmentation.inference import make_inference -from dinov3.eval.segmentation.metrics import ( - calculate_intersect_and_union, - calculate_segmentation_metrics, -) -from dinov3.eval.segmentation.models import build_segmentation_decoder -from dinov3.eval.segmentation.transforms import make_segmentation_eval_transforms -from dinov3.hub.segmentors import dinov3_vit7b16_ms -from dinov3.logging import MetricLogger - -logger = logging.getLogger("dinov3") - -RESULTS_FILENAME = "results-semantic-segmentation.csv" -MAIN_METRICS = ["mIoU"] - - -def evaluate_segmentation_model( - segmentation_model: torch.nn.Module, - test_dataloader, - device, - eval_res, - eval_stride, - decoder_head_type, - num_classes, - autocast_dtype, -): - segmentation_model = segmentation_model.to(device) - segmentation_model.eval() - all_metric_values = [] - metric_logger = MetricLogger(delimiter=" ") - - for batch_img, (_, gt) in metric_logger.log_every(test_dataloader, 10, header="Validation: "): - batch_img = [img.to(device).to(dtype=autocast_dtype) for img in batch_img] - gt = gt.to(device)[0] - aggregated_preds = torch.zeros(1, num_classes, gt.shape[-2], gt.shape[-1]) - for img_idx, img in enumerate(batch_img): - aggregated_preds += make_inference( - img, - segmentation_model.module, - inference_mode="slide", - decoder_head_type=decoder_head_type, - rescale_to=gt.shape[-2:], - n_output_channels=num_classes, - crop_size=(eval_res, eval_res), - stride=(eval_stride, eval_stride), - apply_horizontal_flip=(img_idx and img_idx >= len(batch_img) / 2), - output_activation=partial(torch.nn.functional.softmax, dim=1), - ) - aggregated_preds = (aggregated_preds / len(batch_img)).argmax(dim=1, keepdim=True).to(device) - intersect_and_union = calculate_intersect_and_union( - aggregated_preds[0], - gt, - num_classes=num_classes, - reduce_zero_label=True, - ) - all_metric_values.append(intersect_and_union) - del img, gt, aggregated_preds, intersect_and_union - - all_metric_values = torch.stack(all_metric_values) - if distributed.is_enabled(): - all_metric_values = torch.cat(distributed.gather_all_tensors((all_metric_values))) - final_metrics = calculate_segmentation_metrics( - all_metric_values, - metrics=["mIoU", "dice", "fscore"], - ) - final_metrics = {k: round(v.cpu().item() * 100, 2) for k, v in final_metrics.items()} - logger.info(final_metrics) - return final_metrics - - -def test_segmentation(backbone, config): - # 1- construct a segmentation decoder - if config.load_from == "dinov3_vit7b16_ms": # torch hub descriptor - # Load public m2f head checkpoints - logger.info("Loading the 7B backbone and the M2F adapter with torchhub") - segmentation_model = dinov3_vit7b16_ms(autocast_dtype=config.model_dtype.autocast_dtype, check_hash=True) - else: - segmentation_model = build_segmentation_decoder( - backbone, - config.decoder_head.backbone_out_layers, - config.decoder_head.type, - hidden_dim=config.decoder_head.hidden_dim, # Only used for instantiating a M2F head - num_classes=config.decoder_head.num_classes, - autocast_dtype=config.model_dtype.autocast_dtype, - ) - state_dict = torch.load(config.load_from, map_location="cpu")["model"] - _, _ = segmentation_model.load_state_dict(state_dict, strict=False) - device = distributed.get_rank() - segmentation_model = torch.nn.parallel.DistributedDataParallel(segmentation_model.to(device), device_ids=[device]) - - # 2- dataloader for testing - eval_res = config.eval.crop_size - eval_stride = config.eval.stride - transforms = make_segmentation_eval_transforms( - img_size=eval_res, - inference_mode="slide", - use_tta=config.eval.use_tta, - tta_ratios=config.transforms.eval.tta_ratios, - ) - - test_dataset = DatasetWithEnumeratedTargets( - make_dataset( - dataset_str=f"{config.datasets.val}:root={config.datasets.root}", - transforms=transforms, - ) - ) - - test_sampler_type = None - if distributed.is_enabled(): - test_sampler_type = SamplerType.DISTRIBUTED - - test_dataloader = make_data_loader( - dataset=test_dataset, - batch_size=1, - num_workers=6, - sampler_type=test_sampler_type, - drop_last=False, - shuffle=False, - persistent_workers=True, - ) - - # 3- make inference - return evaluate_segmentation_model( - segmentation_model=segmentation_model, - test_dataloader=test_dataloader, - device=device, - eval_res=eval_res, - eval_stride=eval_stride, - decoder_head_type=config.decoder_head.type, - num_classes=config.decoder_head.num_classes, - autocast_dtype=config.model_dtype.autocast_dtype, - ) diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/inference.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/inference.py deleted file mode 100644 index db0eabd5948e9875a99a624a9c06f59e4c2a95a2..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/inference.py +++ /dev/null @@ -1,149 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -from typing import Callable, Optional, Tuple - -import torch -import torch.nn.functional as F -from torch import nn -from torchvision.transforms import functional as Fv - - -def make_inference( - x: torch.Tensor, - segmentation_model: nn.Module, - inference_mode: str = "whole", - decoder_head_type: str = "linear", - rescale_to=(512, 512), - n_output_channels: int = 256, - crop_size: Optional[Tuple[int]] = None, - stride: Optional[Tuple[int]] = None, - apply_horizontal_flip: bool = False, - num_max_forward: int = 1, - output_activation: Callable | None = None, -): - """Make inference on a given image, and reverts horizontal flip TTA if applicable. - If `inference_mode` = whole, one single prediction is made for the image. - If `inference_mode` = slide, the image is cropped into multiple slices and the latter are - used to make prediction following a sliding window method. - - Args: - x (tensor): input image to make inference on. - dense_predictor (nn.Module): model to use for evaluating on dense tasks. - requires a `predict` method. - inference_mode (str, optional): Do inference on the whole image (mode="whole"), or by - adopting a sliding window approach to aggregate the results on - smaller patches of the input image (mode="slide"). Defaults to "whole". - rescale_to (tuple, optional): Resizing the output of the model prediction to the - shape of the ground truth. Defaults to (512, 512). - n_output_channels (int): number of output classes - crop_size (tuple, optional): [h_crop, w_crop] - stride (tuple, optional): [h_stride, w_stride] - apply_horizontal_flip (bool): Determines if horizontal flip TTA was applied for - the prediction. Defaults to False. - output_activation (callable): Output activation to use on top of the predictions. - - softmax is used when each pixel belongs to a single class (multiclass), - - sigmoid is used when pixel can belong to multiple classes (multilabel). Defaults to None (identity). - Returns: - Tensor: The segmentation results created from the input image. - """ - assert inference_mode in ["whole", "slide"] - if inference_mode == "slide": - # crop size and stride are needed for sliding inference - assert crop_size is not None - assert stride is not None - pred = F.interpolate( - slide_inference( - x, - segmentation_model, - decoder_head_type, - n_output_channels=n_output_channels, - crop_size=crop_size, - stride=stride, - num_max_forward=num_max_forward, - ), - size=rescale_to, - mode="bilinear", - align_corners=False, - ) - else: - pred = segmentation_model.predict( - F.interpolate( - x, - size=(512, 512), - mode="bilinear", - align_corners=False, - ), - rescale_to=rescale_to, - ) - if decoder_head_type == "m2f": - mask_pred, mask_cls = pred["pred_masks"], pred["pred_logits"] - mask_cls = F.softmax(mask_cls, dim=-1)[..., :-1] - mask_pred = mask_pred.sigmoid() - pred = torch.einsum("bqc,bqhw->bchw", mask_cls.to(torch.float), mask_pred.to(torch.float)) - if apply_horizontal_flip: - pred = Fv.hflip(pred) - if output_activation: - pred = output_activation(pred) - return pred - - -def slide_inference( - inputs: torch.Tensor, - segmentation_model: nn.Module, - decoder_head_type: str = "linear", - n_output_channels: int = 256, - crop_size: Tuple = (512, 512), - stride: Tuple = (341, 341), - num_max_forward: int = 1, -): - """Inference by sliding-window with overlap. - If h_crop > h_img or w_crop > w_img, the small patch will be used to - decode without padding. - Args: - inputs (tensor): the tensor should have a shape NxCxHxW, - which contains all images in the batch. - segmentation_model (nn.Module): model to use for evaluating on dense tasks. - n_output_channels (int): number of output channels - crop_size (tuple): (h_crop, w_crop) - stride (tuple): (h_stride, w_stride) - Returns: - Tensor: The output results from model of each input image. - """ - h_stride, w_stride = stride - h_crop, w_crop = crop_size - batch_size, C, h_img, w_img = inputs.shape - if h_crop > h_img and w_crop > w_img: # Meaning we are doing < 1.0 TTA - h_crop, w_crop = min(h_img, w_img), min(h_img, w_img) - assert batch_size == 1 # As of now, the code assumes that a single image is passed at a time at inference time - h_grids = max(h_img - h_crop + h_stride - 1, 0) // h_stride + 1 - w_grids = max(w_img - w_crop + w_stride - 1, 0) // w_stride + 1 - preds = inputs.new_zeros((1, n_output_channels, h_img, w_img)).cpu() - count_mat = inputs.new_zeros((1, 1, h_img, w_img)).to(torch.int8).cpu() - for h_idx in range(h_grids): - for w_idx in range(w_grids): - y1 = h_idx * h_stride - x1 = w_idx * w_stride - y2 = min(y1 + h_crop, h_img) - x2 = min(x1 + w_crop, w_img) - y1 = max(y2 - h_crop, 0) - x1 = max(x2 - w_crop, 0) - crop_img = inputs[:, :, y1:y2, x1:x2] - crop_pred = segmentation_model.predict(crop_img, rescale_to=crop_img.shape[2:]) - if decoder_head_type == "m2f": - mask_pred, mask_cls = crop_pred["pred_masks"], crop_pred["pred_logits"] - mask_cls = F.softmax(mask_cls, dim=-1)[..., :-1] - mask_pred = mask_pred.sigmoid() - crop_pred = torch.einsum("bqc,bqhw->bchw", mask_cls.to(torch.bfloat16), mask_pred.to(torch.bfloat16)) - del mask_cls, mask_pred - preds += F.pad(crop_pred, (int(x1), int(preds.shape[-1] - x2), int(y1), int(preds.shape[-2] - y2))).cpu() - count_mat[:, :, y1:y2, x1:x2] += 1 - del crop_img, crop_pred - # Optional buffer to ensure each gpu does the same number of operations for sharded models - for _ in range(h_grids * w_grids, num_max_forward): - dummy_input = inputs.new_zeros((1, C, h_crop, w_crop)) - _ = segmentation_model.predict(dummy_input, rescale_to=dummy_input.shape[2:]) - assert (count_mat == 0).sum() == 0 - return preds / count_mat diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/loss.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/loss.py deleted file mode 100644 index b73b1efc002bc295cd635dca50cd66a9bfd25a29..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/loss.py +++ /dev/null @@ -1,296 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import functools - -import numpy as np -import torch -import torch.nn.functional as F -from torch import nn - - -def reduce_loss(loss, reduction) -> torch.Tensor: - """Reduce loss as specified. - - Args: - loss (Tensor): Elementwise loss tensor. - reduction (str): Options are "none", "mean" and "sum". - - Return: - Tensor: Reduced loss tensor. - """ - reduction_enum = nn._reduction.get_enum(reduction) - # None: 0, element-wise mean: 1, sum: 2 - assert reduction_enum in [0, 1, 2] - if reduction_enum == 0: - return loss - if reduction_enum == 1: - return loss.mean() - return loss.sum() - - -def weight_reduce_loss(loss, weight=None, reduction="mean", avg_factor=None) -> torch.Tensor: - """Apply element-wise weight and reduce loss. - - Args: - loss (Tensor): Element-wise loss. - weight (Tensor): Element-wise weights. - reduction (str): Same as built-in losses of PyTorch. - avg_factor (float): Average factor when computing the mean of losses. - - Returns: - Tensor: Processed loss values. - """ - # if weight is specified, apply element-wise weight - if weight is not None: - assert weight.dim() == loss.dim() - if weight.dim() > 1: - assert weight.size(1) == 1 or weight.size(1) == loss.size(1) - loss = loss * weight - - # if avg_factor is not specified, just reduce the loss - if avg_factor is None: - loss = reduce_loss(loss, reduction) - else: - # if reduction is mean, then average the loss by avg_factor - if reduction == "mean": - # Avoid causing ZeroDivisionError when avg_factor is 0.0, - # i.e., all labels of an image belong to ignore index. - eps = torch.finfo(torch.float32).eps - loss = loss.sum() / (avg_factor + eps) - # if reduction is 'none', then do nothing, otherwise raise an error - elif reduction != "none": - raise ValueError('avg_factor can not be used with reduction="sum"') - return loss - - -def weighted_loss(loss_func): - """Create a weighted version of a given loss function. - - To use this decorator, the loss function must have the signature like - `loss_func(pred, target, **kwargs)`. The function only needs to compute - element-wise loss without any reduction. This decorator will add weight - and reduction arguments to the function. The decorated function will have - the signature like `loss_func(pred, target, weight=None, reduction='mean', - avg_factor=None, **kwargs)`. - """ - - @functools.wraps(loss_func) - def wrapper(pred, target, weight=None, reduction="mean", avg_factor=None, **kwargs): - # get element-wise loss - loss = loss_func(pred, target, **kwargs) - loss = weight_reduce_loss(loss, weight, reduction, avg_factor) - return loss - - return wrapper - - -def get_class_weight(class_weight): - """Get class weight for loss function. - - Args: - class_weight (list[float] | str | None): If class_weight is a str, - take it as a file name and read from it. - """ - if isinstance(class_weight, str): - class_weight = np.load(class_weight) - - return class_weight - - -@weighted_loss -def dice_loss(pred, target, valid_mask, smooth=1, exponent=2, class_weight=None, ignore_index=255): - assert pred.shape[0] == target.shape[0] - total_loss = 0 - num_classes = pred.shape[1] - for i in range(num_classes): - if i != ignore_index: - dice_loss = binary_dice_loss( - pred[:, i], target[..., i], valid_mask=valid_mask, smooth=smooth, exponent=exponent - ) - if class_weight is not None: - dice_loss *= class_weight[i] - total_loss += dice_loss - return total_loss / num_classes - - -@weighted_loss -def binary_dice_loss(pred, target, valid_mask, smooth=1, exponent=2, **kwargs): - assert pred.shape[0] == target.shape[0] - pred = pred.reshape(pred.shape[0], -1) - target = target.reshape(target.shape[0], -1) - valid_mask = valid_mask.reshape(valid_mask.shape[0], -1) - - num = torch.sum(torch.mul(pred, target) * valid_mask, dim=1) * 2 + smooth - den = torch.sum(pred.pow(exponent) + target.pow(exponent), dim=1) + smooth - - return 1 - num / den - - -class DiceLoss(nn.Module): - """DiceLoss. - - This loss is proposed in `V-Net: Fully Convolutional Neural Networks for - Volumetric Medical Image Segmentation `_. - - Args: - smooth (float): A float number to smooth loss, and avoid NaN error. - Default: 1 - exponent (float): An float number to calculate denominator - value: \\sum{x^exponent} + \\sum{y^exponent}. Default: 2. - reduction (str, optional): The method used to reduce the loss. Options - are "none", "mean" and "sum". This parameter only works when - per_image is True. Default: 'mean'. - class_weight (list[float] | str, optional): Weight of each class. If in - str format, read them from a file. Defaults to None. - loss_weight (float, optional): Weight of the loss. Default to 1.0. - ignore_index (int | None): The label index to be ignored. Default: 255. - loss_name (str, optional): Name of the loss item. If you want this loss - item to be included into the backward graph, `loss_` must be the - prefix of the name. Defaults to 'loss_dice'. - """ - - def __init__( - self, - smooth=1, - exponent=2, - reduction="mean", - class_weight=None, - loss_weight=1.0, - ignore_index=255, - loss_name="loss_dice", - **kwargs, - ): - super(DiceLoss, self).__init__() - self.smooth = smooth - self.exponent = exponent - self.reduction = reduction - self.class_weight = get_class_weight(class_weight) - self.loss_weight = loss_weight - self.ignore_index = ignore_index - self._loss_name = loss_name - - def forward(self, pred, target, avg_factor=None, reduction_override=None, **kwargs): - assert reduction_override in (None, "none", "mean", "sum") - reduction = reduction_override if reduction_override else self.reduction - if self.class_weight is not None: - class_weight = pred.new_tensor(self.class_weight) - else: - class_weight = None - - pred = F.softmax(pred, dim=1) - num_classes = pred.shape[1] - one_hot_target = F.one_hot(torch.clamp(target.long(), 0, num_classes - 1), num_classes=num_classes) - valid_mask = (target != self.ignore_index).long() - - loss = self.loss_weight * dice_loss( - pred, - one_hot_target, - valid_mask=valid_mask, - reduction=reduction, - avg_factor=avg_factor, - smooth=self.smooth, - exponent=self.exponent, - class_weight=class_weight, - ignore_index=self.ignore_index, - ) - return loss - - -@weighted_loss -def multilabel_dice_loss(pred, target, valid_mask, smooth=1, exponent=2, class_weight=None, ignore_index=255): - assert pred.shape[0] == target.shape[0] - total_loss = 0 - num_classes = pred.shape[1] - for i in range(num_classes): - if i != ignore_index: - dice_loss = binary_dice_loss( - pred[:, i], target[:, i], valid_mask=valid_mask, smooth=smooth, exponent=exponent - ) - if class_weight is not None: - dice_loss *= class_weight[i] - total_loss += dice_loss - return total_loss / num_classes - - -class MultilabelDiceLoss(DiceLoss): - def forward(self, pred, target, avg_factor=None, reduction_override=None, **kwargs): - assert reduction_override in (None, "none", "mean", "sum") - reduction = reduction_override if reduction_override else self.reduction - if self.class_weight is not None: - class_weight = pred.new_tensor(self.class_weight) - else: - class_weight = None - - pred = F.sigmoid(pred) - if False: - valid_mask = (target[..., self.ignore_index] == 0).long() - else: - valid_mask = torch.ones_like(target[:, 0]).long() - - loss = self.loss_weight * multilabel_dice_loss( - pred, - target, - valid_mask=valid_mask, - reduction=reduction, - avg_factor=avg_factor, - smooth=self.smooth, - exponent=self.exponent, - class_weight=class_weight, - ignore_index=self.ignore_index, - ) - return loss - - -class CrossEntropyLoss(nn.Module): - def __init__( - self, - weight=None, - class_weight=None, - loss_weight=1.0, - reduction="mean", - avg_factor=None, - ignore_index=255, - avg_non_ignore=False, - ): - super(CrossEntropyLoss, self).__init__() - self.weight = weight - self.class_weight = class_weight - self.loss_weight = loss_weight - self.reduction = reduction - self.avg_factor = avg_factor - self.ignore_index = ignore_index - self.avg_non_ignore = avg_non_ignore - - def forward(self, pred, label): - loss = F.cross_entropy(pred, label, weight=self.class_weight, reduction="none", ignore_index=self.ignore_index) - - if (self.avg_factor is None) and self.avg_non_ignore and self.reduction == "mean": - avg_factor = label.numel() - (label == self.ignore_index).sum().item() - else: - avg_factor = None - - loss = weight_reduce_loss(loss, weight=self.weight, reduction=self.reduction, avg_factor=avg_factor) - return self.loss_weight * loss - - -class MultiSegmentationLoss(nn.Module): - """ - Combine different losses used in segmentation. - """ - - def __init__(self, diceloss_weight=0.0, celoss_weight=0.0): - super(MultiSegmentationLoss, self).__init__() - - if diceloss_weight > 0: - self.loss = MultilabelDiceLoss(loss_weight=diceloss_weight) - elif celoss_weight > 0: - self.loss = CrossEntropyLoss(reduction="mean", loss_weight=celoss_weight) - else: - self.loss = lambda _: 0 - - def forward(self, pred, gt): - """Forward function.""" - return self.loss(pred, gt) diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/metrics.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/metrics.py deleted file mode 100644 index 608d7ca98ec279fa96498fe1e7578efda827bd96..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/metrics.py +++ /dev/null @@ -1,153 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import logging -import numpy as np -import pandas as pd -import torch - - -pd.set_option("display.max_rows", 200) - -logger = logging.getLogger("dinov3") - - -SEGMENTATION_METRICS = ["mIoU", "acc", "aAcc", "dice", "fscore", "precision", "recall"] - - -def calculate_segmentation_metrics( - pre_eval_results, - metrics=["mIoU"], - beta=1, -): - """Calculate the segmentation metrics after aggregating all the intermediate results. - - Args: - pre_eval_results (list): Lists of (area_intersect, area_union, area_pred_label, and area_label). - These are intermediate results to compute the final metrics such as iou, fscore, etc. - metrics (list): Metrics to compute. Defaults to ["mIoU"]. - beta (int): Parameter for computing F-score. Defaults to 1 (for computing F1-score). - - Returns: - Dictionary of final metrics. - """ - pre_eval_results = tuple(zip(*pre_eval_results)) - assert len(pre_eval_results) == 4 - total_area_intersect = sum(pre_eval_results[0]) - total_area_union = sum(pre_eval_results[1]) - total_area_pred_label = sum(pre_eval_results[2]) - total_area_label = sum(pre_eval_results[3]) - metrics_dict = total_area_to_metrics( - total_area_intersect, - total_area_union, - total_area_pred_label, - total_area_label, - metrics=metrics, - beta=beta, - ) - df = pd.DataFrame( - { - "Class Index": np.arange(len(metrics_dict["mIoU"])), - "mIoU": 100 * metrics_dict["mIoU"].cpu().numpy(), - } - ) - logger.info(f"mIoU per class:\n{df.to_string(index=False)}") - return { - "mIoU": metrics_dict["mIoU"].nanmean(), - "acc": metrics_dict["acc"].nanmean(), - "aAcc": metrics_dict["aAcc"].nanmean(), - "dice": metrics_dict["dice"].nanmean(), - "fscore": metrics_dict["fscore"].nanmean(), - "precision": metrics_dict["precision"].nanmean(), - "recall": metrics_dict["recall"].nanmean(), - } - - -def preprocess_nonzero_labels(label, ignore_index=255): - label_new = label.clone() - label_new[label_new == ignore_index] += 1 - label_new -= 1 - label_new[label_new == -1] = ignore_index - return label_new - - -def calculate_intersect_and_union(pred_label, label, num_classes, ignore_index=255, reduce_zero_label=False): - """Calculate intersection and Union. - Args: - pred_label (torch.Tensor): Prediction segmentation map - label (torch.Tensor): Ground truth segmentation map - num_classes (int): Number of categories. - ignore_index (int): Index that will be ignored in evaluation. - reduce_zero_label (bool): Indicates whether or not label 0 is to be ignored. - """ - pred_label = pred_label.float() # Enables float tensor operations - if reduce_zero_label: - label = preprocess_nonzero_labels(label, ignore_index=ignore_index) - - mask = label != ignore_index - pred_label = pred_label[mask] - label = label[mask] - intersect = pred_label[pred_label == label] - area_intersect = torch.histc(intersect.float(), bins=(num_classes), min=0, max=num_classes - 1) - area_pred_label = torch.histc(pred_label.float(), bins=(num_classes), min=0, max=num_classes - 1) - area_label = torch.histc(label.float(), bins=(num_classes), min=0, max=num_classes - 1) - area_union = area_pred_label + area_label - area_intersect - - return torch.stack([area_intersect, area_union, area_pred_label, area_label]) - - -def total_area_to_metrics( - total_area_intersect, - total_area_union, - total_area_pred_label, - total_area_label, - metrics=["mIoU"], - beta=1, -): - """Calculate evaluation metrics - Args: - total_area_intersect (torch.Tensor): The intersection of prediction and - ground truth histogram on all classes. - total_area_union (torch.Tensor): The union of prediction and ground truth - histogram on all classes. - total_area_pred_label (torch.Tensor): The prediction histogram on all - classes. - total_area_label (torch.Tensor): The ground truth histogram on all classes. - metrics (list[str] | str): Metrics to be evaluated, - can be 'mIoU', 'mDice', or 'mFscore'. - beta (int): Parameter for computing F-score. Defaults to 1 (for computing F1-score). - Returns: - float: Overall accuracy on all images. - ndarray: Per category accuracy, shape (num_classes, ). - ndarray: Per category evaluation metrics, shape (num_classes, ). - """ - - def f_score(precision, recall, beta=1): - score = (1 + beta**2) * (precision * recall) / ((beta**2 * precision) + recall) - return score - - if isinstance(metrics, str): - metrics = [metrics] - allowed_metrics = ["mIoU", "dice", "fscore"] - if not set(metrics).issubset(set(allowed_metrics)): - raise KeyError(f"metrics {metrics} is not supported") - - all_acc = total_area_intersect.sum() / total_area_label.sum() - ret_metrics = dict({"aAcc": all_acc}) - for metric in metrics: - if metric == "mIoU": - ret_metrics["mIoU"] = total_area_intersect / total_area_union - ret_metrics["acc"] = total_area_intersect / total_area_label - elif metric == "dice": - ret_metrics["dice"] = 2 * total_area_intersect / (total_area_pred_label + total_area_label) - ret_metrics["acc"] = total_area_intersect / total_area_label - elif metric == "fscore": - precision = total_area_intersect / total_area_pred_label - recall = total_area_intersect / total_area_label - f_value = torch.tensor([f_score(x[0], x[1], beta) for x in zip(precision, recall)]) - ret_metrics["fscore"] = f_value - ret_metrics["precision"] = precision - ret_metrics["recall"] = recall - return ret_metrics diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/models/__init__.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/models/__init__.py deleted file mode 100644 index 202386d2cef7aac6809fef6a6f8facfe7a061fc5..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/models/__init__.py +++ /dev/null @@ -1,137 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -from enum import Enum -from functools import partial - -import torch - -from dinov3.eval.segmentation.models.backbone.dinov3_adapter import DINOv3_Adapter -from dinov3.eval.segmentation.models.heads.linear_head import LinearHead -from dinov3.eval.segmentation.models.heads.mask2former_head import Mask2FormerHead -from dinov3.eval.utils import ModelWithIntermediateLayers - - -class BackboneLayersSet(Enum): - """ - Set of intermediate layers to take from the backbone. - """ - - LAST = "LAST" # extracting only the last layer - FOUR_LAST = "FOUR_LAST" # extracting the four last layers - FOUR_EVEN_INTERVALS = "FOUR_EVEN_INTERVALS" # extracting outputs every 1/4 of the total number of blocks - - -def _get_backbone_out_indices( - model: torch.nn.Module, - backbone_out_layers: BackboneLayersSet = BackboneLayersSet.FOUR_EVEN_INTERVALS, -): - """ - Get indices for output layers of the ViT backbone. For now there are 3 options available: - BackboneLayersSet.LAST : only extract the last layer, used in segmentation tasks with a bn head. - BackboneLayersSet.FOUR_EVEN_INTERVALS : extract outputs every 1/4 of the total number of blocks - Reference outputs in 'FOUR_EVEN_INTERVALS' mode : - ViT/S (12 blocks): [2, 5, 8, 11] - ViT/B (12 blocks): [2, 5, 8, 11] - ViT/L (24 blocks): [5, 11, 17, 23] (classic), [4, 11, 17, 23] (used in the paper) - ViT/g (40 blocks): [9, 19, 29, 39] - """ - n_blocks = getattr(model, "n_blocks", 1) - if backbone_out_layers == BackboneLayersSet.LAST: - out_indices = [n_blocks - 1] - elif backbone_out_layers == BackboneLayersSet.FOUR_LAST: - out_indices = [i for i in range(n_blocks - 4, n_blocks)] - elif backbone_out_layers == BackboneLayersSet.FOUR_EVEN_INTERVALS: - # Take indices that were used in the paper (for ViT/L only) - if n_blocks == 24: - out_indices = [4, 11, 17, 23] - else: - out_indices = [i * (n_blocks // 4) - 1 for i in range(1, 5)] - assert all([out_index < n_blocks for out_index in out_indices]) - return out_indices - - -class FeatureDecoder(torch.nn.Module): - def __init__(self, segmentation_model: torch.nn.ModuleList, autocast_ctx): - super().__init__() - self.segmentation_model = segmentation_model - self.autocast_ctx = autocast_ctx - - def forward(self, inputs): - with self.autocast_ctx(): - for module in self.segmentation_model: - inputs = module.forward(inputs) - return inputs - - def predict(self, inputs, rescale_to=(512, 512)): - with torch.inference_mode(): - with self.autocast_ctx(): - out = self.segmentation_model[0](inputs) # backbone forward - out = self.segmentation_model[1].predict(out, rescale_to=rescale_to) # decoder head prediction - return out - - -def build_segmentation_decoder( - backbone_model, - backbone_out_layers=BackboneLayersSet.FOUR_EVEN_INTERVALS, - decoder_type="linear", - hidden_dim=2048, - num_classes=150, - autocast_dtype=torch.float32, -): - backbone_indices_to_use = _get_backbone_out_indices(backbone_model, backbone_out_layers) - autocast_ctx = partial(torch.autocast, device_type="cuda", enabled=True, dtype=autocast_dtype) - if decoder_type == "m2f": - backbone_model = DINOv3_Adapter( - backbone_model, - interaction_indexes=backbone_indices_to_use, - ) - backbone_model.eval() - embed_dim = backbone_model.backbone.embed_dim - patch_size = backbone_model.patch_size - decoder = Mask2FormerHead( - input_shape={ - "1": [embed_dim, patch_size * 4, patch_size * 4, 4], - "2": [embed_dim, patch_size * 2, patch_size * 2, 4], - "3": [embed_dim, patch_size, patch_size, 4], - "4": [embed_dim, int(patch_size / 2), int(patch_size / 2), 4], - }, - hidden_dim=hidden_dim, - num_classes=num_classes, - ignore_value=255, - ) - elif decoder_type == "linear": - backbone_model = ModelWithIntermediateLayers( - backbone_model, - n=backbone_indices_to_use, - autocast_ctx=autocast_ctx, - reshape=True, - return_class_token=False, - ) - # Important: we freeze the backbone - backbone_model.requires_grad_(False) - embed_dim = backbone_model.feature_model.embed_dim - if isinstance(embed_dim, int): - if backbone_out_layers in [BackboneLayersSet.FOUR_LAST, BackboneLayersSet.FOUR_EVEN_INTERVALS]: - embed_dim = [embed_dim] * 4 - else: - embed_dim = [embed_dim] - decoder = LinearHead( - in_channels=embed_dim, - n_output_channels=num_classes, - ) - else: - raise ValueError(f'Unsupported decoder "{decoder_type}"') - - segmentation_model = FeatureDecoder( - torch.nn.ModuleList( - [ - backbone_model, - decoder, - ] - ), - autocast_ctx=autocast_ctx, - ) - return segmentation_model diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/models/backbone/dinov3_adapter.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/models/backbone/dinov3_adapter.py deleted file mode 100644 index 14906bb7aec55ec519e82a88c80518acdfdd998c..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/models/backbone/dinov3_adapter.py +++ /dev/null @@ -1,484 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import math - -import torch -import torch.nn as nn -import torch.nn.functional as F -import torch.utils.checkpoint as cp - -from functools import partial - -from dinov3.eval.segmentation.models.utils.ms_deform_attn import MSDeformAttn - - -def drop_path(x, drop_prob: float = 0.0, training: bool = False): - if drop_prob == 0.0 or not training: - return x - keep_prob = 1 - drop_prob - shape = (x.shape[0],) + (1,) * (x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets - random_tensor = x.new_empty(shape).bernoulli_(keep_prob) - if keep_prob > 0.0: - random_tensor.div_(keep_prob) - return x * random_tensor - - -class DropPath(nn.Module): - """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).""" - - def __init__(self, drop_prob: float = 0.0): - super(DropPath, self).__init__() - self.drop_prob = drop_prob - - def forward(self, x): - return drop_path(x, self.drop_prob, self.training) - - -def get_reference_points(spatial_shapes, device): - reference_points_list = [] - for lvl, (H_, W_) in enumerate(spatial_shapes): - ref_y, ref_x = torch.meshgrid( - torch.linspace(0.5, H_ - 0.5, H_, dtype=torch.float32, device=device), - torch.linspace(0.5, W_ - 0.5, W_, dtype=torch.float32, device=device), - ) - ref_y = ref_y.reshape(-1)[None] / H_ - ref_x = ref_x.reshape(-1)[None] / W_ - ref = torch.stack((ref_x, ref_y), -1) - reference_points_list.append(ref) - reference_points = torch.cat(reference_points_list, 1) - reference_points = reference_points[:, :, None] - return reference_points - - -def deform_inputs(x, patch_size): - bs, c, h, w = x.shape - spatial_shapes = torch.as_tensor( - [(h // 8, w // 8), (h // 16, w // 16), (h // 32, w // 32)], dtype=torch.long, device=x.device - ) - level_start_index = torch.cat((spatial_shapes.new_zeros((1,)), spatial_shapes.prod(1).cumsum(0)[:-1])) - reference_points = get_reference_points([(h // patch_size, w // patch_size)], x.device) - deform_inputs1 = [reference_points, spatial_shapes, level_start_index] - - spatial_shapes = torch.as_tensor([(h // patch_size, w // patch_size)], dtype=torch.long, device=x.device) - level_start_index = torch.cat((spatial_shapes.new_zeros((1,)), spatial_shapes.prod(1).cumsum(0)[:-1])) - reference_points = get_reference_points([(h // 8, w // 8), (h // 16, w // 16), (h // 32, w // 32)], x.device) - deform_inputs2 = [reference_points, spatial_shapes, level_start_index] - - return deform_inputs1, deform_inputs2 - - -class ConvFFN(nn.Module): - def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.0): - super().__init__() - out_features = out_features or in_features - hidden_features = hidden_features or in_features - self.fc1 = nn.Linear(in_features, hidden_features) - self.dwconv = DWConv(hidden_features) - self.act = act_layer() - self.fc2 = nn.Linear(hidden_features, out_features) - self.drop = nn.Dropout(drop) - - def forward(self, x, H, W): - x = self.fc1(x) - x = self.dwconv(x, H, W) - x = self.act(x) - x = self.drop(x) - x = self.fc2(x) - x = self.drop(x) - return x - - -class DWConv(nn.Module): - def __init__(self, dim=768): - super().__init__() - self.dwconv = nn.Conv2d(dim, dim, 3, 1, 1, bias=True, groups=dim) - - def forward(self, x, H, W): - B, N, C = x.shape - n = N // 21 - x1 = x[:, 0 : 16 * n, :].transpose(1, 2).view(B, C, H * 2, W * 2).contiguous() - x2 = x[:, 16 * n : 20 * n, :].transpose(1, 2).view(B, C, H, W).contiguous() - x3 = x[:, 20 * n :, :].transpose(1, 2).view(B, C, H // 2, W // 2).contiguous() - x1 = self.dwconv(x1).flatten(2).transpose(1, 2) - x2 = self.dwconv(x2).flatten(2).transpose(1, 2) - x3 = self.dwconv(x3).flatten(2).transpose(1, 2) - x = torch.cat([x1, x2, x3], dim=1) - return x - - -class Extractor(nn.Module): - def __init__( - self, - dim, - num_heads=6, - n_points=4, - n_levels=1, - deform_ratio=1.0, - with_cffn=True, - cffn_ratio=0.25, - drop=0.0, - drop_path=0.0, - norm_layer=partial(nn.LayerNorm, eps=1e-6), - with_cp=False, - ): - super().__init__() - self.query_norm = norm_layer(dim) - self.feat_norm = norm_layer(dim) - self.attn = MSDeformAttn( - d_model=dim, n_levels=n_levels, n_heads=num_heads, n_points=n_points, ratio=deform_ratio - ) - self.with_cffn = with_cffn - self.with_cp = with_cp - if with_cffn: - self.ffn = ConvFFN(in_features=dim, hidden_features=int(dim * cffn_ratio), drop=drop) - self.ffn_norm = norm_layer(dim) - self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() - - def forward(self, query, reference_points, feat, spatial_shapes, level_start_index, H, W): - def _inner_forward(query, feat): - attn = self.attn( - self.query_norm(query), reference_points, self.feat_norm(feat), spatial_shapes, level_start_index, None - ) - query = query + attn - - if self.with_cffn: - query = query + self.drop_path(self.ffn(self.ffn_norm(query), H, W)) - return query - - if self.with_cp and query.requires_grad: - query = cp.checkpoint(_inner_forward, query, feat) - else: - query = _inner_forward(query, feat) - - return query - - -class InteractionBlockWithCls(nn.Module): - def __init__( - self, - dim, - num_heads=6, - n_points=4, - norm_layer=partial(nn.LayerNorm, eps=1e-6), - drop=0.0, - drop_path=0.0, - with_cffn=True, - cffn_ratio=0.25, - init_values=0.0, - deform_ratio=1.0, - extra_extractor=False, - with_cp=False, - ): - super().__init__() - self.extractor = Extractor( - dim=dim, - n_levels=1, - num_heads=num_heads, - n_points=n_points, - norm_layer=norm_layer, - deform_ratio=deform_ratio, - with_cffn=with_cffn, - cffn_ratio=cffn_ratio, - drop=drop, - drop_path=drop_path, - with_cp=with_cp, - ) - if extra_extractor: - self.extra_extractors = nn.Sequential( - *[ - Extractor( - dim=dim, - num_heads=num_heads, - n_points=n_points, - norm_layer=norm_layer, - with_cffn=with_cffn, - cffn_ratio=cffn_ratio, - deform_ratio=deform_ratio, - drop=drop, - drop_path=drop_path, - with_cp=with_cp, - ) - for _ in range(2) - ] - ) - else: - self.extra_extractors = None - - def forward(self, x, c, cls, deform_inputs1, deform_inputs2, H_c, W_c, H_toks, W_toks): - c = self.extractor( - query=c, - reference_points=deform_inputs2[0], - feat=x, - spatial_shapes=deform_inputs2[1], - level_start_index=deform_inputs2[2], - H=H_c, - W=W_c, - ) - if self.extra_extractors is not None: - for extractor in self.extra_extractors: - c = extractor( - query=c, - reference_points=deform_inputs2[0], - feat=x, - spatial_shapes=deform_inputs2[1], - level_start_index=deform_inputs2[2], - H=H_c, - W=W_c, - ) - return x, c, cls - - -class SpatialPriorModule(nn.Module): - def __init__(self, inplanes=64, embed_dim=384, with_cp=False): - super().__init__() - self.with_cp = with_cp - - self.stem = nn.Sequential( - *[ - nn.Conv2d(3, inplanes, kernel_size=3, stride=2, padding=1, bias=False), - nn.SyncBatchNorm(inplanes), - nn.ReLU(inplace=True), - nn.Conv2d(inplanes, inplanes, kernel_size=3, stride=1, padding=1, bias=False), - nn.SyncBatchNorm(inplanes), - nn.ReLU(inplace=True), - nn.Conv2d(inplanes, inplanes, kernel_size=3, stride=1, padding=1, bias=False), - nn.SyncBatchNorm(inplanes), - nn.ReLU(inplace=True), - nn.MaxPool2d(kernel_size=3, stride=2, padding=1), - ] - ) - self.conv2 = nn.Sequential( - *[ - nn.Conv2d(inplanes, 2 * inplanes, kernel_size=3, stride=2, padding=1, bias=False), - nn.SyncBatchNorm(2 * inplanes), - nn.ReLU(inplace=True), - ] - ) - self.conv3 = nn.Sequential( - *[ - nn.Conv2d(2 * inplanes, 4 * inplanes, kernel_size=3, stride=2, padding=1, bias=False), - nn.SyncBatchNorm(4 * inplanes), - nn.ReLU(inplace=True), - ] - ) - self.conv4 = nn.Sequential( - *[ - nn.Conv2d(4 * inplanes, 4 * inplanes, kernel_size=3, stride=2, padding=1, bias=False), - nn.SyncBatchNorm(4 * inplanes), - nn.ReLU(inplace=True), - ] - ) - self.fc1 = nn.Conv2d(inplanes, embed_dim, kernel_size=1, stride=1, padding=0, bias=True) - self.fc2 = nn.Conv2d(2 * inplanes, embed_dim, kernel_size=1, stride=1, padding=0, bias=True) - self.fc3 = nn.Conv2d(4 * inplanes, embed_dim, kernel_size=1, stride=1, padding=0, bias=True) - self.fc4 = nn.Conv2d(4 * inplanes, embed_dim, kernel_size=1, stride=1, padding=0, bias=True) - - def forward(self, x): - def _inner_forward(x): - c1 = self.stem(x) - c2 = self.conv2(c1) - c3 = self.conv3(c2) - c4 = self.conv4(c3) - c1 = self.fc1(c1) - c2 = self.fc2(c2) - c3 = self.fc3(c3) - c4 = self.fc4(c4) - - bs, dim, _, _ = c1.shape - # c1 = c1.view(bs, dim, -1).transpose(1, 2) # 4s - c2 = c2.view(bs, dim, -1).transpose(1, 2) # 8s - c3 = c3.view(bs, dim, -1).transpose(1, 2) # 16s - c4 = c4.view(bs, dim, -1).transpose(1, 2) # 32s - - return c1, c2, c3, c4 - - if self.with_cp and x.requires_grad: - outs = cp.checkpoint(_inner_forward, x) - else: - outs = _inner_forward(x) - return outs - - -class DINOv3_Adapter(nn.Module): - def __init__( - self, - backbone, - interaction_indexes=[9, 19, 29, 39], - pretrain_size=512, - conv_inplane=64, - n_points=4, - deform_num_heads=16, - drop_path_rate=0.3, - init_values=0.0, - with_cffn=True, - cffn_ratio=0.25, - deform_ratio=0.5, - add_vit_feature=True, - use_extra_extractor=True, - with_cp=True, - ): - super(DINOv3_Adapter, self).__init__() - self.backbone = backbone - # Important: we freeze the backbone - self.backbone.requires_grad_(False) - - self.pretrain_size = (pretrain_size, pretrain_size) - self.interaction_indexes = interaction_indexes - self.add_vit_feature = add_vit_feature - embed_dim = self.backbone.embed_dim - self.patch_size = self.backbone.patch_size - print("embed dim", embed_dim) - print("interaction_indexes", self.interaction_indexes) - print("patch_size", self.patch_size) - - block_fn = InteractionBlockWithCls - self.level_embed = nn.Parameter(torch.zeros(3, embed_dim)) - self.spm = SpatialPriorModule(inplanes=conv_inplane, embed_dim=embed_dim, with_cp=False) - self.interactions = nn.Sequential( - *[ - block_fn( - dim=embed_dim, - num_heads=deform_num_heads, - n_points=n_points, - init_values=init_values, - drop_path=drop_path_rate, - norm_layer=partial(nn.LayerNorm, eps=1e-6), - with_cffn=with_cffn, - cffn_ratio=cffn_ratio, - deform_ratio=deform_ratio, - extra_extractor=( - (True if i == len(self.interaction_indexes) - 1 else False) and use_extra_extractor - ), - with_cp=with_cp, - ) - for i in range(len(self.interaction_indexes)) - ] - ) - self.up = nn.ConvTranspose2d(embed_dim, embed_dim, 2, 2) - self.norm1 = nn.SyncBatchNorm(embed_dim) - self.norm2 = nn.SyncBatchNorm(embed_dim) - self.norm3 = nn.SyncBatchNorm(embed_dim) - self.norm4 = nn.SyncBatchNorm(embed_dim) - - self.up.apply(self._init_weights) - self.spm.apply(self._init_weights) - self.interactions.apply(self._init_weights) - self.apply(self._init_deform_weights) - torch.nn.init.normal_(self.level_embed) - - def _init_weights(self, m): - if isinstance(m, nn.Linear): - torch.nn.init.trunc_normal_(m.weight, std=0.02) - if isinstance(m, nn.Linear) and m.bias is not None: - nn.init.constant_(m.bias, 0) - elif isinstance(m, nn.LayerNorm) or isinstance(m, nn.BatchNorm2d): - nn.init.constant_(m.bias, 0) - nn.init.constant_(m.weight, 1.0) - elif isinstance(m, nn.Conv2d) or isinstance(m, nn.ConvTranspose2d): - fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels - fan_out //= m.groups - m.weight.data.normal_(0, math.sqrt(2.0 / fan_out)) - if m.bias is not None: - m.bias.data.zero_() - - def _get_pos_embed(self, pos_embed, H, W): - pos_embed = pos_embed.reshape( - 1, self.pretrain_size[0] // self.patch_size, self.pretrain_size[1] // self.patch_size, -1 - ).permute(0, 3, 1, 2) - pos_embed = ( - F.interpolate(pos_embed, size=(H, W), mode="bicubic", align_corners=False) - .reshape(1, -1, H * W) - .permute(0, 2, 1) - ) - return pos_embed - - def _init_deform_weights(self, m): - if isinstance(m, MSDeformAttn): - m._reset_parameters() - - def _add_level_embed(self, c2, c3, c4): - c2 = c2 + self.level_embed[0] - c3 = c3 + self.level_embed[1] - c4 = c4 + self.level_embed[2] - return c2, c3, c4 - - def forward(self, x): - deform_inputs1, deform_inputs2 = deform_inputs(x, self.patch_size) - - # SPM forward - c1, c2, c3, c4 = self.spm(x) - c2, c3, c4 = self._add_level_embed(c2, c3, c4) - - c = torch.cat([c2, c3, c4], dim=1) - - # Code for matching with oss - H_c, W_c = x.shape[2] // 16, x.shape[3] // 16 - H_toks, W_toks = x.shape[2] // self.patch_size, x.shape[3] // self.patch_size - bs, C, h, w = x.shape - - with torch.autocast("cuda", torch.bfloat16): - with torch.no_grad(): - all_layers = self.backbone.get_intermediate_layers( - x, n=self.interaction_indexes, return_class_token=True - ) - - x_for_shape, _ = all_layers[0] - bs, _, dim = x_for_shape.shape - del x_for_shape - - cls, x = ( - x[ - :, - :1, - ], - x[ - :, - 5:, - ], - ) - - outs = list() - for i, layer in enumerate(self.interactions): - x, cls = all_layers[i] - _, c, _ = layer( - x, - c, - cls, - deform_inputs1, - deform_inputs2, - H_c, - W_c, - H_toks, - W_toks, - ) - outs.append(x.transpose(1, 2).view(bs, dim, H_toks, W_toks).contiguous()) - - # Split & Reshape - c2 = c[:, 0 : c2.size(1), :] - c3 = c[:, c2.size(1) : c2.size(1) + c3.size(1), :] - c4 = c[:, c2.size(1) + c3.size(1) :, :] - - c2 = c2.transpose(1, 2).view(bs, dim, H_c * 2, W_c * 2).contiguous() - c3 = c3.transpose(1, 2).view(bs, dim, H_c, W_c).contiguous() - c4 = c4.transpose(1, 2).view(bs, dim, H_c // 2, W_c // 2).contiguous() - c1 = self.up(c2) + c1 - - if self.add_vit_feature: - x1, x2, x3, x4 = outs - - x1 = F.interpolate(x1, size=(4 * H_c, 4 * W_c), mode="bilinear", align_corners=False) - x2 = F.interpolate(x2, size=(2 * H_c, 2 * W_c), mode="bilinear", align_corners=False) - x3 = F.interpolate(x3, size=(1 * H_c, 1 * W_c), mode="bilinear", align_corners=False) - x4 = F.interpolate(x4, size=(H_c // 2, W_c // 2), mode="bilinear", align_corners=False) - c1, c2, c3, c4 = c1 + x1, c2 + x2, c3 + x3, c4 + x4 - - # Final Norm - f1 = self.norm1(c1) - f2 = self.norm2(c2) - f3 = self.norm3(c3) - f4 = self.norm4(c4) - - return {"1": f1, "2": f2, "3": f3, "4": f4} diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/models/heads/__init__.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/models/heads/__init__.py deleted file mode 100644 index 5375bc66e1ed841a7091b81a0dcf56d1993c1f87..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/models/heads/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/models/heads/linear_head.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/models/heads/linear_head.py deleted file mode 100644 index eadcad5031466e6494f1d56b8d693e700b73ea9d..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/models/heads/linear_head.py +++ /dev/null @@ -1,97 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import torch -import torch.nn as nn -import torch.nn.functional as F - - -class LinearHead(nn.Module): - """Linear layer .""" - - def __init__( - self, - in_channels, - n_output_channels, - use_batchnorm=True, - use_cls_token=False, - ): - super().__init__() - self.in_channels = in_channels - self.channels = sum(in_channels) - if use_cls_token: - self.channels *= 2 # concatenate CLS to patch tokens - self.n_output_channels = n_output_channels - self.use_cls_token = use_cls_token - self.batchnorm_layer = nn.SyncBatchNorm(self.channels) if use_batchnorm else nn.Identity(self.channels) - self.conv = nn.Conv2d(self.channels, self.n_output_channels, kernel_size=1, padding=0, stride=1) - self.dropout = nn.Dropout2d(0.1) - nn.init.normal_(self.conv.weight, mean=0, std=0.01) - nn.init.constant_(self.conv.bias, 0) - - def _transform_inputs(self, inputs): - """Transform inputs for decoder. - Args: - inputs (list[Tensor]): List of multi-level img features. - Returns: - Tensor: The transformed inputs - """ - inputs = [ - torch.nn.functional.interpolate( - input=x, - size=inputs[0].shape[2:], - mode="bilinear", - align_corners=False, - ) - for x in inputs - ] - inputs = torch.cat(inputs, dim=1) - return inputs - - def _forward_feature(self, inputs): - """Forward function for feature maps before classifying each pixel with - ``self.cls_seg`` fc. - Args: - inputs (list[Tensor]): List of multi-level img features. - Returns: - feats (Tensor): A tensor of shape (batch_size, self.channels, - H, W) which is feature map for last layer of decoder head. - """ - # accept lists (for cls token) - inputs = list(inputs) - for i, x in enumerate(inputs): - if self.use_cls_token: - assert len(x) == 2, "Missing class tokens" - x, cls_token = x[0], x[1] - if len(x.shape) == 2: - x = x[:, :, None, None] - cls_token = cls_token[:, :, None, None].expand_as(x) - inputs[i] = torch.cat((x, cls_token), 1) - else: - if len(x.shape) == 2: - x = x[:, :, None, None] - inputs[i] = x - x = self._transform_inputs(inputs) - return x - - def forward(self, inputs): - """Forward function.""" - output = self._forward_feature(inputs) - output = self.dropout(output) - output = self.batchnorm_layer(output) - output = self.conv(output) - return output - - def predict(self, x, rescale_to=(512, 512)): - """ - Predict function used in evaluation. - No dropout is used, and the output is rescaled to the ground truth - for computing metrics. - """ - x = self._forward_feature(x) - x = self.batchnorm_layer(x) - x = self.conv(x) - x = F.interpolate(input=x, size=rescale_to, mode="bilinear") - return x diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/models/heads/mask2former_head.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/models/heads/mask2former_head.py deleted file mode 100644 index 2352af7277216ce0e55a62e7f99bedb3acbf8cb1..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/models/heads/mask2former_head.py +++ /dev/null @@ -1,96 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -# Copyright (c) Facebook, Inc. and its affiliates. -from typing import Dict, Tuple - -from torch import nn -from torch.nn import functional as F - -from dinov3.eval.segmentation.models.heads.pixel_decoder import MSDeformAttnPixelDecoder -from dinov3.eval.segmentation.models.heads.mask2former_transformer_decoder import MultiScaleMaskedTransformerDecoder - - -class Mask2FormerHead(nn.Module): - def __init__( - self, - input_shape: Dict[str, Tuple[int]], # ShapeSpec: [channels, height, width, stride] - hidden_dim: int = 2048, - num_classes: int = 150, - loss_weight: float = 1.0, - ignore_value: int = -1, - # extra parameters - transformer_in_feature: str = "multi_scale_pixel_decoder", - ): - """ - NOTE: this interface is experimental. - Args: - input_shape: shapes (channels and stride) of the input features - num_classes: number of classes to predict - pixel_decoder: the pixel decoder module - loss_weight: loss weight - ignore_value: category id to be ignored during training. - transformer_predictor: the transformer decoder that makes prediction - transformer_in_feature: input feature name to the transformer_predictor - """ - super().__init__() - orig_input_shape = input_shape - input_shape = sorted(input_shape.items(), key=lambda x: x[1][-1]) - self.in_features = [k for k, _ in input_shape] - - self.ignore_value = ignore_value - self.common_stride = 4 - self.loss_weight = loss_weight - - self.pixel_decoder = MSDeformAttnPixelDecoder( - input_shape=orig_input_shape, - transformer_dropout=0.0, - transformer_nheads=16, - transformer_dim_feedforward=4096, - transformer_enc_layers=6, - conv_dim=hidden_dim, - mask_dim=hidden_dim, - norm="GN", - transformer_in_features=["1", "2", "3", "4"], - common_stride=4, - ) - self.predictor = MultiScaleMaskedTransformerDecoder( - in_channels=hidden_dim, - mask_classification=True, - num_classes=num_classes, - hidden_dim=hidden_dim, - num_queries=100, - nheads=16, - dim_feedforward=4096, - dec_layers=9, - pre_norm=False, - mask_dim=hidden_dim, - enforce_input_project=False, - ) - - self.transformer_in_feature = transformer_in_feature - self.num_classes = num_classes - - def forward_features(self, features, mask=None): - return self.layers(features, mask) - - def forward(self, features, mask=None): - output = self.forward_features(features, mask) - return output - - def predict(self, features, mask=None, rescale_to=(512, 512)): - output = self.forward_features(features, mask) - output["pred_masks"] = F.interpolate( - output["pred_masks"], - size=rescale_to, - mode="bilinear", - align_corners=False, - ) - return output - - def layers(self, features, mask=None): - mask_features, _, multi_scale_features = self.pixel_decoder.forward_features(features) - predictions = self.predictor(multi_scale_features, mask_features, mask) - return predictions diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/models/heads/mask2former_transformer_decoder.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/models/heads/mask2former_transformer_decoder.py deleted file mode 100644 index b3ff3190f8cb8fe0708ba7b0e4990c1cc7fff7f0..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/models/heads/mask2former_transformer_decoder.py +++ /dev/null @@ -1,471 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -# Copyright (c) Facebook, Inc. and its affiliates. -# Adapted from: https://github.com/facebookresearch/detr/blob/master/models/detr.py - -from typing import Optional -import torch -from torch import nn, Tensor -from torch.nn import functional as F - -from dinov3.eval.segmentation.models.utils.position_encoding import PositionEmbeddingSine - - -def c2_xavier_fill(module: nn.Module) -> None: - """ - Initialize `module.weight` using the "XavierFill" implemented in Caffe2. - Also initializes `module.bias` to 0. - - Args: - module (torch.nn.Module): module to initialize. - """ - # Caffe2 implementation of XavierFill in fact - # corresponds to kaiming_uniform_ in PyTorch - # pyre-fixme[6]: For 1st param expected `Tensor` but got `Union[Module, Tensor]`. - nn.init.kaiming_uniform_(module.weight, a=1) - if module.bias is not None: - # pyre-fixme[6]: Expected `Tensor` for 1st param but got `Union[nn.Module, - # torch.Tensor]`. - nn.init.constant_(module.bias, 0) - - -class Conv2d(torch.nn.Conv2d): - """ - A wrapper around :class:`torch.nn.Conv2d` to support empty inputs and more features. - """ - - def __init__(self, *args, **kwargs): - """ - Extra keyword arguments supported in addition to those in `torch.nn.Conv2d`: - - Args: - norm (nn.Module, optional): a normalization layer - activation (callable(Tensor) -> Tensor): a callable activation function - - It assumes that norm layer is used before activation. - """ - norm = kwargs.pop("norm", None) - activation = kwargs.pop("activation", None) - super().__init__(*args, **kwargs) - - self.norm = norm - self.activation = activation - - def forward(self, x): - x = F.conv2d(x, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups) - if self.norm is not None: - x = self.norm(x) - if self.activation is not None: - x = self.activation(x) - return x - - -class SelfAttentionLayer(nn.Module): - def __init__(self, d_model, nhead, dropout=0.0, activation="relu", normalize_before=False): - super().__init__() - self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) - - self.norm = nn.LayerNorm(d_model) - self.dropout = nn.Dropout(dropout) - - self.activation = _get_activation_fn(activation) - self.normalize_before = normalize_before - - self._reset_parameters() - - def _reset_parameters(self): - for p in self.parameters(): - if p.dim() > 1: - nn.init.xavier_uniform_(p) - - def with_pos_embed(self, tensor, pos: Optional[Tensor]): - return tensor if pos is None else tensor + pos - - def forward_post( - self, - tgt, - tgt_mask: Optional[Tensor] = None, - tgt_key_padding_mask: Optional[Tensor] = None, - query_pos: Optional[Tensor] = None, - ): - q = k = self.with_pos_embed(tgt, query_pos) - tgt2 = self.self_attn(q, k, value=tgt, attn_mask=tgt_mask, key_padding_mask=tgt_key_padding_mask)[0] - tgt = tgt + self.dropout(tgt2) - tgt = self.norm(tgt) - - return tgt - - def forward_pre( - self, - tgt, - tgt_mask: Optional[Tensor] = None, - tgt_key_padding_mask: Optional[Tensor] = None, - query_pos: Optional[Tensor] = None, - ): - tgt2 = self.norm(tgt) - q = k = self.with_pos_embed(tgt2, query_pos) - tgt2 = self.self_attn(q, k, value=tgt2, attn_mask=tgt_mask, key_padding_mask=tgt_key_padding_mask)[0] - tgt = tgt + self.dropout(tgt2) - - return tgt - - def forward( - self, - tgt, - tgt_mask: Optional[Tensor] = None, - tgt_key_padding_mask: Optional[Tensor] = None, - query_pos: Optional[Tensor] = None, - ): - if self.normalize_before: - return self.forward_pre(tgt, tgt_mask, tgt_key_padding_mask, query_pos) - return self.forward_post(tgt, tgt_mask, tgt_key_padding_mask, query_pos) - - -class CrossAttentionLayer(nn.Module): - def __init__(self, d_model, nhead, dropout=0.0, activation="relu", normalize_before=False): - super().__init__() - self.multihead_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) - - self.norm = nn.LayerNorm(d_model) - self.dropout = nn.Dropout(dropout) - - self.activation = _get_activation_fn(activation) - self.normalize_before = normalize_before - - self._reset_parameters() - - def _reset_parameters(self): - for p in self.parameters(): - if p.dim() > 1: - nn.init.xavier_uniform_(p) - - def with_pos_embed(self, tensor, pos: Optional[Tensor]): - return tensor if pos is None else tensor + pos - - def forward_post( - self, - tgt, - memory, - memory_mask: Optional[Tensor] = None, - memory_key_padding_mask: Optional[Tensor] = None, - pos: Optional[Tensor] = None, - query_pos: Optional[Tensor] = None, - ): - tgt2 = self.multihead_attn( - query=self.with_pos_embed(tgt, query_pos), - key=self.with_pos_embed(memory, pos), - value=memory, - attn_mask=memory_mask, - key_padding_mask=memory_key_padding_mask, - )[0] - tgt = tgt + self.dropout(tgt2) - tgt = self.norm(tgt) - - return tgt - - def forward_pre( - self, - tgt, - memory, - memory_mask: Optional[Tensor] = None, - memory_key_padding_mask: Optional[Tensor] = None, - pos: Optional[Tensor] = None, - query_pos: Optional[Tensor] = None, - ): - tgt2 = self.norm(tgt) - tgt2 = self.multihead_attn( - query=self.with_pos_embed(tgt2, query_pos), - key=self.with_pos_embed(memory, pos), - value=memory, - attn_mask=memory_mask, - key_padding_mask=memory_key_padding_mask, - )[0] - tgt = tgt + self.dropout(tgt2) - - return tgt - - def forward( - self, - tgt, - memory, - memory_mask: Optional[Tensor] = None, - memory_key_padding_mask: Optional[Tensor] = None, - pos: Optional[Tensor] = None, - query_pos: Optional[Tensor] = None, - ): - if self.normalize_before: - return self.forward_pre(tgt, memory, memory_mask, memory_key_padding_mask, pos, query_pos) - return self.forward_post(tgt, memory, memory_mask, memory_key_padding_mask, pos, query_pos) - - -class FFNLayer(nn.Module): - def __init__(self, d_model, dim_feedforward=2048, dropout=0.0, activation="relu", normalize_before=False): - super().__init__() - # Implementation of Feedforward model - self.linear1 = nn.Linear(d_model, dim_feedforward) - self.dropout = nn.Dropout(dropout) - self.linear2 = nn.Linear(dim_feedforward, d_model) - - self.norm = nn.LayerNorm(d_model) - - self.activation = _get_activation_fn(activation) - self.normalize_before = normalize_before - - self._reset_parameters() - - def _reset_parameters(self): - for p in self.parameters(): - if p.dim() > 1: - nn.init.xavier_uniform_(p) - - def with_pos_embed(self, tensor, pos: Optional[Tensor]): - return tensor if pos is None else tensor + pos - - def forward_post(self, tgt): - tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt)))) - tgt = tgt + self.dropout(tgt2) - tgt = self.norm(tgt) - return tgt - - def forward_pre(self, tgt): - tgt2 = self.norm(tgt) - tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2)))) - tgt = tgt + self.dropout(tgt2) - return tgt - - def forward(self, tgt): - if self.normalize_before: - return self.forward_pre(tgt) - return self.forward_post(tgt) - - -def _get_activation_fn(activation): - """Return an activation function given a string""" - if activation == "relu": - return F.relu - if activation == "gelu": - return F.gelu - if activation == "glu": - return F.glu - raise RuntimeError(f"activation should be relu/gelu, not {activation}.") - - -class MLP(nn.Module): - """Very simple multi-layer perceptron (also called FFN)""" - - def __init__(self, input_dim, hidden_dim, output_dim, num_layers): - super().__init__() - self.num_layers = num_layers - h = [hidden_dim] * (num_layers - 1) - self.layers = nn.ModuleList(nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim])) - - def forward(self, x): - for i, layer in enumerate(self.layers): - x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x) - return x - - -class MultiScaleMaskedTransformerDecoder(nn.Module): - def __init__( - self, - in_channels, - mask_classification=True, - *, - num_classes: int, - hidden_dim: int, - num_queries: int, - nheads: int, - dim_feedforward: int, - dec_layers: int, - pre_norm: bool, - mask_dim: int, - enforce_input_project: bool, - ): - """ - NOTE: this interface is experimental. - Args: - in_channels: channels of the input features - mask_classification: whether to add mask classifier or not - num_classes: number of classes - hidden_dim: Transformer feature dimension - num_queries: number of queries - nheads: number of heads - dim_feedforward: feature dimension in feedforward network - enc_layers: number of Transformer encoder layers - dec_layers: number of Transformer decoder layers - pre_norm: whether to use pre-LayerNorm or not - mask_dim: mask feature dimension - enforce_input_project: add input project 1x1 conv even if input - channels and hidden dim is identical - """ - super().__init__() - - assert mask_classification, "Only support mask classification model" - self.mask_classification = mask_classification - - # positional encoding - N_steps = hidden_dim // 2 - self.pe_layer = PositionEmbeddingSine(N_steps, normalize=True) - - # define Transformer decoder here - self.num_heads = nheads - self.num_layers = dec_layers - self.transformer_self_attention_layers = nn.ModuleList() - self.transformer_cross_attention_layers = nn.ModuleList() - self.transformer_ffn_layers = nn.ModuleList() - - for _ in range(self.num_layers): - self.transformer_self_attention_layers.append( - SelfAttentionLayer( - d_model=hidden_dim, - nhead=nheads, - dropout=0.0, - normalize_before=pre_norm, - ) - ) - - self.transformer_cross_attention_layers.append( - CrossAttentionLayer( - d_model=hidden_dim, - nhead=nheads, - dropout=0.0, - normalize_before=pre_norm, - ) - ) - - self.transformer_ffn_layers.append( - FFNLayer( - d_model=hidden_dim, - dim_feedforward=dim_feedforward, - dropout=0.0, - normalize_before=pre_norm, - ) - ) - - self.post_norm = nn.LayerNorm(hidden_dim) - - self.num_queries = num_queries - # learnable query features - self.query_feat = nn.Embedding(num_queries, hidden_dim) - # learnable query p.e. - self.query_embed = nn.Embedding(num_queries, hidden_dim) - - # level embedding (we always use 3 scales) - self.num_feature_levels = 3 - self.level_embed = nn.Embedding(self.num_feature_levels, hidden_dim) - self.input_proj = nn.ModuleList() - for _ in range(self.num_feature_levels): - if in_channels != hidden_dim or enforce_input_project: - self.input_proj.append(Conv2d(in_channels, hidden_dim, kernel_size=1)) - c2_xavier_fill(self.input_proj[-1]) - else: - self.input_proj.append(nn.Sequential()) - - # output FFNs - if self.mask_classification: - self.class_embed = nn.Linear(hidden_dim, num_classes + 1) - self.mask_embed = MLP(hidden_dim, hidden_dim, mask_dim, 3) - - def forward(self, x, mask_features, mask=None): - # x is a list of multi-scale feature - assert len(x) == self.num_feature_levels - src = [] - pos = [] - size_list = [] - - # disable mask, it does not affect performance - del mask - - for i in range(self.num_feature_levels): - size_list.append(x[i].shape[-2:]) - pos.append(self.pe_layer(x[i], None).flatten(2)) - src.append(self.input_proj[i](x[i]).flatten(2) + self.level_embed.weight[i][None, :, None]) - - # flatten NxCxHxW to HWxNxC - pos[-1] = pos[-1].permute(2, 0, 1) - src[-1] = src[-1].permute(2, 0, 1) - - _, bs, _ = src[0].shape - - # QxNxC - query_embed = self.query_embed.weight.unsqueeze(1).repeat(1, bs, 1) - output = self.query_feat.weight.unsqueeze(1).repeat(1, bs, 1) - - predictions_class = [] - predictions_mask = [] - - # prediction heads on learnable query features - outputs_class, outputs_mask, attn_mask = self.forward_prediction_heads( - output, mask_features, attn_mask_target_size=size_list[0] - ) - predictions_class.append(outputs_class) - predictions_mask.append(outputs_mask) - - for i in range(self.num_layers): - level_index = i % self.num_feature_levels - attn_mask[torch.where(attn_mask.sum(-1) == attn_mask.shape[-1])] = False - # attention: cross-attention first - output = self.transformer_cross_attention_layers[i]( - output, - src[level_index], - memory_mask=attn_mask, - memory_key_padding_mask=None, # here we do not apply masking on padded region - pos=pos[level_index], - query_pos=query_embed, - ) - - output = self.transformer_self_attention_layers[i]( - output, tgt_mask=None, tgt_key_padding_mask=None, query_pos=query_embed - ) - - # FFN - output = self.transformer_ffn_layers[i](output) - - outputs_class, outputs_mask, attn_mask = self.forward_prediction_heads( - output, mask_features, attn_mask_target_size=size_list[(i + 1) % self.num_feature_levels] - ) - predictions_class.append(outputs_class) - predictions_mask.append(outputs_mask) - - assert len(predictions_class) == self.num_layers + 1 - - out = { - "pred_logits": predictions_class[-1], - "pred_masks": predictions_mask[-1], - "aux_outputs": self._set_aux_loss( - predictions_class if self.mask_classification else None, predictions_mask - ), - } - return out - - def forward_prediction_heads(self, output, mask_features, attn_mask_target_size): - decoder_output = self.post_norm(output) - decoder_output = decoder_output.transpose(0, 1) - outputs_class = self.class_embed(decoder_output) - mask_embed = self.mask_embed(decoder_output) - outputs_mask = torch.einsum("bqc,bchw->bqhw", mask_embed, mask_features) - - # NOTE: prediction is of higher-resolution - # [B, Q, H, W] -> [B, Q, H*W] -> [B, h, Q, H*W] -> [B*h, Q, HW] - attn_mask = F.interpolate(outputs_mask, size=attn_mask_target_size, mode="bilinear", align_corners=False) - # must use bool type - # If a BoolTensor is provided, positions with ``True`` are not allowed to attend while ``False`` values will be unchanged. - attn_mask = ( - attn_mask.sigmoid().flatten(2).unsqueeze(1).repeat(1, self.num_heads, 1, 1).flatten(0, 1) < 0.5 - ).bool() - attn_mask = attn_mask.detach() - - return outputs_class, outputs_mask, attn_mask - - @torch.jit.unused - def _set_aux_loss(self, outputs_class, outputs_seg_masks): - # this is a workaround to make torchscript happy, as torchscript - # doesn't support dictionary with non-homogeneous values, such - # as a dict having both a Tensor and a list. - if self.mask_classification: - return [{"pred_logits": a, "pred_masks": b} for a, b in zip(outputs_class[:-1], outputs_seg_masks[:-1])] - else: - return [{"pred_masks": b} for b in outputs_seg_masks[:-1]] diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/models/heads/pixel_decoder.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/models/heads/pixel_decoder.py deleted file mode 100644 index 693001db18b44f50ab966d5470d1f2fb1ec6ed03..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/models/heads/pixel_decoder.py +++ /dev/null @@ -1,413 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -# Copyright (c) Facebook, Inc. and its affiliates. -import numpy as np -from typing import Callable, Dict, List, Optional, Tuple, Union - -import torch -from torch import nn -from torch.nn import functional as F -from torch.nn.init import normal_ -from torch.amp import autocast - -from dinov3.eval.segmentation.models.utils.batch_norm import get_norm -from dinov3.eval.segmentation.models.utils.position_encoding import PositionEmbeddingSine -from dinov3.eval.segmentation.models.utils.transformer import _get_clones, _get_activation_fn -from dinov3.eval.segmentation.models.utils.ms_deform_attn import MSDeformAttn - - -def c2_xavier_fill(module: nn.Module) -> None: - """ - Initialize `module.weight` using the "XavierFill" implemented in Caffe2. - Also initializes `module.bias` to 0. - - Args: - module (torch.nn.Module): module to initialize. - """ - # Caffe2 implementation of XavierFill in fact - # corresponds to kaiming_uniform_ in PyTorch - # pyre-fixme[6]: For 1st param expected `Tensor` but got `Union[Module, Tensor]`. - nn.init.kaiming_uniform_(module.weight, a=1) - if module.bias is not None: - # pyre-fixme[6]: Expected `Tensor` for 1st param but got `Union[nn.Module, - # torch.Tensor]`. - nn.init.constant_(module.bias, 0) - - -class Conv2d(torch.nn.Conv2d): - """ - A wrapper around :class:`torch.nn.Conv2d` to support empty inputs and more features. - """ - - def __init__(self, *args, **kwargs): - """ - Extra keyword arguments supported in addition to those in `torch.nn.Conv2d`: - - Args: - norm (nn.Module, optional): a normalization layer - activation (callable(Tensor) -> Tensor): a callable activation function - - It assumes that norm layer is used before activation. - """ - norm = kwargs.pop("norm", None) - activation = kwargs.pop("activation", None) - super().__init__(*args, **kwargs) - - self.norm = norm - self.activation = activation - - def forward(self, x): - # torchscript does not support SyncBatchNorm yet - # https://github.com/pytorch/pytorch/issues/40507 - # and we skip these codes in torchscript since: - # 1. currently we only support torchscript in evaluation mode - # 2. features needed by exporting module to torchscript are added in PyTorch 1.6 or - # later version, `Conv2d` in these PyTorch versions has already supported empty inputs. - # if not torch.jit.is_scripting(): - # # Dynamo doesn't support context managers yet - # is_dynamo_compiling = check_if_dynamo_compiling() - # if not is_dynamo_compiling: - # with warnings.catch_warnings(record=True): - # if x.numel() == 0 and self.training: - # # https://github.com/pytorch/pytorch/issues/12013 - # assert not isinstance( - # self.norm, torch.nn.SyncBatchNorm - # ), "SyncBatchNorm does not support empty inputs!" - - x = F.conv2d(x, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups) - if self.norm is not None: - x = self.norm(x) - if self.activation is not None: - x = self.activation(x) - return x - - -# MSDeformAttn Transformer encoder in deformable detr -class MSDeformAttnTransformerEncoderOnly(nn.Module): - def __init__( - self, - d_model=256, - nhead=8, - num_encoder_layers=6, - dim_feedforward=1024, - dropout=0.1, - activation="relu", - num_feature_levels=4, - enc_n_points=4, - ): - super().__init__() - - self.d_model = d_model - self.nhead = nhead - - encoder_layer = MSDeformAttnTransformerEncoderLayer( - d_model, dim_feedforward, dropout, activation, num_feature_levels, nhead, enc_n_points - ) - self.encoder = MSDeformAttnTransformerEncoder(encoder_layer, num_encoder_layers) - - self.level_encoding = nn.Parameter(torch.Tensor(num_feature_levels, d_model)) - - self._reset_parameters() - - def _reset_parameters(self): - for p in self.parameters(): - if p.dim() > 1: - nn.init.xavier_uniform_(p) - for m in self.modules(): - if isinstance(m, MSDeformAttn): - m._reset_parameters() - normal_(self.level_encoding) - - def get_valid_ratio(self, mask): - _, H, W = mask.shape - valid_H = torch.sum(~mask[:, :, 0], 1) - valid_W = torch.sum(~mask[:, 0, :], 1) - valid_ratio_h = valid_H.float() / H - valid_ratio_w = valid_W.float() / W - valid_ratio = torch.stack([valid_ratio_w, valid_ratio_h], -1) - return valid_ratio - - def forward(self, srcs, pos_embeds): - masks = [torch.zeros((x.size(0), x.size(2), x.size(3)), device=x.device, dtype=torch.bool) for x in srcs] - # prepare input for encoder - src_flatten = [] - mask_flatten = [] - lvl_pos_embed_flatten = [] - spatial_shapes = [] - for lvl, (src, mask, pos_embed) in enumerate(zip(srcs, masks, pos_embeds)): - bs, c, h, w = src.shape - spatial_shape = (h, w) - spatial_shapes.append(spatial_shape) - src = src.flatten(2).transpose(1, 2) - mask = mask.flatten(1) - pos_embed = pos_embed.flatten(2).transpose(1, 2) - lvl_pos_embed = pos_embed + self.level_encoding[lvl].view(1, 1, -1) - lvl_pos_embed_flatten.append(lvl_pos_embed) - src_flatten.append(src) - mask_flatten.append(mask) - src_flatten = torch.cat(src_flatten, 1) - mask_flatten = torch.cat(mask_flatten, 1) - lvl_pos_embed_flatten = torch.cat(lvl_pos_embed_flatten, 1) - spatial_shapes = torch.as_tensor(spatial_shapes, dtype=torch.long, device=src_flatten.device) - level_start_index = torch.cat((spatial_shapes.new_zeros((1,)), spatial_shapes.prod(1).cumsum(0)[:-1])) - valid_ratios = torch.stack([self.get_valid_ratio(m) for m in masks], 1) - - # encoder - memory = self.encoder( - src_flatten, spatial_shapes, level_start_index, valid_ratios, lvl_pos_embed_flatten, mask_flatten - ) - - return memory, spatial_shapes, level_start_index - - -class MSDeformAttnTransformerEncoderLayer(nn.Module): - def __init__(self, d_model=256, d_ffn=1024, dropout=0.1, activation="relu", n_levels=4, n_heads=8, n_points=4): - super().__init__() - - # self attention - self.self_attn = MSDeformAttn(d_model, n_levels, n_heads, n_points) - self.dropout1 = nn.Dropout(dropout) - self.norm1 = nn.LayerNorm(d_model) - - # ffn - self.linear1 = nn.Linear(d_model, d_ffn) - self.activation = _get_activation_fn(activation) - self.dropout2 = nn.Dropout(dropout) - self.linear2 = nn.Linear(d_ffn, d_model) - self.dropout3 = nn.Dropout(dropout) - self.norm2 = nn.LayerNorm(d_model) - - @staticmethod - def with_pos_embed(tensor, pos): - return tensor if pos is None else tensor + pos - - def forward_ffn(self, src): - src2 = self.linear2(self.dropout2(self.activation(self.linear1(src)))) - src = src + self.dropout3(src2) - src = self.norm2(src) - return src - - def forward(self, src, pos, reference_points, spatial_shapes, level_start_index, padding_mask=None): - # self attention - src2 = self.self_attn( - self.with_pos_embed(src, pos), reference_points, src, spatial_shapes, level_start_index, padding_mask - ) - src = src + self.dropout1(src2) - src = self.norm1(src) - - # ffn - src = self.forward_ffn(src) - - return src - - -class MSDeformAttnTransformerEncoder(nn.Module): - def __init__(self, encoder_layer, num_layers): - super().__init__() - self.layers = _get_clones(encoder_layer, num_layers) - self.num_layers = num_layers - - @staticmethod - def get_reference_points(spatial_shapes, valid_ratios, device): - reference_points_list = [] - for lvl, (H_, W_) in enumerate(spatial_shapes): - ref_y, ref_x = torch.meshgrid( - torch.linspace(0.5, H_ - 0.5, H_, dtype=torch.float32, device=device), - torch.linspace(0.5, W_ - 0.5, W_, dtype=torch.float32, device=device), - ) - ref_y = ref_y.reshape(-1)[None] / (valid_ratios[:, None, lvl, 1] * H_) - ref_x = ref_x.reshape(-1)[None] / (valid_ratios[:, None, lvl, 0] * W_) - ref = torch.stack((ref_x, ref_y), -1) - reference_points_list.append(ref) - reference_points = torch.cat(reference_points_list, 1) - reference_points = reference_points[:, :, None] * valid_ratios[:, None] - return reference_points - - def forward(self, src, spatial_shapes, level_start_index, valid_ratios, pos=None, padding_mask=None): - output = src - reference_points = self.get_reference_points(spatial_shapes, valid_ratios, device=src.device) - for _, layer in enumerate(self.layers): - output = layer(output, pos, reference_points, spatial_shapes, level_start_index, padding_mask) - - return output - - -# @SEM_SEG_HEADS_REGISTRY.register() -class MSDeformAttnPixelDecoder(nn.Module): - # @configurable - def __init__( - self, - input_shape: Dict[str, Tuple[int]], # ShapeSpec: [channels, height, width, stride] - *, - transformer_dropout: float, - transformer_nheads: int, - transformer_dim_feedforward: int, - transformer_enc_layers: int, - conv_dim: int, - mask_dim: int, - norm: Optional[Union[str, Callable]] = None, - # deformable transformer encoder args - transformer_in_features: List[str], - common_stride: int, - ): - """ - NOTE: this interface is experimental. - Args: - input_shape: shapes (channels and stride) of the input features - transformer_dropout: dropout probability in transformer - transformer_nheads: number of heads in transformer - transformer_dim_feedforward: dimension of feedforward network - transformer_enc_layers: number of transformer encoder layers - conv_dims: number of output channels for the intermediate conv layers. - mask_dim: number of output channels for the final conv layer. - norm (str or callable): normalization for all conv layers - """ - super().__init__() - transformer_input_shape = {k: v for k, v in input_shape.items() if k in transformer_in_features} - - # this is the input shape of pixel decoder # ShapeSpec: [channels, height, width, stride] - input_shape = sorted(input_shape.items(), key=lambda x: x[1][-1]) - self.in_features = [k for k, v in input_shape] # starting from "res2" to "res5" - self.feature_strides = [v[-1] for k, v in input_shape] - self.feature_channels = [v[0] for k, v in input_shape] - - # this is the input shape of transformer encoder (could use less features than pixel decoder - transformer_input_shape = sorted(transformer_input_shape.items(), key=lambda x: x[1][-1]) - self.transformer_in_features = [k for k, v in transformer_input_shape] # starting from "res2" to "res5" - transformer_in_channels = [v[0] for k, v in transformer_input_shape] - self.transformer_feature_strides = [v[-1] for k, v in transformer_input_shape] # to decide extra FPN layers - - self.transformer_num_feature_levels = 3 # TODO switch with len(self.transformer_in_features) - if self.transformer_num_feature_levels > 1: - input_proj_list = [] - # from low resolution to high resolution (res5 -> res2) - for in_channels in transformer_in_channels[::-1][:-1]: # TODO remove [:-1] - input_proj_list.append( - nn.Sequential( - nn.Conv2d(in_channels, conv_dim, kernel_size=1), - nn.GroupNorm(32, conv_dim), - ) - ) - self.input_convs = nn.ModuleList(input_proj_list) - else: - self.input_convs = nn.ModuleList( - [ - nn.Sequential( - nn.Conv2d(transformer_in_channels[-1], conv_dim, kernel_size=1), - nn.GroupNorm(32, conv_dim), - ) - ] - ) - - for proj in self.input_convs: - nn.init.xavier_uniform_(proj[0].weight, gain=1) - nn.init.constant_(proj[0].bias, 0) - - self.encoder = MSDeformAttnTransformerEncoderOnly( - d_model=conv_dim, - dropout=transformer_dropout, - nhead=transformer_nheads, - dim_feedforward=transformer_dim_feedforward, - num_encoder_layers=transformer_enc_layers, - num_feature_levels=self.transformer_num_feature_levels, - ) - N_steps = conv_dim // 2 - self.pe_layer = PositionEmbeddingSine(N_steps, normalize=True) - - self.mask_dim = mask_dim - # use 1x1 conv instead - self.mask_feature = Conv2d( - conv_dim, - mask_dim, - kernel_size=1, - stride=1, - padding=0, - ) - c2_xavier_fill(self.mask_feature) - - self.maskformer_num_feature_levels = 3 # always use 3 scales - self.common_stride = common_stride - - # extra fpn levels - stride = min(self.transformer_feature_strides) - self.num_fpn_levels = int(np.log2(stride) - np.log2(self.common_stride)) - - lateral_convs = [] - output_convs = [] - - use_bias = norm == "" - for idx, in_channels in enumerate(self.feature_channels[:1]): # TODO self.num_fpn_levels]): - lateral_norm = get_norm(norm, conv_dim) - output_norm = get_norm(norm, conv_dim) - - lateral_conv = Conv2d(in_channels, conv_dim, kernel_size=1, bias=use_bias, norm=lateral_norm) - output_conv = Conv2d( - conv_dim, - conv_dim, - kernel_size=3, - stride=1, - padding=1, - bias=use_bias, - norm=output_norm, - activation=F.relu, - ) - c2_xavier_fill(lateral_conv) - c2_xavier_fill(output_conv) - # self.add_module("lateral_convs".format(idx + 1), lateral_conv) # TODO replace "adapter_{}" - # self.add_module("output_convs".format(idx + 1), output_conv) # TODO replace layer_{}"" - - lateral_convs.append(lateral_conv) - output_convs.append(output_conv) - # Place convs into top-down order (from low to high resolution) - # to make the top-down computation in forward clearer. - self.lateral_convs = nn.ModuleList(lateral_convs[::-1]) - self.output_convs = nn.ModuleList(output_convs[::-1]) - - @autocast(device_type="cuda", enabled=False) - def forward_features(self, features): - srcs = [] - pos = [] - # Reverse feature maps into top-down order (from low to high resolution) - for idx, f in enumerate(self.transformer_in_features[::-1][:-1]): # TODO remove [:-1] - x = features[f].float() # deformable detr does not support half precision - srcs.append(self.input_convs[idx](x)) - pos.append(self.pe_layer(x)) - - y, spatial_shapes, level_start_index = self.encoder(srcs, pos) - bs = y.shape[0] - - split_size_or_sections = [None] * self.transformer_num_feature_levels - for i in range(self.transformer_num_feature_levels): - if i < self.transformer_num_feature_levels - 1: - split_size_or_sections[i] = level_start_index[i + 1] - level_start_index[i] - else: - split_size_or_sections[i] = y.shape[1] - level_start_index[i] - y = torch.split(y, split_size_or_sections, dim=1) - - out = [] - multi_scale_features = [] - num_cur_levels = 0 - for i, z in enumerate(y): - out.append(z.transpose(1, 2).view(bs, -1, spatial_shapes[i][0], spatial_shapes[i][1])) - - # append `out` with extra FPN levels - # Reverse feature maps into top-down order (from low to high resolution) - for idx, f in enumerate(self.in_features[0]): # TODO re put [:self.num_fpn_levels][::-1]): - x = features[f].float() - lateral_conv = self.lateral_convs[idx] - output_conv = self.output_convs[idx] - cur_fpn = lateral_conv(x) - # Following FPN implementation, we use nearest upsampling here - y = cur_fpn + F.interpolate(out[-1], size=cur_fpn.shape[-2:], mode="bilinear", align_corners=False) - y = output_conv(y) - out.append(y) - - for o in out: - if num_cur_levels < self.maskformer_num_feature_levels: - multi_scale_features.append(o) - num_cur_levels += 1 - - return self.mask_feature(out[-1]), out[0], multi_scale_features diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/models/utils/batch_norm.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/models/utils/batch_norm.py deleted file mode 100644 index 1dc0c94cf166e5595c503c2d2ff9b5e4a8c8a998..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/models/utils/batch_norm.py +++ /dev/null @@ -1,355 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -# Copyright (c) Facebook, Inc. and its affiliates. -import torch -import torch.distributed as dist -from torch import nn -from torch.nn import functional as F -from torch.nn import BatchNorm2d - -import dinov3.distributed as distributed - - -class FrozenBatchNorm2d(nn.Module): - """ - BatchNorm2d where the batch statistics and the affine parameters are fixed. - - It contains non-trainable buffers called - "weight" and "bias", "running_mean", "running_var", - initialized to perform identity transformation. - - The pre-trained backbone models from Caffe2 only contain "weight" and "bias", - which are computed from the original four parameters of BN. - The affine transform `x * weight + bias` will perform the equivalent - computation of `(x - running_mean) / sqrt(running_var) * weight + bias`. - When loading a backbone model from Caffe2, "running_mean" and "running_var" - will be left unchanged as identity transformation. - - Other pre-trained backbone models may contain all 4 parameters. - - The forward is implemented by `F.batch_norm(..., training=False)`. - """ - - _version = 3 - - def __init__(self, num_features, eps=1e-5): - super().__init__() - self.num_features = num_features - self.eps = eps - self.register_buffer("weight", torch.ones(num_features)) - self.register_buffer("bias", torch.zeros(num_features)) - self.register_buffer("running_mean", torch.zeros(num_features)) - self.register_buffer("running_var", torch.ones(num_features) - eps) - self.register_buffer("num_batches_tracked", None) - - def forward(self, x): - if x.requires_grad: - # When gradients are needed, F.batch_norm will use extra memory - # because its backward op computes gradients for weight/bias as well. - scale = self.weight * (self.running_var + self.eps).rsqrt() - bias = self.bias - self.running_mean * scale - scale = scale.reshape(1, -1, 1, 1) - bias = bias.reshape(1, -1, 1, 1) - out_dtype = x.dtype # may be half - return x * scale.to(out_dtype) + bias.to(out_dtype) - else: - # When gradients are not needed, F.batch_norm is a single fused op - # and provide more optimization opportunities. - return F.batch_norm( - x, - self.running_mean, - self.running_var, - self.weight, - self.bias, - training=False, - eps=self.eps, - ) - - def _load_from_state_dict( - self, - state_dict, - prefix, - local_metadata, - strict, - missing_keys, - unexpected_keys, - error_msgs, - ): - version = local_metadata.get("version", None) - - if version is None or version < 2: - # No running_mean/var in early versions - # This will silent the warnings - if prefix + "running_mean" not in state_dict: - state_dict[prefix + "running_mean"] = torch.zeros_like(self.running_mean) - if prefix + "running_var" not in state_dict: - state_dict[prefix + "running_var"] = torch.ones_like(self.running_var) - - super()._load_from_state_dict( - state_dict, - prefix, - local_metadata, - strict, - missing_keys, - unexpected_keys, - error_msgs, - ) - - def __repr__(self): - return "FrozenBatchNorm2d(num_features={}, eps={})".format(self.num_features, self.eps) - - @classmethod - def convert_frozen_batchnorm(cls, module): - """ - Convert all BatchNorm/SyncBatchNorm in module into FrozenBatchNorm. - - Args: - module (torch.nn.Module): - - Returns: - If module is BatchNorm/SyncBatchNorm, returns a new module. - Otherwise, in-place convert module and return it. - - Similar to convert_sync_batchnorm in - https://github.com/pytorch/pytorch/blob/master/torch/nn/modules/batchnorm.py - """ - bn_module = nn.modules.batchnorm - bn_module = (bn_module.BatchNorm2d, bn_module.SyncBatchNorm) - res = module - if isinstance(module, bn_module): - res = cls(module.num_features) - if module.affine: - res.weight.data = module.weight.data.clone().detach() - res.bias.data = module.bias.data.clone().detach() - res.running_mean.data = module.running_mean.data - res.running_var.data = module.running_var.data - res.eps = module.eps - res.num_batches_tracked = module.num_batches_tracked - else: - for name, child in module.named_children(): - new_child = cls.convert_frozen_batchnorm(child) - if new_child is not child: - res.add_module(name, new_child) - return res - - @classmethod - def convert_frozenbatchnorm2d_to_batchnorm2d(cls, module: nn.Module) -> nn.Module: - """ - Convert all FrozenBatchNorm2d to BatchNorm2d - - Args: - module (torch.nn.Module): - - Returns: - If module is FrozenBatchNorm2d, returns a new module. - Otherwise, in-place convert module and return it. - - This is needed for quantization. - """ - - res = module - if isinstance(module, FrozenBatchNorm2d): - res = torch.nn.BatchNorm2d(module.num_features, module.eps) - - res.weight.data = module.weight.data.clone().detach() - res.bias.data = module.bias.data.clone().detach() - res.running_mean.data = module.running_mean.data.clone().detach() - res.running_var.data = module.running_var.data.clone().detach() - res.eps = module.eps - res.num_batches_tracked = module.num_batches_tracked - else: - for name, child in module.named_children(): - new_child = cls.convert_frozenbatchnorm2d_to_batchnorm2d(child) - if new_child is not child: - res.add_module(name, new_child) - return res - - -def get_norm(norm, out_channels): - """ - Args: - norm (str or callable): either one of BN, SyncBN, FrozenBN, GN; - or a callable that takes a channel number and returns - the normalization layer as a nn.Module. - - Returns: - nn.Module or None: the normalization layer - """ - if norm is None: - return None - if isinstance(norm, str): - if len(norm) == 0: - return None - norm = { - "BN": BatchNorm2d, - # Fixed in https://github.com/pytorch/pytorch/pull/36382 - "SyncBN": nn.SyncBatchNorm, - "FrozenBN": FrozenBatchNorm2d, - "GN": lambda channels: nn.GroupNorm(32, channels), - # for debugging: - "nnSyncBN": nn.SyncBatchNorm, - "naiveSyncBN": NaiveSyncBatchNorm, - # expose stats_mode N as an option to caller, required for zero-len inputs - "naiveSyncBN_N": lambda channels: NaiveSyncBatchNorm(channels, stats_mode="N"), - "LN": lambda channels: LayerNorm(channels), - }[norm] - return norm(out_channels) - - -class NaiveSyncBatchNorm(BatchNorm2d): - """ - In PyTorch<=1.5, ``nn.SyncBatchNorm`` has incorrect gradient - when the batch size on each worker is different. - (e.g., when scale augmentation is used, or when it is applied to mask head). - - This is a slower but correct alternative to `nn.SyncBatchNorm`. - - Note: - There isn't a single definition of Sync BatchNorm. - - When ``stats_mode==""``, this module computes overall statistics by using - statistics of each worker with equal weight. The result is true statistics - of all samples (as if they are all on one worker) only when all workers - have the same (N, H, W). This mode does not support inputs with zero batch size. - - When ``stats_mode=="N"``, this module computes overall statistics by weighting - the statistics of each worker by their ``N``. The result is true statistics - of all samples (as if they are all on one worker) only when all workers - have the same (H, W). It is slower than ``stats_mode==""``. - - Even though the result of this module may not be the true statistics of all samples, - it may still be reasonable because it might be preferrable to assign equal weights - to all workers, regardless of their (H, W) dimension, instead of putting larger weight - on larger images. From preliminary experiments, little difference is found between such - a simplified implementation and an accurate computation of overall mean & variance. - """ - - def __init__(self, *args, stats_mode="", **kwargs): - super().__init__(*args, **kwargs) - assert stats_mode in ["", "N"] - self._stats_mode = stats_mode - - def forward(self, input): - if distributed.get_world_size() == 1 or not self.training: - return super().forward(input) - - B, C = input.shape[0], input.shape[1] - - half_input = input.dtype == torch.float16 - if half_input: - # fp16 does not have good enough numerics for the reduction here - input = input.float() - mean = torch.mean(input, dim=[0, 2, 3]) - meansqr = torch.mean(input * input, dim=[0, 2, 3]) - - if self._stats_mode == "": - assert B > 0, 'SyncBatchNorm(stats_mode="") does not support zero batch size.' - vec = torch.cat([mean, meansqr], dim=0) - vec = torch.distributed.nn.all_reduce(vec) * (1.0 / dist.get_world_size()) - mean, meansqr = torch.split(vec, C) - momentum = self.momentum - else: - if B == 0: - vec = torch.zeros([2 * C + 1], device=mean.device, dtype=mean.dtype) - vec = vec + input.sum() # make sure there is gradient w.r.t input - else: - vec = torch.cat( - [ - mean, - meansqr, - torch.ones([1], device=mean.device, dtype=mean.dtype), - ], - dim=0, - ) - vec = torch.distributed.nn.all_reduce(vec * B) - - total_batch = vec[-1].detach() - momentum = total_batch.clamp(max=1) * self.momentum # no update if total_batch is 0 - mean, meansqr, _ = torch.split(vec / total_batch.clamp(min=1), C) # avoid div-by-zero - - var = meansqr - mean * mean - invstd = torch.rsqrt(var + self.eps) - scale = self.weight * invstd - bias = self.bias - mean * scale - scale = scale.reshape(1, -1, 1, 1) - bias = bias.reshape(1, -1, 1, 1) - - self.running_mean += momentum * (mean.detach() - self.running_mean) - self.running_var += momentum * (var.detach() - self.running_var) - ret = input * scale + bias - if half_input: - ret = ret.half() - return ret - - -class CycleBatchNormList(nn.ModuleList): - """ - Implement domain-specific BatchNorm by cycling. - - When a BatchNorm layer is used for multiple input domains or input - features, it might need to maintain a separate test-time statistics - for each domain. See Sec 5.2 in :paper:`rethinking-batchnorm`. - - This module implements it by using N separate BN layers - and it cycles through them every time a forward() is called. - - NOTE: The caller of this module MUST guarantee to always call - this module by multiple of N times. Otherwise its test-time statistics - will be incorrect. - """ - - def __init__(self, length: int, bn_class=nn.BatchNorm2d, **kwargs): - """ - Args: - length: number of BatchNorm layers to cycle. - bn_class: the BatchNorm class to use - kwargs: arguments of the BatchNorm class, such as num_features. - """ - self._affine = kwargs.pop("affine", True) - super().__init__([bn_class(**kwargs, affine=False) for k in range(length)]) - if self._affine: - # shared affine, domain-specific BN - channels = self[0].num_features - self.weight = nn.Parameter(torch.ones(channels)) - self.bias = nn.Parameter(torch.zeros(channels)) - self._pos = 0 - - def forward(self, x): - ret = self[self._pos](x) - self._pos = (self._pos + 1) % len(self) - - if self._affine: - w = self.weight.reshape(1, -1, 1, 1) - b = self.bias.reshape(1, -1, 1, 1) - return ret * w + b - else: - return ret - - def extra_repr(self): - return f"affine={self._affine}" - - -class LayerNorm(nn.Module): - """ - A LayerNorm variant, popularized by Transformers, that performs point-wise mean and - variance normalization over the channel dimension for inputs that have shape - (batch_size, channels, height, width). - https://github.com/facebookresearch/ConvNeXt/blob/d1fa8f6fef0a165b27399986cc2bdacc92777e40/models/convnext.py#L119 # noqa B950 - """ - - def __init__(self, normalized_shape, eps=1e-6): - super().__init__() - self.weight = nn.Parameter(torch.ones(normalized_shape)) - self.bias = nn.Parameter(torch.zeros(normalized_shape)) - self.eps = eps - self.normalized_shape = (normalized_shape,) - - def forward(self, x): - u = x.mean(1, keepdim=True) - s = (x - u).pow(2).mean(1, keepdim=True) - x = (x - u) / torch.sqrt(s + self.eps) - x = self.weight[:, None, None] * x + self.bias[:, None, None] - return x diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/models/utils/ms_deform_attn.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/models/utils/ms_deform_attn.py deleted file mode 100644 index fd662c9b4ac38f2658944a66eef49869fdea96f5..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/models/utils/ms_deform_attn.py +++ /dev/null @@ -1,214 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import math -import warnings - -import torch -import torch.nn.functional as F -from torch import nn -from torch.autograd import Function -from torch.amp import custom_fwd, custom_bwd - -from torch.autograd.function import once_differentiable -from torch.nn.init import constant_, xavier_uniform_ - -try: - import MultiScaleDeformableAttention as MSDA -except ImportError: - # if we just care about inference, we don't need - # the compiled extension for multi-scale deformable attention - MSDA = None - - -class MSDeformAttnFunction(Function): - @staticmethod - @custom_fwd(device_type="cuda", cast_inputs=torch.float32) - def forward( - ctx, value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights, im2col_step - ): - ctx.im2col_step = im2col_step - output = ms_deform_attn_core_pytorch( - value, - value_spatial_shapes, - # value_level_start_index, - sampling_locations, - attention_weights, - ) - ctx.save_for_backward( - value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights - ) - return output - - @staticmethod - @once_differentiable - @custom_bwd(device_type="cuda") - def backward(ctx, grad_output): - if MSDA is None: - raise RuntimeError( - "MultiScaleDeformableAttention is not available, " - "please compile with CUDA if you want to train a " - "segmentation head with deformable attention" - ) - value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights = ctx.saved_tensors - grad_value, grad_sampling_loc, grad_attn_weight = MSDA.ms_deform_attn_backward( - value, - value_spatial_shapes, - value_level_start_index, - sampling_locations, - attention_weights, - grad_output, - ctx.im2col_step, - ) - - return grad_value, None, None, grad_sampling_loc, grad_attn_weight, None - - -def ms_deform_attn_core_pytorch(value, value_spatial_shapes, sampling_locations, attention_weights): - # for debug and test only, - # need to use cuda version instead - N_, S_, M_, D_ = value.shape - _, Lq_, M_, L_, P_, _ = sampling_locations.shape - value_list = value.split([H_ * W_ for H_, W_ in value_spatial_shapes], dim=1) - sampling_grids = 2 * sampling_locations - 1 - sampling_value_list = [] - for lid_, (H_, W_) in enumerate(value_spatial_shapes): - # N_, H_*W_, M_, D_ -> N_, H_*W_, M_*D_ -> N_, M_*D_, H_*W_ -> N_*M_, D_, H_, W_ - value_l_ = value_list[lid_].flatten(2).transpose(1, 2).reshape(N_ * M_, D_, H_, W_) - # N_, Lq_, M_, P_, 2 -> N_, M_, Lq_, P_, 2 -> N_*M_, Lq_, P_, 2 - sampling_grid_l_ = sampling_grids[:, :, :, lid_].transpose(1, 2).flatten(0, 1) - # N_*M_, D_, Lq_, P_ - sampling_value_l_ = F.grid_sample( - value_l_, sampling_grid_l_, mode="bilinear", padding_mode="zeros", align_corners=False - ) - sampling_value_list.append(sampling_value_l_) - # (N_, Lq_, M_, L_, P_) -> (N_, M_, Lq_, L_, P_) -> (N_, M_, 1, Lq_, L_*P_) - attention_weights = attention_weights.transpose(1, 2).reshape(N_ * M_, 1, Lq_, L_ * P_) - output = (torch.stack(sampling_value_list, dim=-2).flatten(-2) * attention_weights).sum(-1).view(N_, M_ * D_, Lq_) - return output.transpose(1, 2).contiguous() - - -def _is_power_of_2(n): - if (not isinstance(n, int)) or (n < 0): - raise ValueError("invalid input for _is_power_of_2: {} (type: {})".format(n, type(n))) - return (n & (n - 1) == 0) and n != 0 - - -class MSDeformAttn(nn.Module): - def __init__(self, d_model=256, n_levels=4, n_heads=8, n_points=4, ratio=1.0): - """Multi-Scale Deformable Attention Module. - - :param d_model hidden dimension - :param n_levels number of feature levels - :param n_heads number of attention heads - :param n_points number of sampling points per attention head per feature level - """ - super().__init__() - if d_model % n_heads != 0: - raise ValueError("d_model must be divisible by n_heads, but got {} and {}".format(d_model, n_heads)) - _d_per_head = d_model // n_heads - # you'd better set _d_per_head to a power of 2 - # which is more efficient in our CUDA implementation - if not _is_power_of_2(_d_per_head): - warnings.warn( - "You'd better set d_model in MSDeformAttn to make " - "the dimension of each attention head a power of 2 " - "which is more efficient in our CUDA implementation." - ) - - self.im2col_step = 64 - - self.d_model = d_model - self.n_levels = n_levels - self.n_heads = n_heads - self.n_points = n_points - self.ratio = ratio - self.sampling_offsets = nn.Linear(d_model, n_heads * n_levels * n_points * 2) - self.attention_weights = nn.Linear(d_model, n_heads * n_levels * n_points) - self.value_proj = nn.Linear(d_model, int(d_model * ratio)) - self.output_proj = nn.Linear(int(d_model * ratio), d_model) - - self._reset_parameters() - - def _reset_parameters(self): - constant_(self.sampling_offsets.weight.data, 0.0) - thetas = torch.arange(self.n_heads, dtype=torch.float32) * (2.0 * math.pi / self.n_heads) - grid_init = torch.stack([thetas.cos(), thetas.sin()], -1) - grid_init = ( - (grid_init / grid_init.abs().max(-1, keepdim=True)[0]) - .view(self.n_heads, 1, 1, 2) - .repeat(1, self.n_levels, self.n_points, 1) - ) - for i in range(self.n_points): - grid_init[:, :, i, :] *= i + 1 - - with torch.no_grad(): - self.sampling_offsets.bias = nn.Parameter(grid_init.view(-1)) - constant_(self.attention_weights.weight.data, 0.0) - constant_(self.attention_weights.bias.data, 0.0) - xavier_uniform_(self.value_proj.weight.data) - constant_(self.value_proj.bias.data, 0.0) - xavier_uniform_(self.output_proj.weight.data) - constant_(self.output_proj.bias.data, 0.0) - - def forward( - self, - query, - reference_points, - input_flatten, - input_spatial_shapes, - input_level_start_index, - input_padding_mask=None, - ): - """ - :param query (N, Length_{query}, C) - :param reference_points (N, Length_{query}, n_levels, 2), range in [0, 1], top-left (0,0), bottom-right (1, 1), including padding area - or (N, Length_{query}, n_levels, 4), add additional (w, h) to form reference boxes - :param input_flatten (N, \\sum_{l=0}^{L-1} H_l \\cdot W_l, C) - :param input_spatial_shapes (n_levels, 2), [(H_0, W_0), (H_1, W_1), ..., (H_{L-1}, W_{L-1})] - :param input_level_start_index (n_levels, ), [0, H_0*W_0, H_0*W_0+H_1*W_1, H_0*W_0+H_1*W_1+H_2*W_2, ..., H_0*W_0+H_1*W_1+...+H_{L-1}*W_{L-1}] - :param input_padding_mask (N, \\sum_{l=0}^{L-1} H_l \\cdot W_l), True for padding elements, False for non-padding elements - - :return output (N, Length_{query}, C) - """ - - N, Len_q, _ = query.shape - N, Len_in, _ = input_flatten.shape - assert (input_spatial_shapes[:, 0] * input_spatial_shapes[:, 1]).sum() == Len_in - - value = self.value_proj(input_flatten) - if input_padding_mask is not None: - value = value.masked_fill(input_padding_mask[..., None], float(0)) - - value = value.view(N, Len_in, self.n_heads, int(self.ratio * self.d_model) // self.n_heads) - sampling_offsets = self.sampling_offsets(query).view(N, Len_q, self.n_heads, self.n_levels, self.n_points, 2) - attention_weights = self.attention_weights(query).view(N, Len_q, self.n_heads, self.n_levels * self.n_points) - attention_weights = F.softmax(attention_weights, -1).view(N, Len_q, self.n_heads, self.n_levels, self.n_points) - - if reference_points.shape[-1] == 2: - offset_normalizer = torch.stack([input_spatial_shapes[..., 1], input_spatial_shapes[..., 0]], -1) - sampling_locations = ( - reference_points[:, :, None, :, None, :] - + sampling_offsets / offset_normalizer[None, None, None, :, None, :] - ) - elif reference_points.shape[-1] == 4: - sampling_locations = ( - reference_points[:, :, None, :, None, :2] - + sampling_offsets / self.n_points * reference_points[:, :, None, :, None, 2:] * 0.5 - ) - else: - raise ValueError( - "Last dim of reference_points must be 2 or 4, but get {} instead.".format(reference_points.shape[-1]) - ) - output = MSDeformAttnFunction.apply( - value, - input_spatial_shapes, - input_level_start_index, - sampling_locations, - attention_weights, - self.im2col_step, - ) - output = self.output_proj(output) - return output diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/models/utils/ops/functions/__init__.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/models/utils/ops/functions/__init__.py deleted file mode 100644 index ce67775be80031ef924d64478192cbf616fc988d..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/models/utils/ops/functions/__init__.py +++ /dev/null @@ -1,14 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -# ------------------------------------------------------------------------------------------------ -# Deformable DETR -# Copyright (c) 2020 SenseTime. All Rights Reserved. -# Licensed under the Apache License, Version 2.0 [see LICENSE for details] -# ------------------------------------------------------------------------------------------------ -# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 -# ------------------------------------------------------------------------------------------------ - -from .ms_deform_attn_func import MSDeformAttnFunction diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/models/utils/ops/functions/ms_deform_attn_func.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/models/utils/ops/functions/ms_deform_attn_func.py deleted file mode 100644 index fa343cbed0f1e28af7f9006fe5bc9cbaabbe6aa8..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/models/utils/ops/functions/ms_deform_attn_func.py +++ /dev/null @@ -1,78 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -# ------------------------------------------------------------------------------------------------ -# Deformable DETR -# Copyright (c) 2020 SenseTime. All Rights Reserved. -# Licensed under the Apache License, Version 2.0 [see LICENSE for details] -# ------------------------------------------------------------------------------------------------ -# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 -# ------------------------------------------------------------------------------------------------ - -from __future__ import absolute_import -from __future__ import print_function -from __future__ import division - -import torch -import torch.nn.functional as F -from torch.autograd import Function -from torch.autograd.function import once_differentiable - -import MultiScaleDeformableAttention as MSDA - - -class MSDeformAttnFunction(Function): - @staticmethod - def forward( - ctx, value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights, im2col_step - ): - ctx.im2col_step = im2col_step - output = MSDA.ms_deform_attn_forward( - value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights, ctx.im2col_step - ) - ctx.save_for_backward( - value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights - ) - return output - - @staticmethod - @once_differentiable - def backward(ctx, grad_output): - value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights = ctx.saved_tensors - grad_value, grad_sampling_loc, grad_attn_weight = MSDA.ms_deform_attn_backward( - value, - value_spatial_shapes, - value_level_start_index, - sampling_locations, - attention_weights, - grad_output, - ctx.im2col_step, - ) - - return grad_value, None, None, grad_sampling_loc, grad_attn_weight, None - - -def ms_deform_attn_core_pytorch(value, value_spatial_shapes, sampling_locations, attention_weights): - # for debug and test only, - # need to use cuda version instead - N_, S_, M_, D_ = value.shape - _, Lq_, M_, L_, P_, _ = sampling_locations.shape - value_list = value.split([H_ * W_ for H_, W_ in value_spatial_shapes], dim=1) - sampling_grids = 2 * sampling_locations - 1 - sampling_value_list = [] - for lid_, (H_, W_) in enumerate(value_spatial_shapes): - # N_, H_*W_, M_, D_ -> N_, H_*W_, M_*D_ -> N_, M_*D_, H_*W_ -> N_*M_, D_, H_, W_ - value_l_ = value_list[lid_].flatten(2).transpose(1, 2).reshape(N_ * M_, D_, H_, W_) - # N_, Lq_, M_, P_, 2 -> N_, M_, Lq_, P_, 2 -> N_*M_, Lq_, P_, 2 - sampling_grid_l_ = sampling_grids[:, :, :, lid_].transpose(1, 2).flatten(0, 1) - # N_*M_, D_, Lq_, P_ - sampling_value_l_ = F.grid_sample( - value_l_, sampling_grid_l_, mode="bilinear", padding_mode="zeros", align_corners=False - ) - sampling_value_list.append(sampling_value_l_) - # (N_, Lq_, M_, L_, P_) -> (N_, M_, Lq_, L_, P_) -> (N_, M_, 1, Lq_, L_*P_) - attention_weights = attention_weights.transpose(1, 2).reshape(N_ * M_, 1, Lq_, L_ * P_) - output = (torch.stack(sampling_value_list, dim=-2).flatten(-2) * attention_weights).sum(-1).view(N_, M_ * D_, Lq_) - return output.transpose(1, 2).contiguous() diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/models/utils/ops/modules/__init__.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/models/utils/ops/modules/__init__.py deleted file mode 100644 index 7449bf1949737d220c5917d1df6d924842251c6d..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/models/utils/ops/modules/__init__.py +++ /dev/null @@ -1,14 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -# ------------------------------------------------------------------------------------------------ -# Deformable DETR -# Copyright (c) 2020 SenseTime. All Rights Reserved. -# Licensed under the Apache License, Version 2.0 [see LICENSE for details] -# ------------------------------------------------------------------------------------------------ -# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 -# ------------------------------------------------------------------------------------------------ - -from .ms_deform_attn import MSDeformAttn diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/models/utils/ops/modules/ms_deform_attn.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/models/utils/ops/modules/ms_deform_attn.py deleted file mode 100644 index e4b7d70c21f89146b461758065994d5ae414594d..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/models/utils/ops/modules/ms_deform_attn.py +++ /dev/null @@ -1,145 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -# ------------------------------------------------------------------------------------------------ -# Deformable DETR -# Copyright (c) 2020 SenseTime. All Rights Reserved. -# Licensed under the Apache License, Version 2.0 [see LICENSE for details] -# ------------------------------------------------------------------------------------------------ -# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 -# ------------------------------------------------------------------------------------------------ - -from __future__ import absolute_import -from __future__ import print_function -from __future__ import division - -import warnings -import math - -import torch -from torch import nn -import torch.nn.functional as F -from torch.nn.init import xavier_uniform_, constant_ - -from ..functions import MSDeformAttnFunction - - -def _is_power_of_2(n): - if (not isinstance(n, int)) or (n < 0): - raise ValueError("invalid input for _is_power_of_2: {} (type: {})".format(n, type(n))) - return (n & (n - 1) == 0) and n != 0 - - -class MSDeformAttn(nn.Module): - def __init__(self, d_model=256, n_levels=4, n_heads=8, n_points=4): - """ - Multi-Scale Deformable Attention Module - :param d_model hidden dimension - :param n_levels number of feature levels - :param n_heads number of attention heads - :param n_points number of sampling points per attention head per feature level - """ - super().__init__() - if d_model % n_heads != 0: - raise ValueError("d_model must be divisible by n_heads, but got {} and {}".format(d_model, n_heads)) - _d_per_head = d_model // n_heads - # you'd better set _d_per_head to a power of 2 which is more efficient in our CUDA implementation - if not _is_power_of_2(_d_per_head): - warnings.warn( - "You'd better set d_model in MSDeformAttn to make the dimension of each attention head a power of 2 " - "which is more efficient in our CUDA implementation." - ) - - self.im2col_step = 64 - - self.d_model = d_model - self.n_levels = n_levels - self.n_heads = n_heads - self.n_points = n_points - - self.sampling_offsets = nn.Linear(d_model, n_heads * n_levels * n_points * 2) - self.attention_weights = nn.Linear(d_model, n_heads * n_levels * n_points) - self.value_proj = nn.Linear(d_model, d_model) - self.output_proj = nn.Linear(d_model, d_model) - - self._reset_parameters() - - def _reset_parameters(self): - constant_(self.sampling_offsets.weight.data, 0.0) - thetas = torch.arange(self.n_heads, dtype=torch.float32) * (2.0 * math.pi / self.n_heads) - grid_init = torch.stack([thetas.cos(), thetas.sin()], -1) - grid_init = ( - (grid_init / grid_init.abs().max(-1, keepdim=True)[0]) - .view(self.n_heads, 1, 1, 2) - .repeat(1, self.n_levels, self.n_points, 1) - ) - for i in range(self.n_points): - grid_init[:, :, i, :] *= i + 1 - with torch.no_grad(): - self.sampling_offsets.bias = nn.Parameter(grid_init.view(-1)) - constant_(self.attention_weights.weight.data, 0.0) - constant_(self.attention_weights.bias.data, 0.0) - xavier_uniform_(self.value_proj.weight.data) - constant_(self.value_proj.bias.data, 0.0) - xavier_uniform_(self.output_proj.weight.data) - constant_(self.output_proj.bias.data, 0.0) - - def forward( - self, - query, - reference_points, - input_flatten, - input_spatial_shapes, - input_level_start_index, - input_padding_mask=None, - ): - """ - :param query (N, Length_{query}, C) - :param reference_points (N, Length_{query}, n_levels, 2), range in [0, 1], top-left (0,0), bottom-right (1, 1), including padding area - or (N, Length_{query}, n_levels, 4), add additional (w, h) to form reference boxes - :param input_flatten (N, \sum_{l=0}^{L-1} H_l \cdot W_l, C) - :param input_spatial_shapes (n_levels, 2), [(H_0, W_0), (H_1, W_1), ..., (H_{L-1}, W_{L-1})] - :param input_level_start_index (n_levels, ), [0, H_0*W_0, H_0*W_0+H_1*W_1, H_0*W_0+H_1*W_1+H_2*W_2, ..., H_0*W_0+H_1*W_1+...+H_{L-1}*W_{L-1}] - :param input_padding_mask (N, \sum_{l=0}^{L-1} H_l \cdot W_l), True for padding elements, False for non-padding elements - - :return output (N, Length_{query}, C) - """ - N, Len_q, _ = query.shape - N, Len_in, _ = input_flatten.shape - assert (input_spatial_shapes[:, 0] * input_spatial_shapes[:, 1]).sum() == Len_in - - value = self.value_proj(input_flatten) - if input_padding_mask is not None: - value = value.masked_fill(input_padding_mask[..., None], float(0)) - value = value.view(N, Len_in, self.n_heads, self.d_model // self.n_heads) - sampling_offsets = self.sampling_offsets(query).view(N, Len_q, self.n_heads, self.n_levels, self.n_points, 2) - attention_weights = self.attention_weights(query).view(N, Len_q, self.n_heads, self.n_levels * self.n_points) - attention_weights = F.softmax(attention_weights, -1).view(N, Len_q, self.n_heads, self.n_levels, self.n_points) - # N, Len_q, n_heads, n_levels, n_points, 2 - if reference_points.shape[-1] == 2: - offset_normalizer = torch.stack([input_spatial_shapes[..., 1], input_spatial_shapes[..., 0]], -1) - sampling_locations = ( - reference_points[:, :, None, :, None, :] - + sampling_offsets / offset_normalizer[None, None, None, :, None, :] - ) - elif reference_points.shape[-1] == 4: - sampling_locations = ( - reference_points[:, :, None, :, None, :2] - + sampling_offsets / self.n_points * reference_points[:, :, None, :, None, 2:] * 0.5 - ) - else: - raise ValueError( - "Last dim of reference_points must be 2 or 4, but get {} instead.".format(reference_points.shape[-1]) - ) - output = MSDeformAttnFunction.apply( - value, - input_spatial_shapes, - input_level_start_index, - sampling_locations, - attention_weights, - self.im2col_step, - ) - output = self.output_proj(output) - return output diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/models/utils/ops/setup.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/models/utils/ops/setup.py deleted file mode 100644 index e7dc7d64e6966bea6e6800988c7bb2f3f231e2e5..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/models/utils/ops/setup.py +++ /dev/null @@ -1,83 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -# ------------------------------------------------------------------------------------------------ -# Deformable DETR -# Copyright (c) 2020 SenseTime. All Rights Reserved. -# Licensed under the Apache License, Version 2.0 [see LICENSE for details] -# ------------------------------------------------------------------------------------------------ -# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 -# ------------------------------------------------------------------------------------------------ - -import os -import glob - -import torch - -from torch.utils.cpp_extension import CUDA_HOME -from torch.utils.cpp_extension import CppExtension -from torch.utils.cpp_extension import CUDAExtension - -from setuptools import find_packages -from setuptools import setup - -requirements = ["torch", "torchvision"] - - -def get_extensions(): - this_dir = os.path.dirname(os.path.abspath(__file__)) - extensions_dir = os.path.join(this_dir, "src") - - main_file = glob.glob(os.path.join(extensions_dir, "*.cpp")) - source_cpu = glob.glob(os.path.join(extensions_dir, "cpu", "*.cpp")) - source_cuda = glob.glob(os.path.join(extensions_dir, "cuda", "*.cu")) - - sources = main_file + source_cpu - extension = CppExtension - extra_compile_args = {"cxx": []} - define_macros = [] - - if torch.cuda.is_available() and CUDA_HOME is not None: - extension = CUDAExtension - sources += source_cuda - define_macros += [("WITH_CUDA", None)] - extra_compile_args["nvcc"] = [ - "-DCUDA_HAS_FP16=1", - "-D__CUDA_NO_HALF_OPERATORS__", - "-D__CUDA_NO_HALF_CONVERSIONS__", - "-D__CUDA_NO_HALF2_OPERATORS__", - ] - else: - raise NotImplementedError("Cuda is not availabel") - - sources = [os.path.join(extensions_dir, s) for s in sources] - include_dirs = [extensions_dir] - ext_modules = [ - extension( - "MultiScaleDeformableAttention", - sources, - include_dirs=include_dirs, - define_macros=define_macros, - extra_compile_args=extra_compile_args, - ) - ] - return ext_modules - - -setup( - name="MultiScaleDeformableAttention", - version="1.0", - author="Weijie Su", - url="https://github.com/fundamentalvision/Deformable-DETR", - description="PyTorch Wrapper for CUDA Functions of Multi-Scale Deformable Attention", - packages=find_packages( - exclude=( - "configs", - "tests", - ) - ), - ext_modules=get_extensions(), - cmdclass={"build_ext": torch.utils.cpp_extension.BuildExtension}, -) diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/models/utils/ops/src/cpu/ms_deform_attn_cpu.cpp b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/models/utils/ops/src/cpu/ms_deform_attn_cpu.cpp deleted file mode 100644 index d24ce5c2792d56575ddb4e58d9e572eb77d6a028..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/models/utils/ops/src/cpu/ms_deform_attn_cpu.cpp +++ /dev/null @@ -1,46 +0,0 @@ -// Copyright (c) Meta Platforms, Inc. and affiliates. -// -// This software may be used and distributed in accordance with -// the terms of the DINOv3 License Agreement. - -/*! -************************************************************************************************** -* Deformable DETR -* Copyright (c) 2020 SenseTime. All Rights Reserved. -* Licensed under the Apache License, Version 2.0 [see LICENSE for details] -************************************************************************************************** -* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 -************************************************************************************************** -*/ - -#include - -#include -#include - - -at::Tensor -ms_deform_attn_cpu_forward( - const at::Tensor &value, - const at::Tensor &spatial_shapes, - const at::Tensor &level_start_index, - const at::Tensor &sampling_loc, - const at::Tensor &attn_weight, - const int im2col_step) -{ - AT_ERROR("Not implement on cpu"); -} - -std::vector -ms_deform_attn_cpu_backward( - const at::Tensor &value, - const at::Tensor &spatial_shapes, - const at::Tensor &level_start_index, - const at::Tensor &sampling_loc, - const at::Tensor &attn_weight, - const at::Tensor &grad_output, - const int im2col_step) -{ - AT_ERROR("Not implement on cpu"); -} - diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/models/utils/ops/src/cpu/ms_deform_attn_cpu.h b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/models/utils/ops/src/cpu/ms_deform_attn_cpu.h deleted file mode 100644 index cf952dc46782435448e3120dbfc485544e82a620..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/models/utils/ops/src/cpu/ms_deform_attn_cpu.h +++ /dev/null @@ -1,38 +0,0 @@ -// Copyright (c) Meta Platforms, Inc. and affiliates. -// -// This software may be used and distributed in accordance with -// the terms of the DINOv3 License Agreement. - -/*! -************************************************************************************************** -* Deformable DETR -* Copyright (c) 2020 SenseTime. All Rights Reserved. -* Licensed under the Apache License, Version 2.0 [see LICENSE for details] -************************************************************************************************** -* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 -************************************************************************************************** -*/ - -#pragma once -#include - -at::Tensor -ms_deform_attn_cpu_forward( - const at::Tensor &value, - const at::Tensor &spatial_shapes, - const at::Tensor &level_start_index, - const at::Tensor &sampling_loc, - const at::Tensor &attn_weight, - const int im2col_step); - -std::vector -ms_deform_attn_cpu_backward( - const at::Tensor &value, - const at::Tensor &spatial_shapes, - const at::Tensor &level_start_index, - const at::Tensor &sampling_loc, - const at::Tensor &attn_weight, - const at::Tensor &grad_output, - const int im2col_step); - - diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/models/utils/ops/src/cuda/ms_deform_attn_cuda.cu b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/models/utils/ops/src/cuda/ms_deform_attn_cuda.cu deleted file mode 100644 index fe112e464c0ba1c8f10a6c1c393bb23a8c846387..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/models/utils/ops/src/cuda/ms_deform_attn_cuda.cu +++ /dev/null @@ -1,158 +0,0 @@ -// Copyright (c) Meta Platforms, Inc. and affiliates. -// -// This software may be used and distributed in accordance with -// the terms of the DINOv3 License Agreement. - -/*! -************************************************************************************************** -* Deformable DETR -* Copyright (c) 2020 SenseTime. All Rights Reserved. -* Licensed under the Apache License, Version 2.0 [see LICENSE for details] -************************************************************************************************** -* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 -************************************************************************************************** -*/ - -#include -#include "cuda/ms_deform_im2col_cuda.cuh" - -#include -#include -#include -#include - - -at::Tensor ms_deform_attn_cuda_forward( - const at::Tensor &value, - const at::Tensor &spatial_shapes, - const at::Tensor &level_start_index, - const at::Tensor &sampling_loc, - const at::Tensor &attn_weight, - const int im2col_step) -{ - AT_ASSERTM(value.is_contiguous(), "value tensor has to be contiguous"); - AT_ASSERTM(spatial_shapes.is_contiguous(), "spatial_shapes tensor has to be contiguous"); - AT_ASSERTM(level_start_index.is_contiguous(), "level_start_index tensor has to be contiguous"); - AT_ASSERTM(sampling_loc.is_contiguous(), "sampling_loc tensor has to be contiguous"); - AT_ASSERTM(attn_weight.is_contiguous(), "attn_weight tensor has to be contiguous"); - - AT_ASSERTM(value.type().is_cuda(), "value must be a CUDA tensor"); - AT_ASSERTM(spatial_shapes.type().is_cuda(), "spatial_shapes must be a CUDA tensor"); - AT_ASSERTM(level_start_index.type().is_cuda(), "level_start_index must be a CUDA tensor"); - AT_ASSERTM(sampling_loc.type().is_cuda(), "sampling_loc must be a CUDA tensor"); - AT_ASSERTM(attn_weight.type().is_cuda(), "attn_weight must be a CUDA tensor"); - - const int batch = value.size(0); - const int spatial_size = value.size(1); - const int num_heads = value.size(2); - const int channels = value.size(3); - - const int num_levels = spatial_shapes.size(0); - - const int num_query = sampling_loc.size(1); - const int num_point = sampling_loc.size(4); - - const int im2col_step_ = std::min(batch, im2col_step); - - AT_ASSERTM(batch % im2col_step_ == 0, "batch(%d) must divide im2col_step(%d)", batch, im2col_step_); - - auto output = at::zeros({batch, num_query, num_heads, channels}, value.options()); - - const int batch_n = im2col_step_; - auto output_n = output.view({batch/im2col_step_, batch_n, num_query, num_heads, channels}); - auto per_value_size = spatial_size * num_heads * channels; - auto per_sample_loc_size = num_query * num_heads * num_levels * num_point * 2; - auto per_attn_weight_size = num_query * num_heads * num_levels * num_point; - for (int n = 0; n < batch/im2col_step_; ++n) - { - auto columns = output_n.select(0, n); - AT_DISPATCH_FLOATING_TYPES(value.scalar_type(), "ms_deform_attn_forward_cuda", ([&] { - ms_deformable_im2col_cuda(at::cuda::getCurrentCUDAStream(), - value.data() + n * im2col_step_ * per_value_size, - spatial_shapes.data(), - level_start_index.data(), - sampling_loc.data() + n * im2col_step_ * per_sample_loc_size, - attn_weight.data() + n * im2col_step_ * per_attn_weight_size, - batch_n, spatial_size, num_heads, channels, num_levels, num_query, num_point, - columns.data()); - - })); - } - - output = output.view({batch, num_query, num_heads*channels}); - - return output; -} - - -std::vector ms_deform_attn_cuda_backward( - const at::Tensor &value, - const at::Tensor &spatial_shapes, - const at::Tensor &level_start_index, - const at::Tensor &sampling_loc, - const at::Tensor &attn_weight, - const at::Tensor &grad_output, - const int im2col_step) -{ - - AT_ASSERTM(value.is_contiguous(), "value tensor has to be contiguous"); - AT_ASSERTM(spatial_shapes.is_contiguous(), "spatial_shapes tensor has to be contiguous"); - AT_ASSERTM(level_start_index.is_contiguous(), "level_start_index tensor has to be contiguous"); - AT_ASSERTM(sampling_loc.is_contiguous(), "sampling_loc tensor has to be contiguous"); - AT_ASSERTM(attn_weight.is_contiguous(), "attn_weight tensor has to be contiguous"); - AT_ASSERTM(grad_output.is_contiguous(), "grad_output tensor has to be contiguous"); - - AT_ASSERTM(value.type().is_cuda(), "value must be a CUDA tensor"); - AT_ASSERTM(spatial_shapes.type().is_cuda(), "spatial_shapes must be a CUDA tensor"); - AT_ASSERTM(level_start_index.type().is_cuda(), "level_start_index must be a CUDA tensor"); - AT_ASSERTM(sampling_loc.type().is_cuda(), "sampling_loc must be a CUDA tensor"); - AT_ASSERTM(attn_weight.type().is_cuda(), "attn_weight must be a CUDA tensor"); - AT_ASSERTM(grad_output.type().is_cuda(), "grad_output must be a CUDA tensor"); - - const int batch = value.size(0); - const int spatial_size = value.size(1); - const int num_heads = value.size(2); - const int channels = value.size(3); - - const int num_levels = spatial_shapes.size(0); - - const int num_query = sampling_loc.size(1); - const int num_point = sampling_loc.size(4); - - const int im2col_step_ = std::min(batch, im2col_step); - - AT_ASSERTM(batch % im2col_step_ == 0, "batch(%d) must divide im2col_step(%d)", batch, im2col_step_); - - auto grad_value = at::zeros_like(value); - auto grad_sampling_loc = at::zeros_like(sampling_loc); - auto grad_attn_weight = at::zeros_like(attn_weight); - - const int batch_n = im2col_step_; - auto per_value_size = spatial_size * num_heads * channels; - auto per_sample_loc_size = num_query * num_heads * num_levels * num_point * 2; - auto per_attn_weight_size = num_query * num_heads * num_levels * num_point; - auto grad_output_n = grad_output.view({batch/im2col_step_, batch_n, num_query, num_heads, channels}); - - for (int n = 0; n < batch/im2col_step_; ++n) - { - auto grad_output_g = grad_output_n.select(0, n); - AT_DISPATCH_FLOATING_TYPES(value.scalar_type(), "ms_deform_attn_backward_cuda", ([&] { - ms_deformable_col2im_cuda(at::cuda::getCurrentCUDAStream(), - grad_output_g.data(), - value.data() + n * im2col_step_ * per_value_size, - spatial_shapes.data(), - level_start_index.data(), - sampling_loc.data() + n * im2col_step_ * per_sample_loc_size, - attn_weight.data() + n * im2col_step_ * per_attn_weight_size, - batch_n, spatial_size, num_heads, channels, num_levels, num_query, num_point, - grad_value.data() + n * im2col_step_ * per_value_size, - grad_sampling_loc.data() + n * im2col_step_ * per_sample_loc_size, - grad_attn_weight.data() + n * im2col_step_ * per_attn_weight_size); - - })); - } - - return { - grad_value, grad_sampling_loc, grad_attn_weight - }; -} diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/models/utils/ops/src/cuda/ms_deform_attn_cuda.h b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/models/utils/ops/src/cuda/ms_deform_attn_cuda.h deleted file mode 100644 index 080c6a2b17733da8a8b04a4a64c3aa1e81904b26..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/models/utils/ops/src/cuda/ms_deform_attn_cuda.h +++ /dev/null @@ -1,35 +0,0 @@ -// Copyright (c) Meta Platforms, Inc. and affiliates. -// -// This software may be used and distributed in accordance with -// the terms of the DINOv3 License Agreement. - -/*! -************************************************************************************************** -* Deformable DETR -* Copyright (c) 2020 SenseTime. All Rights Reserved. -* Licensed under the Apache License, Version 2.0 [see LICENSE for details] -************************************************************************************************** -* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 -************************************************************************************************** -*/ - -#pragma once -#include - -at::Tensor ms_deform_attn_cuda_forward( - const at::Tensor &value, - const at::Tensor &spatial_shapes, - const at::Tensor &level_start_index, - const at::Tensor &sampling_loc, - const at::Tensor &attn_weight, - const int im2col_step); - -std::vector ms_deform_attn_cuda_backward( - const at::Tensor &value, - const at::Tensor &spatial_shapes, - const at::Tensor &level_start_index, - const at::Tensor &sampling_loc, - const at::Tensor &attn_weight, - const at::Tensor &grad_output, - const int im2col_step); - diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/models/utils/ops/src/cuda/ms_deform_im2col_cuda.cuh b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/models/utils/ops/src/cuda/ms_deform_im2col_cuda.cuh deleted file mode 100644 index 736841fcfbeaf2d6e494fff7e556b6d8b8b73499..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/models/utils/ops/src/cuda/ms_deform_im2col_cuda.cuh +++ /dev/null @@ -1,1332 +0,0 @@ -// Copyright (c) Meta Platforms, Inc. and affiliates. -// -// This software may be used and distributed in accordance with -// the terms of the DINOv3 License Agreement. - -/*! -************************************************************************** -* Deformable DETR -* Copyright (c) 2020 SenseTime. All Rights Reserved. -* Licensed under the Apache License, Version 2.0 [see LICENSE for details] -************************************************************************** -* Modified from DCN (https://github.com/msracver/Deformable-ConvNets) -* Copyright (c) 2018 Microsoft -************************************************************************** -*/ - -#include -#include -#include - -#include -#include - -#include - -#define CUDA_KERNEL_LOOP(i, n) \ - for (int i = blockIdx.x * blockDim.x + threadIdx.x; \ - i < (n); \ - i += blockDim.x * gridDim.x) - -const int CUDA_NUM_THREADS = 1024; -inline int GET_BLOCKS(const int N, const int num_threads) -{ - return (N + num_threads - 1) / num_threads; -} - - -template -__device__ scalar_t ms_deform_attn_im2col_bilinear(const scalar_t* &bottom_data, - const int &height, const int &width, const int &nheads, const int &channels, - const scalar_t &h, const scalar_t &w, const int &m, const int &c) -{ - const int h_low = floor(h); - const int w_low = floor(w); - const int h_high = h_low + 1; - const int w_high = w_low + 1; - - const scalar_t lh = h - h_low; - const scalar_t lw = w - w_low; - const scalar_t hh = 1 - lh, hw = 1 - lw; - - const int w_stride = nheads * channels; - const int h_stride = width * w_stride; - const int h_low_ptr_offset = h_low * h_stride; - const int h_high_ptr_offset = h_low_ptr_offset + h_stride; - const int w_low_ptr_offset = w_low * w_stride; - const int w_high_ptr_offset = w_low_ptr_offset + w_stride; - const int base_ptr = m * channels + c; - - scalar_t v1 = 0; - if (h_low >= 0 && w_low >= 0) - { - const int ptr1 = h_low_ptr_offset + w_low_ptr_offset + base_ptr; - v1 = bottom_data[ptr1]; - } - scalar_t v2 = 0; - if (h_low >= 0 && w_high <= width - 1) - { - const int ptr2 = h_low_ptr_offset + w_high_ptr_offset + base_ptr; - v2 = bottom_data[ptr2]; - } - scalar_t v3 = 0; - if (h_high <= height - 1 && w_low >= 0) - { - const int ptr3 = h_high_ptr_offset + w_low_ptr_offset + base_ptr; - v3 = bottom_data[ptr3]; - } - scalar_t v4 = 0; - if (h_high <= height - 1 && w_high <= width - 1) - { - const int ptr4 = h_high_ptr_offset + w_high_ptr_offset + base_ptr; - v4 = bottom_data[ptr4]; - } - - const scalar_t w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw; - - const scalar_t val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4); - return val; -} - - -template -__device__ void ms_deform_attn_col2im_bilinear(const scalar_t* &bottom_data, - const int &height, const int &width, const int &nheads, const int &channels, - const scalar_t &h, const scalar_t &w, const int &m, const int &c, - const scalar_t &top_grad, - const scalar_t &attn_weight, - scalar_t* &grad_value, - scalar_t* grad_sampling_loc, - scalar_t* grad_attn_weight) -{ - const int h_low = floor(h); - const int w_low = floor(w); - const int h_high = h_low + 1; - const int w_high = w_low + 1; - - const scalar_t lh = h - h_low; - const scalar_t lw = w - w_low; - const scalar_t hh = 1 - lh, hw = 1 - lw; - - const int w_stride = nheads * channels; - const int h_stride = width * w_stride; - const int h_low_ptr_offset = h_low * h_stride; - const int h_high_ptr_offset = h_low_ptr_offset + h_stride; - const int w_low_ptr_offset = w_low * w_stride; - const int w_high_ptr_offset = w_low_ptr_offset + w_stride; - const int base_ptr = m * channels + c; - - const scalar_t w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw; - const scalar_t top_grad_value = top_grad * attn_weight; - scalar_t grad_h_weight = 0, grad_w_weight = 0; - - scalar_t v1 = 0; - if (h_low >= 0 && w_low >= 0) - { - const int ptr1 = h_low_ptr_offset + w_low_ptr_offset + base_ptr; - v1 = bottom_data[ptr1]; - grad_h_weight -= hw * v1; - grad_w_weight -= hh * v1; - atomicAdd(grad_value+ptr1, w1*top_grad_value); - } - scalar_t v2 = 0; - if (h_low >= 0 && w_high <= width - 1) - { - const int ptr2 = h_low_ptr_offset + w_high_ptr_offset + base_ptr; - v2 = bottom_data[ptr2]; - grad_h_weight -= lw * v2; - grad_w_weight += hh * v2; - atomicAdd(grad_value+ptr2, w2*top_grad_value); - } - scalar_t v3 = 0; - if (h_high <= height - 1 && w_low >= 0) - { - const int ptr3 = h_high_ptr_offset + w_low_ptr_offset + base_ptr; - v3 = bottom_data[ptr3]; - grad_h_weight += hw * v3; - grad_w_weight -= lh * v3; - atomicAdd(grad_value+ptr3, w3*top_grad_value); - } - scalar_t v4 = 0; - if (h_high <= height - 1 && w_high <= width - 1) - { - const int ptr4 = h_high_ptr_offset + w_high_ptr_offset + base_ptr; - v4 = bottom_data[ptr4]; - grad_h_weight += lw * v4; - grad_w_weight += lh * v4; - atomicAdd(grad_value+ptr4, w4*top_grad_value); - } - - const scalar_t val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4); - *grad_attn_weight = top_grad * val; - *grad_sampling_loc = width * grad_w_weight * top_grad_value; - *(grad_sampling_loc + 1) = height * grad_h_weight * top_grad_value; -} - - -template -__device__ void ms_deform_attn_col2im_bilinear_gm(const scalar_t* &bottom_data, - const int &height, const int &width, const int &nheads, const int &channels, - const scalar_t &h, const scalar_t &w, const int &m, const int &c, - const scalar_t &top_grad, - const scalar_t &attn_weight, - scalar_t* &grad_value, - scalar_t* grad_sampling_loc, - scalar_t* grad_attn_weight) -{ - const int h_low = floor(h); - const int w_low = floor(w); - const int h_high = h_low + 1; - const int w_high = w_low + 1; - - const scalar_t lh = h - h_low; - const scalar_t lw = w - w_low; - const scalar_t hh = 1 - lh, hw = 1 - lw; - - const int w_stride = nheads * channels; - const int h_stride = width * w_stride; - const int h_low_ptr_offset = h_low * h_stride; - const int h_high_ptr_offset = h_low_ptr_offset + h_stride; - const int w_low_ptr_offset = w_low * w_stride; - const int w_high_ptr_offset = w_low_ptr_offset + w_stride; - const int base_ptr = m * channels + c; - - const scalar_t w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw; - const scalar_t top_grad_value = top_grad * attn_weight; - scalar_t grad_h_weight = 0, grad_w_weight = 0; - - scalar_t v1 = 0; - if (h_low >= 0 && w_low >= 0) - { - const int ptr1 = h_low_ptr_offset + w_low_ptr_offset + base_ptr; - v1 = bottom_data[ptr1]; - grad_h_weight -= hw * v1; - grad_w_weight -= hh * v1; - atomicAdd(grad_value+ptr1, w1*top_grad_value); - } - scalar_t v2 = 0; - if (h_low >= 0 && w_high <= width - 1) - { - const int ptr2 = h_low_ptr_offset + w_high_ptr_offset + base_ptr; - v2 = bottom_data[ptr2]; - grad_h_weight -= lw * v2; - grad_w_weight += hh * v2; - atomicAdd(grad_value+ptr2, w2*top_grad_value); - } - scalar_t v3 = 0; - if (h_high <= height - 1 && w_low >= 0) - { - const int ptr3 = h_high_ptr_offset + w_low_ptr_offset + base_ptr; - v3 = bottom_data[ptr3]; - grad_h_weight += hw * v3; - grad_w_weight -= lh * v3; - atomicAdd(grad_value+ptr3, w3*top_grad_value); - } - scalar_t v4 = 0; - if (h_high <= height - 1 && w_high <= width - 1) - { - const int ptr4 = h_high_ptr_offset + w_high_ptr_offset + base_ptr; - v4 = bottom_data[ptr4]; - grad_h_weight += lw * v4; - grad_w_weight += lh * v4; - atomicAdd(grad_value+ptr4, w4*top_grad_value); - } - - const scalar_t val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4); - atomicAdd(grad_attn_weight, top_grad * val); - atomicAdd(grad_sampling_loc, width * grad_w_weight * top_grad_value); - atomicAdd(grad_sampling_loc + 1, height * grad_h_weight * top_grad_value); -} - - -template -__global__ void ms_deformable_im2col_gpu_kernel(const int n, - const scalar_t *data_value, - const int64_t *data_spatial_shapes, - const int64_t *data_level_start_index, - const scalar_t *data_sampling_loc, - const scalar_t *data_attn_weight, - const int batch_size, - const int spatial_size, - const int num_heads, - const int channels, - const int num_levels, - const int num_query, - const int num_point, - scalar_t *data_col) -{ - CUDA_KERNEL_LOOP(index, n) - { - int _temp = index; - const int c_col = _temp % channels; - _temp /= channels; - const int sampling_index = _temp; - const int m_col = _temp % num_heads; - _temp /= num_heads; - const int q_col = _temp % num_query; - _temp /= num_query; - const int b_col = _temp; - - scalar_t *data_col_ptr = data_col + index; - int data_weight_ptr = sampling_index * num_levels * num_point; - int data_loc_w_ptr = data_weight_ptr << 1; - const int qid_stride = num_heads * channels; - const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride; - scalar_t col = 0; - - for (int l_col=0; l_col < num_levels; ++l_col) - { - const int level_start_id = data_level_start_index[l_col]; - const int spatial_h_ptr = l_col << 1; - const int spatial_h = data_spatial_shapes[spatial_h_ptr]; - const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1]; - const scalar_t *data_value_ptr = data_value + (data_value_ptr_init_offset + level_start_id * qid_stride); - for (int p_col=0; p_col < num_point; ++p_col) - { - const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr]; - const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1]; - const scalar_t weight = data_attn_weight[data_weight_ptr]; - - const scalar_t h_im = loc_h * spatial_h - 0.5; - const scalar_t w_im = loc_w * spatial_w - 0.5; - - if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w) - { - col += ms_deform_attn_im2col_bilinear(data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col) * weight; - } - - data_weight_ptr += 1; - data_loc_w_ptr += 2; - } - } - *data_col_ptr = col; - } -} - -template -__global__ void ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1(const int n, - const scalar_t *grad_col, - const scalar_t *data_value, - const int64_t *data_spatial_shapes, - const int64_t *data_level_start_index, - const scalar_t *data_sampling_loc, - const scalar_t *data_attn_weight, - const int batch_size, - const int spatial_size, - const int num_heads, - const int channels, - const int num_levels, - const int num_query, - const int num_point, - scalar_t *grad_value, - scalar_t *grad_sampling_loc, - scalar_t *grad_attn_weight) -{ - CUDA_KERNEL_LOOP(index, n) - { - __shared__ scalar_t cache_grad_sampling_loc[blockSize * 2]; - __shared__ scalar_t cache_grad_attn_weight[blockSize]; - unsigned int tid = threadIdx.x; - int _temp = index; - const int c_col = _temp % channels; - _temp /= channels; - const int sampling_index = _temp; - const int m_col = _temp % num_heads; - _temp /= num_heads; - const int q_col = _temp % num_query; - _temp /= num_query; - const int b_col = _temp; - - const scalar_t top_grad = grad_col[index]; - - int data_weight_ptr = sampling_index * num_levels * num_point; - int data_loc_w_ptr = data_weight_ptr << 1; - const int grad_sampling_ptr = data_weight_ptr; - grad_sampling_loc += grad_sampling_ptr << 1; - grad_attn_weight += grad_sampling_ptr; - const int grad_weight_stride = 1; - const int grad_loc_stride = 2; - const int qid_stride = num_heads * channels; - const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride; - - for (int l_col=0; l_col < num_levels; ++l_col) - { - const int level_start_id = data_level_start_index[l_col]; - const int spatial_h_ptr = l_col << 1; - const int spatial_h = data_spatial_shapes[spatial_h_ptr]; - const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1]; - const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride; - const scalar_t *data_value_ptr = data_value + value_ptr_offset; - scalar_t *grad_value_ptr = grad_value + value_ptr_offset; - - for (int p_col=0; p_col < num_point; ++p_col) - { - const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr]; - const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1]; - const scalar_t weight = data_attn_weight[data_weight_ptr]; - - const scalar_t h_im = loc_h * spatial_h - 0.5; - const scalar_t w_im = loc_w * spatial_w - 0.5; - *(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0; - *(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0; - *(cache_grad_attn_weight+threadIdx.x)=0; - if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w) - { - ms_deform_attn_col2im_bilinear( - data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col, - top_grad, weight, grad_value_ptr, - cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x); - } - - __syncthreads(); - if (tid == 0) - { - scalar_t _grad_w=cache_grad_sampling_loc[0], _grad_h=cache_grad_sampling_loc[1], _grad_a=cache_grad_attn_weight[0]; - int sid=2; - for (unsigned int tid = 1; tid < blockSize; ++tid) - { - _grad_w += cache_grad_sampling_loc[sid]; - _grad_h += cache_grad_sampling_loc[sid + 1]; - _grad_a += cache_grad_attn_weight[tid]; - sid += 2; - } - - - *grad_sampling_loc = _grad_w; - *(grad_sampling_loc + 1) = _grad_h; - *grad_attn_weight = _grad_a; - } - __syncthreads(); - - data_weight_ptr += 1; - data_loc_w_ptr += 2; - grad_attn_weight += grad_weight_stride; - grad_sampling_loc += grad_loc_stride; - } - } - } -} - - -template -__global__ void ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2(const int n, - const scalar_t *grad_col, - const scalar_t *data_value, - const int64_t *data_spatial_shapes, - const int64_t *data_level_start_index, - const scalar_t *data_sampling_loc, - const scalar_t *data_attn_weight, - const int batch_size, - const int spatial_size, - const int num_heads, - const int channels, - const int num_levels, - const int num_query, - const int num_point, - scalar_t *grad_value, - scalar_t *grad_sampling_loc, - scalar_t *grad_attn_weight) -{ - CUDA_KERNEL_LOOP(index, n) - { - __shared__ scalar_t cache_grad_sampling_loc[blockSize * 2]; - __shared__ scalar_t cache_grad_attn_weight[blockSize]; - unsigned int tid = threadIdx.x; - int _temp = index; - const int c_col = _temp % channels; - _temp /= channels; - const int sampling_index = _temp; - const int m_col = _temp % num_heads; - _temp /= num_heads; - const int q_col = _temp % num_query; - _temp /= num_query; - const int b_col = _temp; - - const scalar_t top_grad = grad_col[index]; - - int data_weight_ptr = sampling_index * num_levels * num_point; - int data_loc_w_ptr = data_weight_ptr << 1; - const int grad_sampling_ptr = data_weight_ptr; - grad_sampling_loc += grad_sampling_ptr << 1; - grad_attn_weight += grad_sampling_ptr; - const int grad_weight_stride = 1; - const int grad_loc_stride = 2; - const int qid_stride = num_heads * channels; - const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride; - - for (int l_col=0; l_col < num_levels; ++l_col) - { - const int level_start_id = data_level_start_index[l_col]; - const int spatial_h_ptr = l_col << 1; - const int spatial_h = data_spatial_shapes[spatial_h_ptr]; - const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1]; - const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride; - const scalar_t *data_value_ptr = data_value + value_ptr_offset; - scalar_t *grad_value_ptr = grad_value + value_ptr_offset; - - for (int p_col=0; p_col < num_point; ++p_col) - { - const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr]; - const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1]; - const scalar_t weight = data_attn_weight[data_weight_ptr]; - - const scalar_t h_im = loc_h * spatial_h - 0.5; - const scalar_t w_im = loc_w * spatial_w - 0.5; - *(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0; - *(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0; - *(cache_grad_attn_weight+threadIdx.x)=0; - if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w) - { - ms_deform_attn_col2im_bilinear( - data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col, - top_grad, weight, grad_value_ptr, - cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x); - } - - __syncthreads(); - - for (unsigned int s=blockSize/2; s>0; s>>=1) - { - if (tid < s) { - const unsigned int xid1 = tid << 1; - const unsigned int xid2 = (tid + s) << 1; - cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + s]; - cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2]; - cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1]; - } - __syncthreads(); - } - - if (tid == 0) - { - *grad_sampling_loc = cache_grad_sampling_loc[0]; - *(grad_sampling_loc + 1) = cache_grad_sampling_loc[1]; - *grad_attn_weight = cache_grad_attn_weight[0]; - } - __syncthreads(); - - data_weight_ptr += 1; - data_loc_w_ptr += 2; - grad_attn_weight += grad_weight_stride; - grad_sampling_loc += grad_loc_stride; - } - } - } -} - - -template -__global__ void ms_deformable_col2im_gpu_kernel_shm_reduce_v1(const int n, - const scalar_t *grad_col, - const scalar_t *data_value, - const int64_t *data_spatial_shapes, - const int64_t *data_level_start_index, - const scalar_t *data_sampling_loc, - const scalar_t *data_attn_weight, - const int batch_size, - const int spatial_size, - const int num_heads, - const int channels, - const int num_levels, - const int num_query, - const int num_point, - scalar_t *grad_value, - scalar_t *grad_sampling_loc, - scalar_t *grad_attn_weight) -{ - CUDA_KERNEL_LOOP(index, n) - { - extern __shared__ int _s[]; - scalar_t* cache_grad_sampling_loc = (scalar_t*)_s; - scalar_t* cache_grad_attn_weight = cache_grad_sampling_loc + 2 * blockDim.x; - unsigned int tid = threadIdx.x; - int _temp = index; - const int c_col = _temp % channels; - _temp /= channels; - const int sampling_index = _temp; - const int m_col = _temp % num_heads; - _temp /= num_heads; - const int q_col = _temp % num_query; - _temp /= num_query; - const int b_col = _temp; - - const scalar_t top_grad = grad_col[index]; - - int data_weight_ptr = sampling_index * num_levels * num_point; - int data_loc_w_ptr = data_weight_ptr << 1; - const int grad_sampling_ptr = data_weight_ptr; - grad_sampling_loc += grad_sampling_ptr << 1; - grad_attn_weight += grad_sampling_ptr; - const int grad_weight_stride = 1; - const int grad_loc_stride = 2; - const int qid_stride = num_heads * channels; - const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride; - - for (int l_col=0; l_col < num_levels; ++l_col) - { - const int level_start_id = data_level_start_index[l_col]; - const int spatial_h_ptr = l_col << 1; - const int spatial_h = data_spatial_shapes[spatial_h_ptr]; - const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1]; - const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride; - const scalar_t *data_value_ptr = data_value + value_ptr_offset; - scalar_t *grad_value_ptr = grad_value + value_ptr_offset; - - for (int p_col=0; p_col < num_point; ++p_col) - { - const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr]; - const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1]; - const scalar_t weight = data_attn_weight[data_weight_ptr]; - - const scalar_t h_im = loc_h * spatial_h - 0.5; - const scalar_t w_im = loc_w * spatial_w - 0.5; - *(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0; - *(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0; - *(cache_grad_attn_weight+threadIdx.x)=0; - if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w) - { - ms_deform_attn_col2im_bilinear( - data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col, - top_grad, weight, grad_value_ptr, - cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x); - } - - __syncthreads(); - if (tid == 0) - { - scalar_t _grad_w=cache_grad_sampling_loc[0], _grad_h=cache_grad_sampling_loc[1], _grad_a=cache_grad_attn_weight[0]; - int sid=2; - for (unsigned int tid = 1; tid < blockDim.x; ++tid) - { - _grad_w += cache_grad_sampling_loc[sid]; - _grad_h += cache_grad_sampling_loc[sid + 1]; - _grad_a += cache_grad_attn_weight[tid]; - sid += 2; - } - - - *grad_sampling_loc = _grad_w; - *(grad_sampling_loc + 1) = _grad_h; - *grad_attn_weight = _grad_a; - } - __syncthreads(); - - data_weight_ptr += 1; - data_loc_w_ptr += 2; - grad_attn_weight += grad_weight_stride; - grad_sampling_loc += grad_loc_stride; - } - } - } -} - -template -__global__ void ms_deformable_col2im_gpu_kernel_shm_reduce_v2(const int n, - const scalar_t *grad_col, - const scalar_t *data_value, - const int64_t *data_spatial_shapes, - const int64_t *data_level_start_index, - const scalar_t *data_sampling_loc, - const scalar_t *data_attn_weight, - const int batch_size, - const int spatial_size, - const int num_heads, - const int channels, - const int num_levels, - const int num_query, - const int num_point, - scalar_t *grad_value, - scalar_t *grad_sampling_loc, - scalar_t *grad_attn_weight) -{ - CUDA_KERNEL_LOOP(index, n) - { - extern __shared__ int _s[]; - scalar_t* cache_grad_sampling_loc = (scalar_t*)_s; - scalar_t* cache_grad_attn_weight = cache_grad_sampling_loc + 2 * blockDim.x; - unsigned int tid = threadIdx.x; - int _temp = index; - const int c_col = _temp % channels; - _temp /= channels; - const int sampling_index = _temp; - const int m_col = _temp % num_heads; - _temp /= num_heads; - const int q_col = _temp % num_query; - _temp /= num_query; - const int b_col = _temp; - - const scalar_t top_grad = grad_col[index]; - - int data_weight_ptr = sampling_index * num_levels * num_point; - int data_loc_w_ptr = data_weight_ptr << 1; - const int grad_sampling_ptr = data_weight_ptr; - grad_sampling_loc += grad_sampling_ptr << 1; - grad_attn_weight += grad_sampling_ptr; - const int grad_weight_stride = 1; - const int grad_loc_stride = 2; - const int qid_stride = num_heads * channels; - const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride; - - for (int l_col=0; l_col < num_levels; ++l_col) - { - const int level_start_id = data_level_start_index[l_col]; - const int spatial_h_ptr = l_col << 1; - const int spatial_h = data_spatial_shapes[spatial_h_ptr]; - const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1]; - const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride; - const scalar_t *data_value_ptr = data_value + value_ptr_offset; - scalar_t *grad_value_ptr = grad_value + value_ptr_offset; - - for (int p_col=0; p_col < num_point; ++p_col) - { - const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr]; - const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1]; - const scalar_t weight = data_attn_weight[data_weight_ptr]; - - const scalar_t h_im = loc_h * spatial_h - 0.5; - const scalar_t w_im = loc_w * spatial_w - 0.5; - *(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0; - *(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0; - *(cache_grad_attn_weight+threadIdx.x)=0; - if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w) - { - ms_deform_attn_col2im_bilinear( - data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col, - top_grad, weight, grad_value_ptr, - cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x); - } - - __syncthreads(); - - for (unsigned int s=blockDim.x/2, spre=blockDim.x; s>0; s>>=1, spre>>=1) - { - if (tid < s) { - const unsigned int xid1 = tid << 1; - const unsigned int xid2 = (tid + s) << 1; - cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + s]; - cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2]; - cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1]; - if (tid + (s << 1) < spre) - { - cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + (s << 1)]; - cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2 + (s << 1)]; - cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1 + (s << 1)]; - } - } - __syncthreads(); - } - - if (tid == 0) - { - *grad_sampling_loc = cache_grad_sampling_loc[0]; - *(grad_sampling_loc + 1) = cache_grad_sampling_loc[1]; - *grad_attn_weight = cache_grad_attn_weight[0]; - } - __syncthreads(); - - data_weight_ptr += 1; - data_loc_w_ptr += 2; - grad_attn_weight += grad_weight_stride; - grad_sampling_loc += grad_loc_stride; - } - } - } -} - -template -__global__ void ms_deformable_col2im_gpu_kernel_shm_reduce_v2_multi_blocks(const int n, - const scalar_t *grad_col, - const scalar_t *data_value, - const int64_t *data_spatial_shapes, - const int64_t *data_level_start_index, - const scalar_t *data_sampling_loc, - const scalar_t *data_attn_weight, - const int batch_size, - const int spatial_size, - const int num_heads, - const int channels, - const int num_levels, - const int num_query, - const int num_point, - scalar_t *grad_value, - scalar_t *grad_sampling_loc, - scalar_t *grad_attn_weight) -{ - CUDA_KERNEL_LOOP(index, n) - { - extern __shared__ int _s[]; - scalar_t* cache_grad_sampling_loc = (scalar_t*)_s; - scalar_t* cache_grad_attn_weight = cache_grad_sampling_loc + 2 * blockDim.x; - unsigned int tid = threadIdx.x; - int _temp = index; - const int c_col = _temp % channels; - _temp /= channels; - const int sampling_index = _temp; - const int m_col = _temp % num_heads; - _temp /= num_heads; - const int q_col = _temp % num_query; - _temp /= num_query; - const int b_col = _temp; - - const scalar_t top_grad = grad_col[index]; - - int data_weight_ptr = sampling_index * num_levels * num_point; - int data_loc_w_ptr = data_weight_ptr << 1; - const int grad_sampling_ptr = data_weight_ptr; - grad_sampling_loc += grad_sampling_ptr << 1; - grad_attn_weight += grad_sampling_ptr; - const int grad_weight_stride = 1; - const int grad_loc_stride = 2; - const int qid_stride = num_heads * channels; - const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride; - - for (int l_col=0; l_col < num_levels; ++l_col) - { - const int level_start_id = data_level_start_index[l_col]; - const int spatial_h_ptr = l_col << 1; - const int spatial_h = data_spatial_shapes[spatial_h_ptr]; - const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1]; - const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride; - const scalar_t *data_value_ptr = data_value + value_ptr_offset; - scalar_t *grad_value_ptr = grad_value + value_ptr_offset; - - for (int p_col=0; p_col < num_point; ++p_col) - { - const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr]; - const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1]; - const scalar_t weight = data_attn_weight[data_weight_ptr]; - - const scalar_t h_im = loc_h * spatial_h - 0.5; - const scalar_t w_im = loc_w * spatial_w - 0.5; - *(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0; - *(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0; - *(cache_grad_attn_weight+threadIdx.x)=0; - if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w) - { - ms_deform_attn_col2im_bilinear( - data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col, - top_grad, weight, grad_value_ptr, - cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x); - } - - __syncthreads(); - - for (unsigned int s=blockDim.x/2, spre=blockDim.x; s>0; s>>=1, spre>>=1) - { - if (tid < s) { - const unsigned int xid1 = tid << 1; - const unsigned int xid2 = (tid + s) << 1; - cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + s]; - cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2]; - cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1]; - if (tid + (s << 1) < spre) - { - cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + (s << 1)]; - cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2 + (s << 1)]; - cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1 + (s << 1)]; - } - } - __syncthreads(); - } - - if (tid == 0) - { - atomicAdd(grad_sampling_loc, cache_grad_sampling_loc[0]); - atomicAdd(grad_sampling_loc + 1, cache_grad_sampling_loc[1]); - atomicAdd(grad_attn_weight, cache_grad_attn_weight[0]); - } - __syncthreads(); - - data_weight_ptr += 1; - data_loc_w_ptr += 2; - grad_attn_weight += grad_weight_stride; - grad_sampling_loc += grad_loc_stride; - } - } - } -} - - -template -__global__ void ms_deformable_col2im_gpu_kernel_gm(const int n, - const scalar_t *grad_col, - const scalar_t *data_value, - const int64_t *data_spatial_shapes, - const int64_t *data_level_start_index, - const scalar_t *data_sampling_loc, - const scalar_t *data_attn_weight, - const int batch_size, - const int spatial_size, - const int num_heads, - const int channels, - const int num_levels, - const int num_query, - const int num_point, - scalar_t *grad_value, - scalar_t *grad_sampling_loc, - scalar_t *grad_attn_weight) -{ - CUDA_KERNEL_LOOP(index, n) - { - int _temp = index; - const int c_col = _temp % channels; - _temp /= channels; - const int sampling_index = _temp; - const int m_col = _temp % num_heads; - _temp /= num_heads; - const int q_col = _temp % num_query; - _temp /= num_query; - const int b_col = _temp; - - const scalar_t top_grad = grad_col[index]; - - int data_weight_ptr = sampling_index * num_levels * num_point; - int data_loc_w_ptr = data_weight_ptr << 1; - const int grad_sampling_ptr = data_weight_ptr; - grad_sampling_loc += grad_sampling_ptr << 1; - grad_attn_weight += grad_sampling_ptr; - const int grad_weight_stride = 1; - const int grad_loc_stride = 2; - const int qid_stride = num_heads * channels; - const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride; - - for (int l_col=0; l_col < num_levels; ++l_col) - { - const int level_start_id = data_level_start_index[l_col]; - const int spatial_h_ptr = l_col << 1; - const int spatial_h = data_spatial_shapes[spatial_h_ptr]; - const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1]; - const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride; - const scalar_t *data_value_ptr = data_value + value_ptr_offset; - scalar_t *grad_value_ptr = grad_value + value_ptr_offset; - - for (int p_col=0; p_col < num_point; ++p_col) - { - const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr]; - const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1]; - const scalar_t weight = data_attn_weight[data_weight_ptr]; - - const scalar_t h_im = loc_h * spatial_h - 0.5; - const scalar_t w_im = loc_w * spatial_w - 0.5; - if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w) - { - ms_deform_attn_col2im_bilinear_gm( - data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col, - top_grad, weight, grad_value_ptr, - grad_sampling_loc, grad_attn_weight); - } - data_weight_ptr += 1; - data_loc_w_ptr += 2; - grad_attn_weight += grad_weight_stride; - grad_sampling_loc += grad_loc_stride; - } - } - } -} - - -template -void ms_deformable_im2col_cuda(cudaStream_t stream, - const scalar_t* data_value, - const int64_t* data_spatial_shapes, - const int64_t* data_level_start_index, - const scalar_t* data_sampling_loc, - const scalar_t* data_attn_weight, - const int batch_size, - const int spatial_size, - const int num_heads, - const int channels, - const int num_levels, - const int num_query, - const int num_point, - scalar_t* data_col) -{ - const int num_kernels = batch_size * num_query * num_heads * channels; - const int num_actual_kernels = batch_size * num_query * num_heads * channels; - const int num_threads = CUDA_NUM_THREADS; - ms_deformable_im2col_gpu_kernel - <<>>( - num_kernels, data_value, data_spatial_shapes, data_level_start_index, data_sampling_loc, data_attn_weight, - batch_size, spatial_size, num_heads, channels, num_levels, num_query, num_point, data_col); - - cudaError_t err = cudaGetLastError(); - if (err != cudaSuccess) - { - printf("error in ms_deformable_im2col_cuda: %s\n", cudaGetErrorString(err)); - } - -} - -template -void ms_deformable_col2im_cuda(cudaStream_t stream, - const scalar_t* grad_col, - const scalar_t* data_value, - const int64_t * data_spatial_shapes, - const int64_t * data_level_start_index, - const scalar_t * data_sampling_loc, - const scalar_t * data_attn_weight, - const int batch_size, - const int spatial_size, - const int num_heads, - const int channels, - const int num_levels, - const int num_query, - const int num_point, - scalar_t* grad_value, - scalar_t* grad_sampling_loc, - scalar_t* grad_attn_weight) -{ - const int num_threads = (channels > CUDA_NUM_THREADS)?CUDA_NUM_THREADS:channels; - const int num_kernels = batch_size * num_query * num_heads * channels; - const int num_actual_kernels = batch_size * num_query * num_heads * channels; - if (channels > 1024) - { - if ((channels & 1023) == 0) - { - ms_deformable_col2im_gpu_kernel_shm_reduce_v2_multi_blocks - <<>>( - num_kernels, - grad_col, - data_value, - data_spatial_shapes, - data_level_start_index, - data_sampling_loc, - data_attn_weight, - batch_size, - spatial_size, - num_heads, - channels, - num_levels, - num_query, - num_point, - grad_value, - grad_sampling_loc, - grad_attn_weight); - } - else - { - ms_deformable_col2im_gpu_kernel_gm - <<>>( - num_kernels, - grad_col, - data_value, - data_spatial_shapes, - data_level_start_index, - data_sampling_loc, - data_attn_weight, - batch_size, - spatial_size, - num_heads, - channels, - num_levels, - num_query, - num_point, - grad_value, - grad_sampling_loc, - grad_attn_weight); - } - } - else{ - switch(channels) - { - case 1: - ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1 - <<>>( - num_kernels, - grad_col, - data_value, - data_spatial_shapes, - data_level_start_index, - data_sampling_loc, - data_attn_weight, - batch_size, - spatial_size, - num_heads, - channels, - num_levels, - num_query, - num_point, - grad_value, - grad_sampling_loc, - grad_attn_weight); - break; - case 2: - ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1 - <<>>( - num_kernels, - grad_col, - data_value, - data_spatial_shapes, - data_level_start_index, - data_sampling_loc, - data_attn_weight, - batch_size, - spatial_size, - num_heads, - channels, - num_levels, - num_query, - num_point, - grad_value, - grad_sampling_loc, - grad_attn_weight); - break; - case 4: - ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1 - <<>>( - num_kernels, - grad_col, - data_value, - data_spatial_shapes, - data_level_start_index, - data_sampling_loc, - data_attn_weight, - batch_size, - spatial_size, - num_heads, - channels, - num_levels, - num_query, - num_point, - grad_value, - grad_sampling_loc, - grad_attn_weight); - break; - case 8: - ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1 - <<>>( - num_kernels, - grad_col, - data_value, - data_spatial_shapes, - data_level_start_index, - data_sampling_loc, - data_attn_weight, - batch_size, - spatial_size, - num_heads, - channels, - num_levels, - num_query, - num_point, - grad_value, - grad_sampling_loc, - grad_attn_weight); - break; - case 16: - ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1 - <<>>( - num_kernels, - grad_col, - data_value, - data_spatial_shapes, - data_level_start_index, - data_sampling_loc, - data_attn_weight, - batch_size, - spatial_size, - num_heads, - channels, - num_levels, - num_query, - num_point, - grad_value, - grad_sampling_loc, - grad_attn_weight); - break; - case 32: - ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1 - <<>>( - num_kernels, - grad_col, - data_value, - data_spatial_shapes, - data_level_start_index, - data_sampling_loc, - data_attn_weight, - batch_size, - spatial_size, - num_heads, - channels, - num_levels, - num_query, - num_point, - grad_value, - grad_sampling_loc, - grad_attn_weight); - break; - case 64: - ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2 - <<>>( - num_kernels, - grad_col, - data_value, - data_spatial_shapes, - data_level_start_index, - data_sampling_loc, - data_attn_weight, - batch_size, - spatial_size, - num_heads, - channels, - num_levels, - num_query, - num_point, - grad_value, - grad_sampling_loc, - grad_attn_weight); - break; - case 128: - ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2 - <<>>( - num_kernels, - grad_col, - data_value, - data_spatial_shapes, - data_level_start_index, - data_sampling_loc, - data_attn_weight, - batch_size, - spatial_size, - num_heads, - channels, - num_levels, - num_query, - num_point, - grad_value, - grad_sampling_loc, - grad_attn_weight); - break; - case 256: - ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2 - <<>>( - num_kernels, - grad_col, - data_value, - data_spatial_shapes, - data_level_start_index, - data_sampling_loc, - data_attn_weight, - batch_size, - spatial_size, - num_heads, - channels, - num_levels, - num_query, - num_point, - grad_value, - grad_sampling_loc, - grad_attn_weight); - break; - case 512: - ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2 - <<>>( - num_kernels, - grad_col, - data_value, - data_spatial_shapes, - data_level_start_index, - data_sampling_loc, - data_attn_weight, - batch_size, - spatial_size, - num_heads, - channels, - num_levels, - num_query, - num_point, - grad_value, - grad_sampling_loc, - grad_attn_weight); - break; - case 1024: - ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2 - <<>>( - num_kernels, - grad_col, - data_value, - data_spatial_shapes, - data_level_start_index, - data_sampling_loc, - data_attn_weight, - batch_size, - spatial_size, - num_heads, - channels, - num_levels, - num_query, - num_point, - grad_value, - grad_sampling_loc, - grad_attn_weight); - break; - default: - if (channels < 64) - { - ms_deformable_col2im_gpu_kernel_shm_reduce_v1 - <<>>( - num_kernels, - grad_col, - data_value, - data_spatial_shapes, - data_level_start_index, - data_sampling_loc, - data_attn_weight, - batch_size, - spatial_size, - num_heads, - channels, - num_levels, - num_query, - num_point, - grad_value, - grad_sampling_loc, - grad_attn_weight); - } - else - { - ms_deformable_col2im_gpu_kernel_shm_reduce_v2 - <<>>( - num_kernels, - grad_col, - data_value, - data_spatial_shapes, - data_level_start_index, - data_sampling_loc, - data_attn_weight, - batch_size, - spatial_size, - num_heads, - channels, - num_levels, - num_query, - num_point, - grad_value, - grad_sampling_loc, - grad_attn_weight); - } - } - } - cudaError_t err = cudaGetLastError(); - if (err != cudaSuccess) - { - printf("error in ms_deformable_col2im_cuda: %s\n", cudaGetErrorString(err)); - } - -} diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/models/utils/ops/src/ms_deform_attn.h b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/models/utils/ops/src/ms_deform_attn.h deleted file mode 100644 index 5fd1f667dba53df0c4797cc7101c17a4a2662a3f..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/models/utils/ops/src/ms_deform_attn.h +++ /dev/null @@ -1,67 +0,0 @@ -// Copyright (c) Meta Platforms, Inc. and affiliates. -// -// This software may be used and distributed in accordance with -// the terms of the DINOv3 License Agreement. - -/*! -************************************************************************************************** -* Deformable DETR -* Copyright (c) 2020 SenseTime. All Rights Reserved. -* Licensed under the Apache License, Version 2.0 [see LICENSE for details] -************************************************************************************************** -* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 -************************************************************************************************** -*/ - -#pragma once - -#include "cpu/ms_deform_attn_cpu.h" - -#ifdef WITH_CUDA -#include "cuda/ms_deform_attn_cuda.h" -#endif - - -at::Tensor -ms_deform_attn_forward( - const at::Tensor &value, - const at::Tensor &spatial_shapes, - const at::Tensor &level_start_index, - const at::Tensor &sampling_loc, - const at::Tensor &attn_weight, - const int im2col_step) -{ - if (value.type().is_cuda()) - { -#ifdef WITH_CUDA - return ms_deform_attn_cuda_forward( - value, spatial_shapes, level_start_index, sampling_loc, attn_weight, im2col_step); -#else - AT_ERROR("Not compiled with GPU support"); -#endif - } - AT_ERROR("Not implemented on the CPU"); -} - -std::vector -ms_deform_attn_backward( - const at::Tensor &value, - const at::Tensor &spatial_shapes, - const at::Tensor &level_start_index, - const at::Tensor &sampling_loc, - const at::Tensor &attn_weight, - const at::Tensor &grad_output, - const int im2col_step) -{ - if (value.type().is_cuda()) - { -#ifdef WITH_CUDA - return ms_deform_attn_cuda_backward( - value, spatial_shapes, level_start_index, sampling_loc, attn_weight, grad_output, im2col_step); -#else - AT_ERROR("Not compiled with GPU support"); -#endif - } - AT_ERROR("Not implemented on the CPU"); -} - diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/models/utils/ops/src/vision.cpp b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/models/utils/ops/src/vision.cpp deleted file mode 100644 index e1af72c89dfc2e4c03732d4bda80b05184a44a39..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/models/utils/ops/src/vision.cpp +++ /dev/null @@ -1,21 +0,0 @@ -// Copyright (c) Meta Platforms, Inc. and affiliates. -// -// This software may be used and distributed in accordance with -// the terms of the DINOv3 License Agreement. - -/*! -************************************************************************************************** -* Deformable DETR -* Copyright (c) 2020 SenseTime. All Rights Reserved. -* Licensed under the Apache License, Version 2.0 [see LICENSE for details] -************************************************************************************************** -* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 -************************************************************************************************** -*/ - -#include "ms_deform_attn.h" - -PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { - m.def("ms_deform_attn_forward", &ms_deform_attn_forward, "ms_deform_attn_forward"); - m.def("ms_deform_attn_backward", &ms_deform_attn_backward, "ms_deform_attn_backward"); -} diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/models/utils/ops/test.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/models/utils/ops/test.py deleted file mode 100644 index e18f4742b3d7651fd12474fcfafa7bf2c89b49e8..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/models/utils/ops/test.py +++ /dev/null @@ -1,121 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -# ------------------------------------------------------------------------------------------------ -# Deformable DETR -# Copyright (c) 2020 SenseTime. All Rights Reserved. -# Licensed under the Apache License, Version 2.0 [see LICENSE for details] -# ------------------------------------------------------------------------------------------------ -# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 -# ------------------------------------------------------------------------------------------------ - -from __future__ import absolute_import -from __future__ import print_function -from __future__ import division - -import torch -from torch.autograd import gradcheck - -from functions.ms_deform_attn_func import MSDeformAttnFunction, ms_deform_attn_core_pytorch - - -N, M, D = 1, 2, 2 -Lq, L, P = 2, 2, 2 -shapes = torch.as_tensor([(6, 4), (3, 2)], dtype=torch.long).cuda() -level_start_index = torch.cat((shapes.new_zeros((1,)), shapes.prod(1).cumsum(0)[:-1])) -S = sum([(H * W).item() for H, W in shapes]) - - -torch.manual_seed(3) - - -@torch.no_grad() -def check_forward_equal_with_pytorch_double(): - value = torch.rand(N, S, M, D).cuda() * 0.01 - sampling_locations = torch.rand(N, Lq, M, L, P, 2).cuda() - attention_weights = torch.rand(N, Lq, M, L, P).cuda() + 1e-5 - attention_weights /= attention_weights.sum(-1, keepdim=True).sum(-2, keepdim=True) - im2col_step = 2 - output_pytorch = ( - ms_deform_attn_core_pytorch(value.double(), shapes, sampling_locations.double(), attention_weights.double()) - .detach() - .cpu() - ) - output_cuda = ( - MSDeformAttnFunction.apply( - value.double(), - shapes, - level_start_index, - sampling_locations.double(), - attention_weights.double(), - im2col_step, - ) - .detach() - .cpu() - ) - fwdok = torch.allclose(output_cuda, output_pytorch) - max_abs_err = (output_cuda - output_pytorch).abs().max() - max_rel_err = ((output_cuda - output_pytorch).abs() / output_pytorch.abs()).max() - - print( - f"* {fwdok} check_forward_equal_with_pytorch_double: max_abs_err {max_abs_err:.2e} max_rel_err {max_rel_err:.2e}" - ) - - -@torch.no_grad() -def check_forward_equal_with_pytorch_float(): - value = torch.rand(N, S, M, D).cuda() * 0.01 - sampling_locations = torch.rand(N, Lq, M, L, P, 2).cuda() - attention_weights = torch.rand(N, Lq, M, L, P).cuda() + 1e-5 - attention_weights /= attention_weights.sum(-1, keepdim=True).sum(-2, keepdim=True) - im2col_step = 2 - output_pytorch = ms_deform_attn_core_pytorch(value, shapes, sampling_locations, attention_weights).detach().cpu() - output_cuda = ( - MSDeformAttnFunction.apply(value, shapes, level_start_index, sampling_locations, attention_weights, im2col_step) - .detach() - .cpu() - ) - fwdok = torch.allclose(output_cuda, output_pytorch, rtol=1e-2, atol=1e-3) - max_abs_err = (output_cuda - output_pytorch).abs().max() - max_rel_err = ((output_cuda - output_pytorch).abs() / output_pytorch.abs()).max() - - print( - f"* {fwdok} check_forward_equal_with_pytorch_float: max_abs_err {max_abs_err:.2e} max_rel_err {max_rel_err:.2e}" - ) - - -def check_gradient_numerical(channels=4, grad_value=True, grad_sampling_loc=True, grad_attn_weight=True): - value = torch.rand(N, S, M, channels).cuda() * 0.01 - sampling_locations = torch.rand(N, Lq, M, L, P, 2).cuda() - attention_weights = torch.rand(N, Lq, M, L, P).cuda() + 1e-5 - attention_weights /= attention_weights.sum(-1, keepdim=True).sum(-2, keepdim=True) - im2col_step = 2 - func = MSDeformAttnFunction.apply - - value.requires_grad = grad_value - sampling_locations.requires_grad = grad_sampling_loc - attention_weights.requires_grad = grad_attn_weight - - gradok = gradcheck( - func, - ( - value.double(), - shapes, - level_start_index, - sampling_locations.double(), - attention_weights.double(), - im2col_step, - ), - ) - - print(f"* {gradok} check_gradient_numerical(D={channels})") - - -if __name__ == "__main__": - check_forward_equal_with_pytorch_double() - check_forward_equal_with_pytorch_float() - - for channels in [30, 32, 64, 71, 1025, 2048, 3096]: - check_gradient_numerical(channels, True, True, True) diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/models/utils/position_encoding.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/models/utils/position_encoding.py deleted file mode 100644 index a8f6abc79bcb285f9e4ad8436dc36a92dc7ee8b9..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/models/utils/position_encoding.py +++ /dev/null @@ -1,66 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -# Copyright (c) Facebook, Inc. and its affiliates. -# # Modified by Bowen Cheng from: https://github.com/facebookresearch/detr/blob/master/models/position_encoding.py -""" -Various positional encodings for the transformer. -""" - -import math - -import torch -from torch import nn - - -class PositionEmbeddingSine(nn.Module): - """ - This is a more standard version of the position embedding, very similar to the one - used by the Attention is all you need paper, generalized to work on images. - """ - - def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None): - super().__init__() - self.num_pos_feats = num_pos_feats - self.temperature = temperature - self.normalize = normalize - if scale is not None and normalize is False: - raise ValueError("normalize should be True if scale is passed") - if scale is None: - scale = 2 * math.pi - self.scale = scale - - def forward(self, x, mask=None): - if mask is None: - mask = torch.zeros((x.size(0), x.size(2), x.size(3)), device=x.device, dtype=torch.bool) - not_mask = ~mask - y_embed = not_mask.cumsum(1, dtype=torch.float32) - x_embed = not_mask.cumsum(2, dtype=torch.float32) - if self.normalize: - eps = 1e-6 - y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale - x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale - - dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device) - dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats) - - pos_x = x_embed[:, :, :, None] / dim_t - pos_y = y_embed[:, :, :, None] / dim_t - pos_x = torch.stack((pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4).flatten(3) - pos_y = torch.stack((pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4).flatten(3) - pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2) - return pos - - def __repr__(self, _repr_indent=4): - head = "Positional encoding " + self.__class__.__name__ - body = [ - "num_pos_feats: {}".format(self.num_pos_feats), - "temperature: {}".format(self.temperature), - "normalize: {}".format(self.normalize), - "scale: {}".format(self.scale), - ] - # _repr_indent = 4 - lines = [head] + [" " * _repr_indent + line for line in body] - return "\n".join(lines) diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/models/utils/transformer.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/models/utils/transformer.py deleted file mode 100644 index 8a6ff187d144ee593c8c8b72d07bccbe12fa8685..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/models/utils/transformer.py +++ /dev/null @@ -1,359 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -# Copyright (c) Facebook, Inc. and its affiliates. -# Modified by Bowen Cheng from: https://github.com/facebookresearch/detr/blob/master/models/transformer.py -""" -Transformer class. - -Copy-paste from torch.nn.Transformer with modifications: - * positional encodings are passed in MHattention - * extra LN at the end of encoder is removed - * decoder returns a stack of activations from all decoding layers -""" - -import copy -from typing import Optional - -import torch -import torch.nn.functional as F -from torch import Tensor, nn - - -class Transformer(nn.Module): - def __init__( - self, - d_model=512, - nhead=8, - num_encoder_layers=6, - num_decoder_layers=6, - dim_feedforward=2048, - dropout=0.1, - activation="relu", - normalize_before=False, - return_intermediate_dec=False, - ): - super().__init__() - - encoder_layer = TransformerEncoderLayer(d_model, nhead, dim_feedforward, dropout, activation, normalize_before) - encoder_norm = nn.LayerNorm(d_model) if normalize_before else None - self.encoder = TransformerEncoder(encoder_layer, num_encoder_layers, encoder_norm) - - decoder_layer = TransformerDecoderLayer(d_model, nhead, dim_feedforward, dropout, activation, normalize_before) - decoder_norm = nn.LayerNorm(d_model) - self.decoder = TransformerDecoder( - decoder_layer, - num_decoder_layers, - decoder_norm, - return_intermediate=return_intermediate_dec, - ) - - self._reset_parameters() - - self.d_model = d_model - self.nhead = nhead - - def _reset_parameters(self): - for p in self.parameters(): - if p.dim() > 1: - nn.init.xavier_uniform_(p) - - def forward(self, src, mask, query_embed, pos_embed): - # flatten NxCxHxW to HWxNxC - bs, c, h, w = src.shape - src = src.flatten(2).permute(2, 0, 1) - pos_embed = pos_embed.flatten(2).permute(2, 0, 1) - query_embed = query_embed.unsqueeze(1).repeat(1, bs, 1) - if mask is not None: - mask = mask.flatten(1) - - tgt = torch.zeros_like(query_embed) - memory = self.encoder(src, src_key_padding_mask=mask, pos=pos_embed) - hs = self.decoder(tgt, memory, memory_key_padding_mask=mask, pos=pos_embed, query_pos=query_embed) - return hs.transpose(1, 2), memory.permute(1, 2, 0).view(bs, c, h, w) - - -class TransformerEncoder(nn.Module): - def __init__(self, encoder_layer, num_layers, norm=None): - super().__init__() - self.layers = _get_clones(encoder_layer, num_layers) - self.num_layers = num_layers - self.norm = norm - - def forward( - self, - src, - mask: Optional[Tensor] = None, - src_key_padding_mask: Optional[Tensor] = None, - pos: Optional[Tensor] = None, - ): - output = src - - for layer in self.layers: - output = layer(output, src_mask=mask, src_key_padding_mask=src_key_padding_mask, pos=pos) - - if self.norm is not None: - output = self.norm(output) - - return output - - -class TransformerDecoder(nn.Module): - def __init__(self, decoder_layer, num_layers, norm=None, return_intermediate=False): - super().__init__() - self.layers = _get_clones(decoder_layer, num_layers) - self.num_layers = num_layers - self.norm = norm - self.return_intermediate = return_intermediate - - def forward( - self, - tgt, - memory, - tgt_mask: Optional[Tensor] = None, - memory_mask: Optional[Tensor] = None, - tgt_key_padding_mask: Optional[Tensor] = None, - memory_key_padding_mask: Optional[Tensor] = None, - pos: Optional[Tensor] = None, - query_pos: Optional[Tensor] = None, - ): - output = tgt - - intermediate = [] - - for layer in self.layers: - output = layer( - output, - memory, - tgt_mask=tgt_mask, - memory_mask=memory_mask, - tgt_key_padding_mask=tgt_key_padding_mask, - memory_key_padding_mask=memory_key_padding_mask, - pos=pos, - query_pos=query_pos, - ) - if self.return_intermediate: - intermediate.append(self.norm(output)) - - if self.norm is not None: - output = self.norm(output) - if self.return_intermediate: - intermediate.pop() - intermediate.append(output) - - if self.return_intermediate: - return torch.stack(intermediate) - - return output.unsqueeze(0) - - -class TransformerEncoderLayer(nn.Module): - def __init__( - self, - d_model, - nhead, - dim_feedforward=2048, - dropout=0.1, - activation="relu", - normalize_before=False, - ): - super().__init__() - self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) - # Implementation of Feedforward model - self.linear1 = nn.Linear(d_model, dim_feedforward) - self.dropout = nn.Dropout(dropout) - self.linear2 = nn.Linear(dim_feedforward, d_model) - - self.norm1 = nn.LayerNorm(d_model) - self.norm2 = nn.LayerNorm(d_model) - self.dropout1 = nn.Dropout(dropout) - self.dropout2 = nn.Dropout(dropout) - - self.activation = _get_activation_fn(activation) - self.normalize_before = normalize_before - - def with_pos_embed(self, tensor, pos: Optional[Tensor]): - return tensor if pos is None else tensor + pos - - def forward_post( - self, - src, - src_mask: Optional[Tensor] = None, - src_key_padding_mask: Optional[Tensor] = None, - pos: Optional[Tensor] = None, - ): - q = k = self.with_pos_embed(src, pos) - src2 = self.self_attn(q, k, value=src, attn_mask=src_mask, key_padding_mask=src_key_padding_mask)[0] - src = src + self.dropout1(src2) - src = self.norm1(src) - src2 = self.linear2(self.dropout(self.activation(self.linear1(src)))) - src = src + self.dropout2(src2) - src = self.norm2(src) - return src - - def forward_pre( - self, - src, - src_mask: Optional[Tensor] = None, - src_key_padding_mask: Optional[Tensor] = None, - pos: Optional[Tensor] = None, - ): - src2 = self.norm1(src) - q = k = self.with_pos_embed(src2, pos) - src2 = self.self_attn(q, k, value=src2, attn_mask=src_mask, key_padding_mask=src_key_padding_mask)[0] - src = src + self.dropout1(src2) - src2 = self.norm2(src) - src2 = self.linear2(self.dropout(self.activation(self.linear1(src2)))) - src = src + self.dropout2(src2) - return src - - def forward( - self, - src, - src_mask: Optional[Tensor] = None, - src_key_padding_mask: Optional[Tensor] = None, - pos: Optional[Tensor] = None, - ): - if self.normalize_before: - return self.forward_pre(src, src_mask, src_key_padding_mask, pos) - return self.forward_post(src, src_mask, src_key_padding_mask, pos) - - -class TransformerDecoderLayer(nn.Module): - def __init__( - self, - d_model, - nhead, - dim_feedforward=2048, - dropout=0.1, - activation="relu", - normalize_before=False, - ): - super().__init__() - self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) - self.multihead_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) - # Implementation of Feedforward model - self.linear1 = nn.Linear(d_model, dim_feedforward) - self.dropout = nn.Dropout(dropout) - self.linear2 = nn.Linear(dim_feedforward, d_model) - - self.norm1 = nn.LayerNorm(d_model) - self.norm2 = nn.LayerNorm(d_model) - self.norm3 = nn.LayerNorm(d_model) - self.dropout1 = nn.Dropout(dropout) - self.dropout2 = nn.Dropout(dropout) - self.dropout3 = nn.Dropout(dropout) - - self.activation = _get_activation_fn(activation) - self.normalize_before = normalize_before - - def with_pos_embed(self, tensor, pos: Optional[Tensor]): - return tensor if pos is None else tensor + pos - - def forward_post( - self, - tgt, - memory, - tgt_mask: Optional[Tensor] = None, - memory_mask: Optional[Tensor] = None, - tgt_key_padding_mask: Optional[Tensor] = None, - memory_key_padding_mask: Optional[Tensor] = None, - pos: Optional[Tensor] = None, - query_pos: Optional[Tensor] = None, - ): - q = k = self.with_pos_embed(tgt, query_pos) - tgt2 = self.self_attn(q, k, value=tgt, attn_mask=tgt_mask, key_padding_mask=tgt_key_padding_mask)[0] - tgt = tgt + self.dropout1(tgt2) - tgt = self.norm1(tgt) - tgt2 = self.multihead_attn( - query=self.with_pos_embed(tgt, query_pos), - key=self.with_pos_embed(memory, pos), - value=memory, - attn_mask=memory_mask, - key_padding_mask=memory_key_padding_mask, - )[0] - tgt = tgt + self.dropout2(tgt2) - tgt = self.norm2(tgt) - tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt)))) - tgt = tgt + self.dropout3(tgt2) - tgt = self.norm3(tgt) - return tgt - - def forward_pre( - self, - tgt, - memory, - tgt_mask: Optional[Tensor] = None, - memory_mask: Optional[Tensor] = None, - tgt_key_padding_mask: Optional[Tensor] = None, - memory_key_padding_mask: Optional[Tensor] = None, - pos: Optional[Tensor] = None, - query_pos: Optional[Tensor] = None, - ): - tgt2 = self.norm1(tgt) - q = k = self.with_pos_embed(tgt2, query_pos) - tgt2 = self.self_attn(q, k, value=tgt2, attn_mask=tgt_mask, key_padding_mask=tgt_key_padding_mask)[0] - tgt = tgt + self.dropout1(tgt2) - tgt2 = self.norm2(tgt) - tgt2 = self.multihead_attn( - query=self.with_pos_embed(tgt2, query_pos), - key=self.with_pos_embed(memory, pos), - value=memory, - attn_mask=memory_mask, - key_padding_mask=memory_key_padding_mask, - )[0] - tgt = tgt + self.dropout2(tgt2) - tgt2 = self.norm3(tgt) - tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2)))) - tgt = tgt + self.dropout3(tgt2) - return tgt - - def forward( - self, - tgt, - memory, - tgt_mask: Optional[Tensor] = None, - memory_mask: Optional[Tensor] = None, - tgt_key_padding_mask: Optional[Tensor] = None, - memory_key_padding_mask: Optional[Tensor] = None, - pos: Optional[Tensor] = None, - query_pos: Optional[Tensor] = None, - ): - if self.normalize_before: - return self.forward_pre( - tgt, - memory, - tgt_mask, - memory_mask, - tgt_key_padding_mask, - memory_key_padding_mask, - pos, - query_pos, - ) - return self.forward_post( - tgt, - memory, - tgt_mask, - memory_mask, - tgt_key_padding_mask, - memory_key_padding_mask, - pos, - query_pos, - ) - - -def _get_clones(module, N): - return nn.ModuleList([copy.deepcopy(module) for i in range(N)]) - - -def _get_activation_fn(activation): - """Return an activation function given a string""" - if activation == "relu": - return F.relu - if activation == "gelu": - return F.gelu - if activation == "glu": - return F.glu - raise RuntimeError(f"activation should be relu/gelu, not {activation}.") diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/run.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/run.py deleted file mode 100644 index 6e4ef010167e8bfb0fb64f9d109bd4e8e81fc479..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/run.py +++ /dev/null @@ -1,76 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import logging -from omegaconf import OmegaConf -import os -import sys -from typing import Any - -from dinov3.eval.segmentation.config import SegmentationConfig -from dinov3.eval.segmentation.eval import test_segmentation -from dinov3.eval.segmentation.train import train_segmentation -from dinov3.eval.helpers import args_dict_to_dataclass, cli_parser, write_results -from dinov3.eval.setup import load_model_and_context -from dinov3.run.init import job_context - - -logger = logging.getLogger("dinov3") - -RESULTS_FILENAME = "results-semantic-segmentation.csv" -MAIN_METRICS = ["mIoU"] - - -def run_segmentation_with_dinov3( - backbone, - config, -): - if config.load_from: - logger.info("Testing model performance on a pretrained decoder head") - return test_segmentation(backbone=backbone, config=config) - assert config.decoder_head.type == "linear", "Only linear head is supported for training" - return train_segmentation(backbone=backbone, config=config) - - -def benchmark_launcher(eval_args: dict[str, object]) -> dict[str, Any]: - """Initialization of distributed and logging are preconditions for this method""" - if "config" in eval_args: # using a config yaml file, useful for training - base_config_path = eval_args.pop("config") - output_dir = eval_args["output_dir"] - base_config = OmegaConf.load(base_config_path) - structured_config = OmegaConf.structured(SegmentationConfig) - dataclass_config: SegmentationConfig = OmegaConf.to_object( - OmegaConf.merge( - structured_config, - base_config, - OmegaConf.create(eval_args), - ) - ) - else: # either using default values, or only adding some args to the command line - dataclass_config, output_dir = args_dict_to_dataclass(eval_args=eval_args, config_dataclass=SegmentationConfig) - backbone = None - if dataclass_config.model: - backbone, _ = load_model_and_context(dataclass_config.model, output_dir=output_dir) - else: - assert dataclass_config.load_from == "dinov3_vit7b16_ms" - logger.info(f"Segmentation Config:\n{OmegaConf.to_yaml(dataclass_config)}") - segmentation_file_path = os.path.join(output_dir, "segmentation_config.yaml") - OmegaConf.save(config=dataclass_config, f=segmentation_file_path) - results_dict = run_segmentation_with_dinov3(backbone=backbone, config=dataclass_config) - write_results(results_dict, output_dir, RESULTS_FILENAME) - return results_dict - - -def main(argv=None): - if argv is None: - argv = sys.argv[1:] - eval_args = cli_parser(argv) - with job_context(output_dir=eval_args["output_dir"]): - benchmark_launcher(eval_args=eval_args) - return 0 - - -if __name__ == "__main__": - sys.exit(main()) diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/schedulers.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/schedulers.py deleted file mode 100644 index 907f3bf8daf07fe17c2c7fc3127a933d813cb8e6..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/schedulers.py +++ /dev/null @@ -1,191 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -from inspect import signature -import math -from typing import Any, Literal - -import torch -from packaging.version import Version -from torch.optim import lr_scheduler as torch_schedulers -from torch.optim.optimizer import Optimizer - -TORCH_VERSION = Version(torch.__version__) - - -def annealing_cos(start, end, pct): - "Cosine anneal from `start` to `end` as pct goes from 0.0 to 1.0." - cos_out = math.cos(math.pi * pct) + 1 - return end + (start - end) / 2.0 * cos_out - - -def annealing_linear(start, end, pct): - "Linearly anneal from `start` to `end` as pct goes from 0.0 to 1.0." - return (end - start) * pct + start - - -class WarmupOneCycleLR(torch_schedulers.LRScheduler): - def __init__( - self, - optimizer: Optimizer, - max_lr: float | None = None, - total_steps: int = 0, - warmup_iters: int = 0, - warmup_ratio: float = 0.0, - pct_start: float = 0.295, - anneal_strategy: Literal["cos", "linear"] = "cos", - base_momentum: float = 0.85, - max_momentum: float = 0.95, - div_factor: float = 25.0, - final_div_factor: float = 1000.0, - use_beta1: bool = True, - update_momentum: bool = True, - last_epoch: int = -1, - ): - """ - A variant of OneCycleLR with a warmup on top which potentially - replaces the first phase of the original OneCycleLR. - """ - self.warmup_iters = warmup_iters - self.warmup_ratio = warmup_ratio - self.max_lr = max_lr - self.min_point = float(pct_start * total_steps) - self.base_momentum = base_momentum - self.max_momentum = max_momentum - self.total_steps = total_steps - self.use_beta1 = use_beta1 - self.anneal_strategy = anneal_strategy - self.final_div_factor = final_div_factor - self.update_momentum = update_momentum - assert self.anneal_strategy in [ - "cos", - "linear", - ], f"Only cosine and linear-annealing strategy supported, got {self.anneal_strategy}" - assert total_steps > 0 - - # Initialize learning rate variables and momentum - for group in optimizer.param_groups: - if "initial_lr" not in group: - assert last_epoch == -1 - ml = group["lr"] - assert isinstance(ml, float) # makes sure that the variable is well updated - group["initial_lr"] = ml / div_factor - group["max_lr"] = ml - group["min_lr"] = group["initial_lr"] / final_div_factor - # initialize learning rate - group["lr"] = ml / final_div_factor if self.warmup_iters > 0 else group["initial_lr"] - if self.use_beta1: - group["betas"] = (self.max_momentum, *group["betas"][1:]) - elif self.update_momentum: - group["momentum"] = self.max_momentum - - super().__init__(optimizer, last_epoch) - - def _anneal_func(self, *args, **kwargs): - if self.anneal_strategy == "cos": - return annealing_cos(*args, **kwargs) - elif self.anneal_strategy == "linear": - return annealing_linear(*args, **kwargs) - - def _compute_lr_momentum(self, optimizer_param_group): - # torch.optim.lr_scheduler.LRScheduler does an initial - # step that sets self._step_count = 1 - step_num = (self._step_count - 1) if self.last_epoch != -1 else 0 - momentum = 0 - if step_num < self.warmup_iters: - if self.warmup_ratio: - k = (1 - step_num / self.warmup_iters) * (1 - self.warmup_ratio) - warmup_lr = optimizer_param_group["max_lr"] * (1 - k) - thelr = warmup_lr * (1 - step_num / self.total_steps) - else: - gmax = ( - optimizer_param_group["max_lr"] * (1 + math.cos(math.pi * step_num / float(self.total_steps))) / 2 - ) - thelr = optimizer_param_group["max_lr"] / self.final_div_factor + gmax * step_num / float( - self.warmup_iters - ) - else: - pct = (step_num - self.warmup_iters) / float(self.total_steps - self.warmup_iters) - step_num_to_use = step_num - momentum = self._anneal_func( - self.base_momentum, - self.max_momentum, - pct, - ) - if self.anneal_strategy == "cos": - step_num_to_use += 1 - thelr = self._anneal_func( - optimizer_param_group["max_lr"], - optimizer_param_group["min_lr"], - step_num_to_use / float(self.total_steps), - ) - return thelr, momentum - - def get_lr(self): - """Compute the learning rate of each parameter group.""" - if TORCH_VERSION >= Version("2.4.0"): - torch_schedulers._warn_get_lr_called_within_step(self) - - lrs = [] - step_num = self.last_epoch - - if step_num > self.total_steps: - raise ValueError( - f"Tried to step {step_num} times. The specified number of total steps is {self.total_steps}" # noqa: UP032 - ) - - for group in self.optimizer.param_groups: - computed_lr, computed_momentum = self._compute_lr_momentum(group) - lrs.append(computed_lr) # type: ignore[possibly-undefined] - if self.use_beta1: - group["betas"] = (computed_momentum, *group["betas"][1:]) # type: ignore[possibly-undefined] - elif self.update_momentum: - group["momentum"] = computed_momentum # type: ignore[possibly-undefined] - - return lrs - - -def build_scheduler( - scheduler_type: str, - optimizer: Optimizer, - lr: float, - total_iter: int, - constructor_kwargs: dict[str, Any], -): - _kwargs = {} - _kwargs.update(**constructor_kwargs) - constructor_fn = SCHEDULERS_DICT[scheduler_type] - accepted_kwargs = signature(constructor_fn).parameters.keys() - keywords = list(constructor_kwargs.keys()) - for key in keywords: - if key not in accepted_kwargs: - # ignore arguments that are not part of kwargs - _kwargs.pop(key) - if scheduler_type in ["OneCycleLR", "WarmupOneCycleLR", "WarmupMultiStepLR"]: - _kwargs.update( - dict( - max_lr=lr, - total_steps=total_iter, - ) - ) - elif scheduler_type in [ - "ConstantLR", - "LinearLR", - "PolynomialLR", - ]: - constructor_kwargs.update(dict(total_iters=total_iter)) - - return constructor_fn(optimizer, **_kwargs) - - -SCHEDULERS_DICT = { - "ConstantLR": torch_schedulers.ConstantLR, - "LinearLR": torch_schedulers.LinearLR, - "MultiStepLR": torch_schedulers.MultiStepLR, - "PolynomialLR": torch_schedulers.PolynomialLR, - "StepLR": torch_schedulers.StepLR, - "OneCycleLR": torch_schedulers.OneCycleLR, - "WarmupOneCycleLR": WarmupOneCycleLR, -} diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/train.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/train.py deleted file mode 100644 index cb73d0646a2b84fa3f02ab5c26c25cbae0024339..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/train.py +++ /dev/null @@ -1,327 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -from functools import partial -import logging -import numpy as np -import os -import random - -import torch -import torch.distributed as dist - -from dinov3.data import DatasetWithEnumeratedTargets, SamplerType, make_data_loader, make_dataset -import dinov3.distributed as distributed -from dinov3.eval.segmentation.eval import evaluate_segmentation_model -from dinov3.eval.segmentation.loss import MultiSegmentationLoss -from dinov3.eval.segmentation.metrics import SEGMENTATION_METRICS -from dinov3.eval.segmentation.models import build_segmentation_decoder -from dinov3.eval.segmentation.schedulers import build_scheduler -from dinov3.eval.segmentation.transforms import make_segmentation_eval_transforms, make_segmentation_train_transforms -from dinov3.logging import MetricLogger, SmoothedValue - -logger = logging.getLogger("dinov3") - - -class InfiniteDataloader: - def __init__(self, dataloader: torch.utils.data.DataLoader): - self.dataloader = dataloader - self.data_iterator = iter(dataloader) - self.sampler = dataloader.sampler - if not hasattr(self.sampler, "epoch"): - self.sampler.epoch = 0 # type: ignore - - def __iter__(self): - return self - - def __len__(self) -> int: - return len(self.dataloader) - - def __next__(self): - try: - data = next(self.data_iterator) - except StopIteration: - self.sampler.epoch += 1 - self.data_iterator = iter(self.dataloader) - data = next(self.data_iterator) - return data - - -def worker_init_fn(worker_id, num_workers, rank, seed): - """Worker init func for dataloader. - The seed of each worker equals to num_worker * rank + worker_id + user_seed - Args: - worker_id (int): Worker id. - num_workers (int): Number of workers. - rank (int): The rank of current process. - seed (int): The random seed to use. - """ - worker_seed = num_workers * rank + worker_id + seed - np.random.seed(worker_seed) - random.seed(worker_seed) - torch.manual_seed(worker_seed) - - -def validate( - segmentation_model: torch.nn.Module, - val_dataloader, - device, - autocast_dtype, - eval_res, - eval_stride, - decoder_head_type, - num_classes, - global_step, - metric_to_save, - current_best_metric_to_save_value, -): - new_metric_values_dict = evaluate_segmentation_model( - segmentation_model, - val_dataloader, - device, - eval_res, - eval_stride, - decoder_head_type, - num_classes, - autocast_dtype, - ) - logger.info(f"Step {global_step}: {new_metric_values_dict}") - # `segmentation_model` is a module list of [backbone, decoder] - # Only put the head in train mode - segmentation_model.module.segmentation_model[1].train() - is_better = False - if new_metric_values_dict[metric_to_save] > current_best_metric_to_save_value: - is_better = True - return is_better, new_metric_values_dict - - -def train_step( - segmentation_model: torch.nn.Module, - batch, - device, - scaler, - optimizer, - optimizer_gradient_clip, - scheduler, - criterion, - model_dtype, - global_step, -): - # a) load batch - batch_img, (_, gt) = batch - batch_img = batch_img.to(device) # B x C x h x w - gt = gt.to(device) # B x (num_classes if multilabel) x h x w - optimizer.zero_grad(set_to_none=True) - - # b) forward pass - with torch.autocast("cuda", dtype=model_dtype, enabled=True if model_dtype is not None else False): - pred = segmentation_model(batch_img) # B x num_classes x h x w - gt = torch.squeeze(gt).long() # Adapt gt dimension to enable loss calculation - - # c) compute loss - if gt.shape[-2:] != pred.shape[-2:]: - pred = torch.nn.functional.interpolate(input=pred, size=gt.shape[-2:], mode="bilinear", align_corners=False) - loss = criterion(pred, gt) - - # d) optimization - if scaler is not None: - scaler.scale(loss).backward() - scaler.unscale_(optimizer) - torch.nn.utils.clip_grad_norm_(segmentation_model.module.parameters(), optimizer_gradient_clip) - scaler.step(optimizer) - scaler.update() - else: - loss.backward() - torch.nn.utils.clip_grad_norm_(segmentation_model.module.parameters(), optimizer_gradient_clip) - optimizer.step() - - if global_step > 0: # inheritance from old mmcv code - scheduler.step() - - return loss - - -def train_segmentation( - backbone, - config, -): - assert config.decoder_head.type == "linear", "Only linear head is supported for training" - # 1- load the segmentation decoder - logger.info("Initializing the segmentation model") - segmentation_model = build_segmentation_decoder( - backbone, - config.decoder_head.backbone_out_layers, - "linear", - num_classes=config.decoder_head.num_classes, - autocast_dtype=config.model_dtype.autocast_dtype, - ) - global_device = distributed.get_rank() - local_device = torch.cuda.current_device() - segmentation_model = torch.nn.parallel.DistributedDataParallel( - segmentation_model.to(local_device), device_ids=[local_device] - ) # should be local rank - model_parameters = filter(lambda p: p.requires_grad, segmentation_model.parameters()) - logger.info(f"Number of trainable parameters: {sum(p.numel() for p in model_parameters)}") - - # 2- create data transforms + dataloaders - train_transforms = make_segmentation_train_transforms( - img_size=config.transforms.train.img_size, - random_img_size_ratio_range=config.transforms.train.random_img_size_ratio_range, - crop_size=config.transforms.train.crop_size, - flip_prob=config.transforms.train.flip_prob, - reduce_zero_label=config.eval.reduce_zero_label, - ) - val_transforms = make_segmentation_eval_transforms( - img_size=config.transforms.eval.img_size, - inference_mode=config.eval.mode, - ) - - train_dataset = DatasetWithEnumeratedTargets( - make_dataset( - dataset_str=f"{config.datasets.train}:root={config.datasets.root}", - transforms=train_transforms, - ) - ) - train_sampler_type = None - if distributed.is_enabled(): - train_sampler_type = SamplerType.DISTRIBUTED - init_fn = partial( - worker_init_fn, num_workers=config.num_workers, rank=global_device, seed=config.seed + global_device - ) - train_dataloader = InfiniteDataloader( - make_data_loader( - dataset=train_dataset, - batch_size=config.bs, - num_workers=config.num_workers, - sampler_type=train_sampler_type, - shuffle=True, - persistent_workers=False, - worker_init_fn=init_fn, - ) - ) - - val_dataset = DatasetWithEnumeratedTargets( - make_dataset( - dataset_str=f"{config.datasets.val}:root={config.datasets.root}", - transforms=val_transforms, - ) - ) - val_sampler_type = None - if distributed.is_enabled(): - val_sampler_type = SamplerType.DISTRIBUTED - val_dataloader = make_data_loader( - dataset=val_dataset, - batch_size=1, - num_workers=config.num_workers, - sampler_type=val_sampler_type, - drop_last=False, - shuffle=False, - persistent_workers=True, - ) - - # 3- define and create scaler, optimizer, scheduler, loss - scaler = None - if config.model_dtype.autocast_dtype is not None: - scaler = torch.amp.GradScaler("cuda") - - optimizer = torch.optim.AdamW( - [ - { - "params": filter(lambda p: p.requires_grad, segmentation_model.parameters()), - "lr": config.optimizer.lr, - "betas": (config.optimizer.beta1, config.optimizer.beta2), - "weight_decay": config.optimizer.weight_decay, - } - ] - ) - scheduler = build_scheduler( - config.scheduler.type, - optimizer=optimizer, - lr=config.optimizer.lr, - total_iter=config.scheduler.total_iter, - constructor_kwargs=config.scheduler.constructor_kwargs, - ) - criterion = MultiSegmentationLoss( - diceloss_weight=config.train.diceloss_weight, celoss_weight=config.train.celoss_weight - ) - total_iter = config.scheduler.total_iter - global_step = 0 - global_best_metric_values = {metric: 0.0 for metric in SEGMENTATION_METRICS} - - # 5- train the model - metric_logger = MetricLogger(delimiter=" ") - metric_logger.add_meter("loss", SmoothedValue(window_size=4, fmt="{value:.3f}")) - for batch in metric_logger.log_every( - train_dataloader, - 50, - header="Train: ", - start_iteration=global_step, - n_iterations=total_iter, - ): - if global_step >= total_iter: - break - loss = train_step( - segmentation_model, - batch, - local_device, - scaler, - optimizer, - config.optimizer.gradient_clip, - scheduler, - criterion, - config.model_dtype.autocast_dtype, - global_step, - ) - global_step += 1 - metric_logger.update(loss=loss) - if global_step % config.eval.eval_interval == 0: - dist.barrier() - is_better, best_metric_values_dict = validate( - segmentation_model, - val_dataloader, - local_device, - config.model_dtype.autocast_dtype, - config.eval.crop_size, - config.eval.stride, - config.decoder_head.type, - config.decoder_head.num_classes, - global_step, - config.metric_to_save, - global_best_metric_values[config.metric_to_save], - ) - if is_better: - logger.info(f"New best metrics at Step {global_step}: {best_metric_values_dict}") - global_best_metric_values = best_metric_values_dict - - # one last validation only if the number of total iterations is NOT divisible by eval interval: - if total_iter % config.eval.eval_interval: - is_better, best_metric_values_dict = validate( - segmentation_model, - val_dataloader, - local_device, - config.model_dtype.autocast_dtype, - config.eval.crop_size, - config.eval.stride, - config.decoder_head.type, - config.decoder_head.num_classes, - global_step, - config.metric_to_save, - global_best_metric_values[config.metric_to_save], - ) - if is_better: - logger.info(f"New best metrics at Step {global_step}: {best_metric_values_dict}") - global_best_metric_values = best_metric_values_dict - logger.info("Training is done!") - # segmentation_model is a module list of [backbone, decoder] - # Only save the decoder head - torch.save( - { - "model": {k: v for k, v in segmentation_model.module.state_dict().items() if "segmentation_model.1" in k}, - "optimizer": optimizer.state_dict(), - }, - os.path.join(config.output_dir, "model_final.pth"), - ) - logger.info(f"Final best metrics: {global_best_metric_values}") - return global_best_metric_values diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/transforms.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/transforms.py deleted file mode 100644 index 2b877177f6721902f94c324ec9ef286ccc9b9d15..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/segmentation/transforms.py +++ /dev/null @@ -1,474 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import numpy as np -from PIL import Image -from typing import Any, List, Optional, Sequence, Tuple, Union - -import torch -import torch.nn.functional as F -from torchvision import transforms as T -from torchvision.transforms import functional as Fv -from torchvision.transforms import v2 -from torchvision.tv_tensors import Mask - -from dinov3.data.transforms import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, make_normalize_transform -from dinov3.eval.segmentation.metrics import preprocess_nonzero_labels - - -class PhotoMetricDistortion(torch.nn.Module): - """Apply photometric distortion to image sequentially, every transformation - is applied with a probability of 0.5. - 1. random brightness - 2. random contrast (mode 0) - 3. convert color from BGR to HSV - 4. random saturation - 5. random hue - 6. convert color from HSV to BGR - 7. random contrast (mode 1) - Args: - brightness_delta (int): delta of brightness. - contrast_range (tuple): range of contrast. - saturation_range (tuple): range of saturation. - hue_delta (int): delta of hue. - """ - - def __init__( - self, - brightness_delta: int = 32, - contrast_range: Sequence[float] = (0.5, 1.5), - saturation_range: Sequence[float] = (0.5, 1.5), - hue_range: Sequence[float] = (-0.5, 0.5), - ): - super().__init__() - self.brightness_delta = brightness_delta - self.contrast_lower, self.contrast_upper = contrast_range - self.saturation_lower, self.saturation_upper = saturation_range - self.hue_lower, self.hue_upper = hue_range - - def convert(self, img: np.ndarray, alpha: float = 1.0, beta: float = 0.0) -> np.ndarray: - """Multiple with alpha and add beat with clip.""" - img = img.astype(np.float32) * alpha + beta - img = np.clip(img, 0, 255) - return img.astype(np.uint8) - - def brightness(self, img: np.ndarray) -> np.ndarray: - if np.random.randint(2): - return self.convert(img, beta=np.random.uniform(-self.brightness_delta, self.brightness_delta)) - return img - - def contrast(self, img: np.ndarray) -> np.ndarray: - if np.random.randint(2): - return self.convert(img, alpha=np.random.uniform(self.contrast_lower, self.contrast_upper)) - return img - - def saturation(self, img: np.ndarray) -> np.ndarray: - if np.random.randint(2): - saturation_factor = np.random.uniform(self.saturation_lower, self.saturation_upper) - img_tensor = torch.tensor(img.astype(np.uint8)).permute((2, 0, 1)) - img_tensor = Fv.adjust_saturation(img_tensor, saturation_factor) - img = img_tensor.permute((1, 2, 0)).numpy() - return img - - def hue(self, img: np.ndarray) -> np.ndarray: - if np.random.randint(2): - hue_factor = np.random.uniform(self.hue_lower, self.hue_upper) - img_tensor = torch.tensor(img.astype(np.uint8)).permute((2, 0, 1)) - img_tensor = Fv.adjust_hue(img_tensor, hue_factor) - img = img_tensor.permute((1, 2, 0)).numpy() - return img - - def forward(self, img, label) -> Tuple[torch.Tensor, Any]: - """Transform function to perform photometric distortion on images.""" - # Operations need numpy arrays - img = img.permute((1, 2, 0)).numpy() - # random brightness - img = self.brightness(img) - # mode == 0 --> do random contrast first - # mode == 1 --> do random contrast last - mode = np.random.randint(2) - if mode == 1: - img = self.contrast(img) - # random saturation - img = self.saturation(img) - # random hue - img = self.hue(img) - # random contrast - if mode == 0: - img = self.contrast(img) - return torch.tensor(img.astype(np.float32)).permute((2, 0, 1)), label - - -class ReduceZeroLabel(torch.nn.Module): - """Operation on the labels when class 0 is to be ignored.""" - - def __init__(self, ignore_index=255): - super().__init__() - self.ignore_index = ignore_index - - def forward(self, img, label): - label = preprocess_nonzero_labels(label, ignore_index=self.ignore_index) - return img, label - - -class MaybeApplyImageLabel(torch.nn.Module): - """Apply a given operation on both image and label - given a probability threshold. - Args: - _transform (torchvision.transforms): type of transform to apply. - Since this transform is applied on both image and label, - it has to be deterministic (e.g. horizontal flip, non-random crop). - _threshold (float): probability of applying the above transform.""" - - def __init__(self, transform, threshold: float = 0.5): - super().__init__() - self._transform = transform - self._threshold = threshold - - def __call__(self, img, label): - x = np.random.rand() - if x < self._threshold: - return self._transform(img), self._transform(label) - return img, label - - -class FixedSideResize: - """Resize an image, given a fixed value for the small side. - Args: - small_size (int): small size to resize an image to. - example: if small_size = 512, an image of size (300, 400) will be resized to (512, 683) - image_interpolation (T.InterpolationMode): Interpolation mode when resizing a given image. - label_interpolation (T.InterpolationMode): Interpolation mode when resizing a given label. - random_img_size_ratio_range (tuple(min, max)): If used, for a given image, a random ratio - between the range is used to multiply to `small_size` for resizing - inference_mode (str): Dataset inference mode. - If value is "whole", resize both image and label for a single prediction on the resized image. - If value is "slide", resize image, do sliding inference on it, then scale it back to the - original image size for final prediction - the label doesn't need to be resized. - Returns: - image, label (PIL.Image, tensor.Tensor): resized image and label - """ - - def __init__( - self, - small_size, - image_interpolation, - label_interpolation, - random_img_size_ratio_range=None, - inference_mode="whole", - use_tta=False, - tta_img_size_ratio_range=[1.0], - ): - self.small_size = small_size - self.image_interpolation = image_interpolation - self.label_interpolation = label_interpolation - self.random_img_size_ratio_range = random_img_size_ratio_range - self.inference_mode = inference_mode - self.use_tta = use_tta - self.tta_img_size_ratio_range = tta_img_size_ratio_range - - def _random_sample_ratio(self): - min_ratio, max_ratio = self.random_img_size_ratio_range - ratio = np.random.random_sample() * (max_ratio - min_ratio) + min_ratio - return int(self.small_size * ratio) - - def _resize(self, img, label, small_size): - init_width, init_height = img.size - if init_height > init_width: - new_width = small_size - new_height = int(small_size * init_height / init_width + 0.5) - else: - new_height = small_size - new_width = int(small_size * init_width / init_height + 0.5) - - img = T.Resize(size=(new_height, new_width), interpolation=self.image_interpolation)(img) - if self.inference_mode == "whole": - label = T.Resize(size=(new_height, new_width), interpolation=self.label_interpolation)(label) - return img, label - - def __call__(self, img, label): - if not self.use_tta: - small_size = self.small_size - if self.random_img_size_ratio_range: - small_size = self._random_sample_ratio() - return self._resize(img, label, small_size) - - tta_img_list = [] # Used only if TTA - for tta_ratio in self.tta_img_size_ratio_range: - tta_size = int(self.small_size * tta_ratio) - if tta_ratio < 1: - tta_size = int(np.ceil(tta_size / 32)) * 32 - tta_img, _ = self._resize(img, label, tta_size) - tta_img_list.append(tta_img) - return tta_img_list, label - - -class ResizeV2: - """ - Resize both image and label using different interpolation modes. - """ - - def __init__(self, size, image_interpolation, label_interpolation): - self.size = size - self.image_interpolation = image_interpolation - self.label_interpolation = label_interpolation - - def __call__(self, img, label): - img = T.Resize(size=self.size, interpolation=self.image_interpolation)(img) - label = T.Resize(size=self.size, interpolation=self.label_interpolation)(label) - return img, label - - -class CustomResize(torch.nn.Module): - def __init__( - self, - img_resize, - image_interpolation, - label_interpolation, - random_img_size_ratio_range=None, - inference_mode="whole", - use_tta=False, - tta_img_size_ratio_range=[1.0], - ): - super().__init__() - if isinstance(img_resize, int): - self.resize_function = FixedSideResize( - small_size=img_resize, - image_interpolation=image_interpolation, - label_interpolation=label_interpolation, - random_img_size_ratio_range=random_img_size_ratio_range, - inference_mode=inference_mode, - use_tta=use_tta, - tta_img_size_ratio_range=tta_img_size_ratio_range, - ) - else: - self.resize_function = ResizeV2( - size=img_resize, - image_interpolation=image_interpolation, - label_interpolation=label_interpolation, - ) - - def forward(self, img, label): - return self.resize_function(img, label) - - -class RandomCropWithLabel(torch.nn.Module): - """Randomly crop the image & segmentation label. - Args: - crop_size (tuple(h, w)): Expected size after cropping. - cat_max_ratio (float): The maximum ratio that a single category could - occupy in the cropped image. Default value is 0.75. - ignore_index (int): Index to ignore when measuring the category ratio - in a cropped image - Returns: - cropped_img (torch.Tensor), Optional[crop_bbox](tuple) - """ - - def __init__(self, crop_size, cat_max_ratio=0.75, ignore_index=255): - super().__init__() - assert crop_size[0] > 0 and crop_size[1] > 0 - self.crop_size = crop_size - self.cat_max_ratio = cat_max_ratio - self.ignore_index = ignore_index - - def get_crop_bbox(self, img): - """Randomly get a crop bounding box.""" - margin_h = max(img.shape[-2] - self.crop_size[0], 0) - margin_w = max(img.shape[-1] - self.crop_size[1], 0) - offset_h = np.random.randint(0, margin_h + 1) - offset_w = np.random.randint(0, margin_w + 1) - crop_y1, crop_y2 = offset_h, offset_h + self.crop_size[0] - crop_x1, crop_x2 = offset_w, offset_w + self.crop_size[1] - - return crop_y1, crop_y2, crop_x1, crop_x2 - - def crop(self, img, crop_bbox): - """Crop given a crop bounding box""" - crop_y1, crop_y2, crop_x1, crop_x2 = crop_bbox - img = img[:, crop_y1:crop_y2, crop_x1:crop_x2] - return img - - def forward(self, img, label): - """Find an adequate crop for a given image and crop it""" - # Create a random crop_bbox - new_crop_bbox = self.get_crop_bbox(img) - if self.cat_max_ratio < 1.0: - # Check that the ratio of label_counts / nb_pixels created - # with the random crop_bbox is under `cat_max_ratio` - # Repeat until 10 times to find a good crop_bbox - for _ in range(10): - seg_temp = self.crop(label, new_crop_bbox) - labels, cnt = np.unique(seg_temp, return_counts=True) - cnt = cnt[labels != self.ignore_index] - if len(cnt) > 1 and np.max(cnt) / np.sum(cnt) < self.cat_max_ratio: - break - new_crop_bbox = self.get_crop_bbox(img) - - return self.crop(img, new_crop_bbox), self.crop(label, new_crop_bbox) - - -class HorizontalFlipAug(torch.nn.Module): - def forward(self, img_list, label): - """Call function to apply test time augment transforms on results. - - Args: - img (PIL image | torch.Tensor | List[PIL image]): Data to transform. - - Returns: - list: A list of augmented data. - """ - if isinstance(img_list, Image.Image): - img_list = [img_list] - augmented_img_list = [Fv.hflip(img) for img in img_list] - img_list.extend(augmented_img_list) - return img_list, label - - def inverse(self, stacked_left_right_pair): - pre_aug_batch_size = len(stacked_left_right_pair) // 2 - assert pre_aug_batch_size * 2 == len(stacked_left_right_pair) - orig_img_list = stacked_left_right_pair[:pre_aug_batch_size] - orig_img_list.extend([Fv.hflip(img) for img in stacked_left_right_pair[pre_aug_batch_size:]]) - return orig_img_list - - -class PadTensor(torch.nn.Module): - """Pad a given tensor to the desired shape""" - - def __init__(self, pad_shape=[512, 512], img_pad_value=0, label_pad_value=255): - super().__init__() - self.pad_shape = pad_shape - self.img_pad_value = img_pad_value - self.label_pad_value = label_pad_value - - def forward(self, img, label): - h, w = img.shape[-2:] - new_h, new_w = self.pad_shape[0] - h, self.pad_shape[1] - w - img = F.pad(input=img, pad=(0, new_w, 0, new_h), mode="constant", value=self.img_pad_value) - label = F.pad(input=label, pad=(0, new_w, 0, new_h), mode="constant", value=self.label_pad_value) - return img, label - - -class NormalizeImage(torch.nn.Module): - def __init__(self, mean, std): - super().__init__() - self.normalize_function = make_normalize_transform(mean=mean, std=std) - - def forward(self, img, label): - return self.normalize_function(img.float()), label - - -class TransformImages(torch.nn.Module): - """Given a list of operations, apply them on a tensor or a list of transforms. - Transforms apply on images. Always return a list of tensors for coherent output format. - Args: - _transform (List[torchvision.transforms]): transforms to apply. - """ - - def __init__(self, transforms): - super().__init__() - self._transforms = transforms - - def forward(self, img, label): - if isinstance(img, (torch.Tensor, Image.Image)): - img = [img] - for transform in self._transforms: - # only apply transforms on the augmented images - img = [transform(im, label)[0] for im in img] - return img, label - - -class MaskToTensor(torch.nn.Module): - """Read segmentation mask from arrays or PIL images""" - - def forward(self, img, label): - if isinstance(label, np.ndarray): - return img, Mask(label).permute(2, 0, 1) - return img, Mask(label) - - -def make_segmentation_train_transforms( - *, - img_size: Optional[Union[List[int], int]] = None, - image_interpolation: T.InterpolationMode = T.InterpolationMode.BILINEAR, - label_interpolation: T.InterpolationMode = T.InterpolationMode.NEAREST, - random_img_size_ratio_range: Optional[List[float]] = None, - crop_size: Optional[Tuple[int]] = None, - flip_prob: float = 0.0, - reduce_zero_label: bool = False, - mean: Sequence[float] = [mean * 255 for mean in IMAGENET_DEFAULT_MEAN], - std: Sequence[float] = [std * 255 for std in IMAGENET_DEFAULT_STD], -): - # Label conversion to tensor - transforms_list = [MaskToTensor()] # type: List[Any] - # Resizing - if img_size is not None: - transforms_list.append( - CustomResize( - img_resize=img_size, - image_interpolation=image_interpolation, - label_interpolation=label_interpolation, - inference_mode="whole", # when training, always resize image + label - random_img_size_ratio_range=random_img_size_ratio_range, - ) - ) - # Conversion to torch.Tensor - transforms_list.extend([v2.PILToTensor()]) - - # Reducing zero labels - if reduce_zero_label: - transforms_list.append(ReduceZeroLabel()) - - # Random crop - if crop_size: - transforms_list.append(RandomCropWithLabel(crop_size=crop_size)) - - # Rest of the image and label-specific transforms - transforms_list.extend( - [ - MaybeApplyImageLabel(transform=Fv.hflip, threshold=flip_prob), - PhotoMetricDistortion(), - NormalizeImage(mean=mean, std=std), - ] - ) - - # Pad if cropping was done previously - if crop_size: - transforms_list.append(PadTensor(pad_shape=crop_size, img_pad_value=0, label_pad_value=255)) - - return v2.Compose(transforms_list) - - -def make_segmentation_eval_transforms( - *, - img_size: Optional[Union[List[int], int]] = None, - inference_mode: str = "whole", - image_interpolation: T.InterpolationMode = T.InterpolationMode.BILINEAR, - label_interpolation: T.InterpolationMode = T.InterpolationMode.NEAREST, - use_tta: bool = False, - tta_ratios: Sequence[float] = [1.0], - mean: Sequence[float] = [mean * 255 for mean in IMAGENET_DEFAULT_MEAN], - std: Sequence[float] = [std * 255 for std in IMAGENET_DEFAULT_STD], -): - # Label conversion to tensor - transforms_list = [MaskToTensor()] # type: List[Any] - # Optional resizing - if img_size is not None: - transforms_list.append( - CustomResize( - img_resize=img_size, - image_interpolation=image_interpolation, - label_interpolation=label_interpolation, - inference_mode=inference_mode, - use_tta=use_tta, - tta_img_size_ratio_range=tta_ratios, - ) - ) - - if use_tta: - transforms_list.append(HorizontalFlipAug()) - # Always return a list of tensors for prediction at evaluation time - transforms_list.append(TransformImages(transforms=[v2.PILToTensor(), NormalizeImage(mean=mean, std=std)])) - - return v2.Compose(transforms_list) diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/setup.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/setup.py deleted file mode 100644 index be4e20fced3e5a35e68a06eca787e1207c665846..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/setup.py +++ /dev/null @@ -1,85 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -from dataclasses import dataclass -from typing import Tuple, TypedDict - -import torch -import torch.backends.cudnn as cudnn -import torch.nn as nn - -from dinov3.configs import DinoV3SetupArgs, setup_config -from dinov3.models import build_model_for_eval - - -@dataclass -class ModelConfig: - # Loading a local file - config_file: str | None = None - pretrained_weights: str | None = None - # Loading a DINOv3 or v2 model from torch.hub - dino_hub: str | None = None - - -class BaseModelContext(TypedDict): - """ - An object that contains the context of a model (autocast, description, ...) - """ - - autocast_dtype: torch.dtype # default could be torch.float - - -def load_model_and_context(model_config: ModelConfig, output_dir: str) -> tuple[torch.nn.Module, BaseModelContext]: - if model_config.dino_hub is not None: - assert model_config.pretrained_weights is None and model_config.config_file is None - if "dinov3" in model_config.dino_hub: - repo = "dinov3" - elif "dinov2" in model_config.dino_hub: - repo = "dinov2" - else: - raise ValueError - model = torch.hub.load(f"facebookresearch/{repo}", model_config.dino_hub) - base_model_context = BaseModelContext(autocast_dtype=torch.float) - else: - model, base_model_context = setup_and_build_model( - config_file=model_config.config_file, - pretrained_weights=model_config.pretrained_weights, - output_dir=output_dir, - ) - - model.cuda() - model.eval() - return model, base_model_context - - -def get_autocast_dtype(config): - teacher_dtype_str = config.compute_precision.param_dtype - if teacher_dtype_str == "bf16": - return torch.bfloat16 - else: - return torch.float - - -def setup_and_build_model( - config_file: str, - pretrained_weights: str | None = None, - shard_unsharded_model: bool = False, - output_dir: str = "", - opts: list | None = None, - **ignored_kwargs, -) -> Tuple[nn.Module, BaseModelContext]: - cudnn.benchmark = True - del ignored_kwargs - setup_args = DinoV3SetupArgs( - config_file=config_file, - pretrained_weights=pretrained_weights, - shard_unsharded_model=shard_unsharded_model, - output_dir=output_dir, - opts=opts or [], - ) - config = setup_config(setup_args, strict_cfg=False) - model = build_model_for_eval(config, setup_args.pretrained_weights) - autocast_dtype = get_autocast_dtype(config) - return model, BaseModelContext(autocast_dtype=autocast_dtype) diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/text/ac_comp_parallelize.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/text/ac_comp_parallelize.py deleted file mode 100644 index 42617b9ea7d59bddd983078accd827b7c3b54e35..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/text/ac_comp_parallelize.py +++ /dev/null @@ -1,177 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import logging -from contextlib import suppress -from functools import partial - -import torch -import torch.nn as nn -from torch.distributed._composable.fsdp import MixedPrecisionPolicy, fully_shard -from torch.distributed.device_mesh import DeviceMesh -from torch.distributed.fsdp import register_fsdp_forward_method -from torch.distributed.fsdp._fully_shard._fsdp_state import FSDPState -from torch.utils.checkpoint import create_selective_checkpoint_contexts - -logger = logging.getLogger("dinov3") - - -def map_modules_and_blocks(models: list[nn.Module], callable) -> None: - for m in models: - for block_id, block in enumerate(m.blocks): - m.blocks[block_id] = callable(block, is_backbone_block=True) - - -def ac_compile_parallelize_and_init( - clip_model: nn.Module, - world_mesh: DeviceMesh, - do_compile: bool, - use_activation_checkpointing: bool, - use_full_activation_checkpointing: bool, - use_cuda_graphs: bool, - param_dtype_str: str = "bf16", - reduce_dtype_str: str = "fp32", -) -> None: - """ - Order of the wrappers: - 1/ Activation checkpointing on blocks - 2/ Compile blocks - 3/ FSDP blocks + global model - """ - logger.info("DISTRIBUTED FSDP -- preparing model for distributed training") - - # 1/ AC on blocks - from torch.distributed.algorithms._checkpoint.checkpoint_wrapper import ( - checkpoint_wrapper, - ) - - trained_models = [] - inference_only_models = [] - for model in [clip_model.visual_model, clip_model.text_model]: - if not model.freeze_backbone: - trained_models.append(model.backbone) - else: - inference_only_models.append(model.backbone) - trained_models.append(model.head) - - for model in trained_models: - if use_activation_checkpointing: - if use_full_activation_checkpointing: - _checkpointing_wrapper = checkpoint_wrapper - logger.info( - "using selective checkpointing on backbone with full checkpointing policy" - ) - else: - _save_list = [ - # mm - torch.ops.aten.mm.default, - torch.ops.aten._scaled_mm.default, - # attentions - torch.ops.aten._scaled_dot_product_efficient_attention.default, - torch.ops.aten._scaled_dot_product_flash_attention.default, - torch.ops._c10d_functional.reduce_scatter_tensor.default, - ] - with suppress( - AttributeError - ): # ignore exception if op is missing (old xFormers) - _save_list.append(torch.ops.xformers_flash3.flash_fwd.default) - _checkpointing_wrapper = partial( - checkpoint_wrapper, - context_fn=partial( - create_selective_checkpoint_contexts, _save_list - ), - preserve_rng_state=True, - ) - logger.info( - "using selective checkpointing on backbone with selective policy" - ) - for i, b in enumerate(model.blocks): - if not isinstance(b, nn.Identity): - model.blocks[i] = _checkpointing_wrapper(b) - - # 2/ Compile blocks - def compile_block(block: nn.Module) -> nn.Module: - if do_compile: - if use_cuda_graphs: - block.compile( - fullgraph=True, dynamic=False, options={"triton.cudagraphs": True} - ) - else: - block.compile() - return block - - def compile_backbone(backbone: nn.Module) -> nn.Module: - for block_id, block in enumerate(backbone.blocks): - backbone.blocks[block_id] = compile_block(block) - - def compile_head(head: nn.Module) -> nn.Module: - for block_id in range(head.num_blocks): - head.blocks[block_id] = compile_block(head.blocks[block_id]) - if do_compile and isinstance(head.linear_projection, nn.Linear): - head.linear_projection.compile() - - compile_backbone(clip_model.visual_model.backbone) - compile_backbone(clip_model.text_model.backbone) - compile_head(clip_model.visual_model.head) - compile_head(clip_model.text_model.head) - DTYPE_MAP = { - "fp16": torch.float16, - "bf16": torch.bfloat16, - "fp32": torch.float32, - } - mp_policy = MixedPrecisionPolicy( - param_dtype=DTYPE_MAP[param_dtype_str], - reduce_dtype=DTYPE_MAP[reduce_dtype_str], - ) - fsdp_config = {"mesh": world_mesh["dp"], "mp_policy": mp_policy} - - for block in clip_model.visual_model.backbone.blocks: - fully_shard(block, **fsdp_config, reshard_after_forward=True) - for i in range(clip_model.visual_model.head.num_blocks): - fully_shard( - clip_model.visual_model.head.blocks[i], - **fsdp_config, - reshard_after_forward=True, - ) - fully_shard( - clip_model.visual_model.head.linear_projection, - **fsdp_config, - reshard_after_forward=True, - ) - fully_shard( - clip_model.visual_model.backbone, **fsdp_config, reshard_after_forward=True - ) - fully_shard(clip_model.visual_model.head, **fsdp_config, reshard_after_forward=True) - register_fsdp_forward_method( - clip_model.visual_model.backbone, "get_intermediate_layers" - ) - for block in clip_model.text_model.backbone.blocks: - fully_shard(block, **fsdp_config, reshard_after_forward=True) - for i in range(clip_model.text_model.head.num_blocks): - fully_shard( - clip_model.text_model.head.blocks[i], - **fsdp_config, - reshard_after_forward=True, - ) - fully_shard( - clip_model.text_model.head.linear_projection, - **fsdp_config, - reshard_after_forward=True, - ) - fully_shard( - clip_model.text_model.backbone, **fsdp_config, reshard_after_forward=True - ) - fully_shard(clip_model.text_model.head, **fsdp_config, reshard_after_forward=True) - - clip_model.to_empty(device="cuda") - clip_model.init_weights() - - for model in inference_only_models: - fsdp_state: FSDPState = model._get_fsdp_state() - if not fsdp_state._fsdp_param_group: - continue - mi = fsdp_state._fsdp_param_group.post_forward_mesh_info - fsdp_state._lazy_init() - fsdp_state._fsdp_param_group.post_forward_mesh_info = mi diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/text/build_dinotxt.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/text/build_dinotxt.py deleted file mode 100644 index 0c879bd7a837bac5b065512a2dc36f0692b4867d..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/text/build_dinotxt.py +++ /dev/null @@ -1,237 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import logging -from pathlib import Path -from typing import Any, Dict, List - -import dinov3.distributed as distributed -import torch -from dinov3.checkpointer import load_checkpoint, register_dont_save_hooks -from dinov3.data import ( - make_classification_eval_transform, - make_classification_train_transform, -) -from torch.distributed import DeviceMesh -from torch.distributed._composable.replicate import replicate -from torch.distributed.device_mesh import init_device_mesh - -from dinov3.eval.text.tokenizer import get_tokenizer - -from dinov3.eval.text.ac_comp_parallelize import ac_compile_parallelize_and_init -from dinov3.eval.text.dinotxt_model import DINOTxt, DINOTxtConfig - -logger = logging.getLogger("dinov3") - - -# This allows us to load OSS DINOv2 models from pretrained weights using DINOv3 ViT -def rename_register_token( - chkpt: Dict[str, Any], n_register_tokens: int, embed_dim: int -) -> Dict[str, Any]: - if "register_tokens" in chkpt: - chkpt["storage_tokens"] = chkpt["register_tokens"] - del chkpt["register_tokens"] - else: - chkpt["storage_tokens"] = torch.zeros(1, n_register_tokens, embed_dim) - return chkpt - - -def load_backbone_checkpoint( - model: torch.nn.Module, - checkpoint_path: str, - world_mesh: DeviceMesh, - skip_load_prefixes: List[str] = [], -): - if not Path(checkpoint_path).is_dir(): # PyTorch standard checkpoint - logger.info(f"Loading pretrained weights from {checkpoint_path}") - state_dict = torch.load(checkpoint_path, map_location="cpu") - if "register_tokens" in state_dict: - state_dict["storage_tokens"] = state_dict["register_tokens"] - del state_dict["register_tokens"] - if "teacher" in state_dict: - state_dict = state_dict["teacher"] - state_dict = {k.replace("module.", ""): v for k, v in state_dict.items()} - state_dict = {k.replace("backbone.", ""): v for k, v in state_dict.items()} - state_dict = { - k: ( - torch.distributed.tensor.distribute_tensor( - v, world_mesh, src_data_rank=None - ) - if not k.startswith("rope_embed.periods") and "qkv.bias_mask" not in k - else v - ) - for k, v in state_dict.items() - } - model.load_state_dict( - { - k: v - for k, v in state_dict.items() - if not any(k.startswith(prefix) for prefix in skip_load_prefixes) - } - ) - else: # DCP checkpoint - load_checkpoint(checkpoint_path, model) - - -def compile_parallelize_and_init( - model: torch.nn.Module, - model_config: DINOTxtConfig, - world_mesh: DeviceMesh, - use_fsdp: bool, - do_compile: bool, - use_ac: bool, - use_full_ac: bool, - use_cuda_graphs: bool, - param_dtype_str: str = "bf16", - reduce_dtype_str: str = "fp32", -) -> None: - if not use_fsdp: - logger.info("Wrap in DDP, compile and initialize the model") - if do_compile: - torch._dynamo.config.optimize_ddp = "ddp_optimizer" - replicate(model, device_mesh=world_mesh, bucket_cap_mb=100) - if do_compile: - model.compile() - model = model.to_empty(device="cuda") - model.init_weights() - else: - logger.info("Wrap in FSDP, compile and initialize the model") - ac_compile_parallelize_and_init( - model, - world_mesh, - do_compile, - use_ac, - use_full_ac, - use_cuda_graphs, - param_dtype_str, - reduce_dtype_str, - ) - if model.visual_model.freeze_backbone: - vision_backbone_pretrained_weights = ( - model_config.vision_backbone_pretrained_weights - ) - logger.info( - f"Loading visual backbone pretrained-weights from: {vision_backbone_pretrained_weights}" - ) - load_backbone_checkpoint( - model.visual_model.backbone, - vision_backbone_pretrained_weights, - world_mesh, - ["dino_loss", "ibot_patch_loss", "dino_head", "ibot_head"], - ) - model.visual_model.backbone = model.visual_model.backbone.eval() - for param in model.visual_model.backbone.parameters(): - param.requires_grad = False - logger.info("Froze visual backbone!") - register_dont_save_hooks( - model, - dont_save=[ - k - for k, _ in model.state_dict().items() - if k.startswith("visual_model.backbone") - ], - ) - if model.text_model.freeze_backbone: - text_backbone_pretrained_weights = model_config.text_backbone_pretrained_weights - logger.info( - f"Loading text backbone pretrained-weights from: {text_backbone_pretrained_weights}" - ) - load_backbone_checkpoint( - model.text_model.backbone, text_backbone_pretrained_weights, world_mesh - ) - logger.info("Assigned pretrained-weights to text backbone..") - logger.info("Freezing text backbone") - model.text_model.backbone = model.text_model.backbone.eval() - for param in model.text_model.backbone.parameters(): - param.requires_grad = False - logger.info("Froze text backbone!") - register_dont_save_hooks( - model, - dont_save=[ - k - for k, _ in model.state_dict().items() - if k.startswith("text_model.backbone") - ], - ) - - -def build_model_and_tokenizer( - model_config: DINOTxtConfig, - use_fsdp: bool = True, - do_compile: bool = False, - use_ac: bool = True, - use_full_ac: bool = False, - use_cuda_graphs: bool = False, - param_dtype_str: str = "bf16", - reduce_dtype_str: str = "fp32", -): - with torch.device("meta"): - model = DINOTxt(model_config=model_config, device="meta") - world_mesh = init_device_mesh( - "cuda", - mesh_shape=(distributed.get_world_size(),), - mesh_dim_names=("dp",), - ) - compile_parallelize_and_init( - model, - model_config, - world_mesh, - use_fsdp, - do_compile, - use_ac, - use_full_ac, - use_cuda_graphs, - param_dtype_str, - reduce_dtype_str, - ) - tokenizer = get_tokenizer(model_config.text_vocab_path_or_url) - return ( - model, - make_classification_train_transform( - crop_size=model_config.vision_model_train_img_size - ), - tokenizer, - ) - - -def build_model_for_eval( - model_config: DINOTxtConfig, - pretrained_weights: str, - use_fsdp: bool = True, - do_compile: bool = True, - param_dtype_str: str = "bf16", - reduce_dtype_str: str = "fp32", -): - with torch.device("meta"): - model = DINOTxt(model_config=model_config) - world_mesh = init_device_mesh( - "cuda", - mesh_shape=(distributed.get_world_size(),), - mesh_dim_names=("dp",), - ) - compile_parallelize_and_init( - model, - model_config, - world_mesh, - use_fsdp, - do_compile, - False, - False, - False, - param_dtype_str, - reduce_dtype_str, - ) - load_checkpoint(pretrained_weights, model=model) - model.eval() - tokenizer = get_tokenizer(model_config.text_vocab_path_or_url) - crop_size = model_config.vision_model_train_img_size - resize_size = int(256 * crop_size / 224) - return ( - model, - make_classification_eval_transform( - resize_size=resize_size, crop_size=crop_size - ), - tokenizer, - ) diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/text/clip_loss.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/text/clip_loss.py deleted file mode 100644 index d6f4c80c5491c3724ee62022ce325db12db45b3e..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/text/clip_loss.py +++ /dev/null @@ -1,205 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -from typing import Callable, Optional, Tuple - -import torch - - -def _cycle_over_all_chunks( - my_chunk: torch.Tensor, - pg: torch.distributed.ProcessGroup, - step_fn: Callable[ - [torch.Tensor, int, Optional[torch.distributed.Work], Optional[torch.Tensor]], - Optional[torch.Tensor], - ], -): - next_rank = (pg.rank() + 1) % pg.size() - prev_rank = (pg.rank() - 1) % pg.size() - - extra_req: Optional[torch.distributed.Work] = None - dst_extra_chunk: Optional[torch.Tensor] = None - - dst_chunk = torch.empty_like(my_chunk) - for iter_ in range(pg.size()): - src_chunk = my_chunk if iter_ == 0 else dst_chunk - dst_chunk = torch.empty_like(my_chunk) - - if iter_ < pg.size() - 1: - send_op = torch.distributed.P2POp( - torch.distributed.isend, src_chunk, next_rank, group=pg - ) - recv_op = torch.distributed.P2POp( - torch.distributed.irecv, dst_chunk, prev_rank, group=pg - ) - reqs = torch.distributed.batch_isend_irecv([send_op, recv_op]) - else: - reqs = [] - - src_extra_chunk = step_fn( - src_chunk, (pg.rank() - iter_) % pg.size(), extra_req, dst_extra_chunk - ) - if src_extra_chunk is not None: - dst_extra_chunk = torch.empty_like(src_extra_chunk) - send_op = torch.distributed.P2POp( - torch.distributed.isend, src_extra_chunk, next_rank, group=pg - ) - recv_op = torch.distributed.P2POp( - torch.distributed.irecv, dst_extra_chunk, prev_rank, group=pg - ) - (extra_req,) = torch.distributed.batch_isend_irecv([send_op, recv_op]) - else: - extra_req = None - dst_extra_chunk = None - - for req in reqs: - req.wait() - - return extra_req, dst_extra_chunk - - -class MemoryEfficientClipLoss(torch.autograd.Function): - @staticmethod - def forward( - ctx: torch.autograd.function.FunctionCtx, - pg: torch.distributed.ProcessGroup, - image_features: torch.Tensor, - text_features: torch.Tensor, - logit_scale: torch.Tensor, - ) -> torch.Tensor: - image_partial_lses_for_me = torch.empty( - (pg.size(), image_features.shape[0]), - dtype=torch.float32, - device=image_features.device, - ) - text_partial_lses_for_others = torch.empty( - (pg.size(), text_features.shape[0]), - dtype=torch.float32, - device=text_features.device, - ) - - positives: Optional[torch.Tensor] = None - - def my_step( - incoming: torch.Tensor, - other_rank: int, - _req: Optional[torch.distributed.Work], - _extra: Optional[torch.Tensor], - ) -> None: - nonlocal positives - logits = logit_scale * (image_features @ incoming.T) - if other_rank == pg.rank(): - positives = torch.diag(logits) - torch.logsumexp(logits, dim=1, out=image_partial_lses_for_me[other_rank]) - torch.logsumexp(logits, dim=0, out=text_partial_lses_for_others[other_rank]) - - _cycle_over_all_chunks(text_features, pg, my_step) - - text_partial_lses_for_me = torch.empty_like(text_partial_lses_for_others) - torch.distributed.all_to_all_single( - text_partial_lses_for_me, text_partial_lses_for_others, group=pg - ) - - image_lses_for_me = torch.logsumexp(image_partial_lses_for_me, dim=0) - text_lses_for_me = torch.logsumexp(text_partial_lses_for_me, dim=0) - - assert positives is not None - ctx.save_for_backward( - image_features, - text_features, - logit_scale, - positives, - image_lses_for_me, - text_lses_for_me, - ) - ctx.pg = pg # type: ignore[attr-defined] - - return (-(2 * positives - image_lses_for_me - text_lses_for_me).mean() / 2).to( - positives.dtype - ) - - @staticmethod - def backward( - ctx: torch.autograd.function.FunctionCtx, *grad_outputs: torch.Tensor - ) -> Tuple[Optional[torch.Tensor], ...]: - pg: torch.distributed.ProcessGroup = ctx.pg # type: ignore[attr-defined] - image_features: torch.Tensor - text_features: torch.Tensor - logit_scale: torch.Tensor - positives: torch.Tensor - image_lses_for_me: torch.Tensor - text_lses_for_me: torch.Tensor - ( - image_features, - text_features, - logit_scale, - positives, - image_lses_for_me, - text_lses_for_me, - ) = ctx.saved_tensors # type: ignore[attr-defined] - - (grad,) = grad_outputs - grad /= 2 * positives.numel() - - text_lse_for_others = text_lses_for_me.new_empty( - (pg.size(),) + text_lses_for_me.shape - ) - torch.distributed.all_gather_into_tensor( - text_lse_for_others, text_lses_for_me, group=pg - ) - - grad_image_features = torch.zeros_like(image_features) - grad_logit_scale = torch.zeros_like(logit_scale) - - def my_step( - incoming: torch.Tensor, - other_rank: int, - req: Optional[torch.distributed.Work], - grad_text_features: Optional[torch.Tensor], - ) -> torch.Tensor: - raw_logits = image_features @ incoming.T - logits = logit_scale * raw_logits - - grad_logits = ( - (logits - image_lses_for_me[:, None]).exp() - + (logits - text_lse_for_others[other_rank, None, :]).exp() - ).to(logits.dtype) - if other_rank == pg.rank(): - torch.diagonal(grad_logits).sub_(2) - - grad_logit_scale.add_((raw_logits * grad_logits).sum()) - grad_raw_logits = grad_logits * logit_scale - - grad_image_features.addmm_(grad_raw_logits, incoming) - if req is None: - grad_text_features = torch.matmul(grad_raw_logits.T, image_features) - else: - req.wait() - assert grad_text_features is not None - grad_text_features.addmm_(grad_raw_logits.T, image_features) - - return grad_text_features - - req, grad_text_features = _cycle_over_all_chunks(text_features, pg, my_step) - req.wait() - - return ( - None, - grad * grad_image_features, - grad * grad_text_features, - grad * grad_logit_scale, - ) - - -def memory_efficient_clip_loss( - image_features: torch.Tensor, - text_features: torch.Tensor, - logit_scale: torch.Tensor, - *, - group: torch.distributed.ProcessGroup, -) -> torch.Tensor: - return MemoryEfficientClipLoss.apply( - group, image_features.float(), text_features.float(), logit_scale.float() - ) diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/text/configs/dinov3_vitl_text.yaml b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/text/configs/dinov3_vitl_text.yaml deleted file mode 100644 index 76fb1c060f0c36d358b40d6a9bc4664869af918d..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/text/configs/dinov3_vitl_text.yaml +++ /dev/null @@ -1,58 +0,0 @@ -train_dataset_str: #Example: CocoCaptions:split=TRAIN:root= -embed_dim: 2048 -vision_backbone_config: #Example: /dinov3/configs/train/dinov3_vitl16_lvd1689m_distilled.yaml -vision_backbone_pretrained_weights: #Example ~/.cache/torch/hub/checkpoints/dinov3_vitl16_pretrain_lvd1689m-8aa4cbdd.pth #Example: -vision_model_train_img_size: 224 -vision_model_use_class_token: true -vision_model_freeze_backbone: true -vision_model_num_head_blocks: 2 -vision_model_head_blocks_drop_path: 0.3 -vision_model_use_linear_projection: false -vision_model_use_patch_tokens: true -vision_model_patch_tokens_pooler_type: mean -vision_model_patch_token_layer: 1 -vision_model_use_gram_loss: false -vision_model_patch_sampling_rate_for_gram_loss: 1.0 -vision_model_normalize_patch_tokens_for_gram_loss: true -vision_model_gram_loss_weight: 1.0 -text_backbone_config: #Example: /dinov3/eval/text/configs/text_backbone.yaml -text_backbone_pretrained_weights: null -text_model_freeze_backbone: false -text_model_num_head_blocks: 0 -text_model_head_blocks_drop_prob: 0.0 -text_model_head_blocks_is_causal: true -text_model_tokens_pooler_type: argmax -text_model_use_linear_projection: true -text_vocab_path_or_url: # https://dl.fbaipublicfiles.com/dinov3/thirdparty/bpe_simple_vocab_16e6.txt.gz -init_logit_scale: 2.659260036932778 -init_logit_bias: null -freeze_logit_scale: false -output_dict: false -no_resume: false -lr_scheduler_type: cosine -lr: 0.0007 -weight_decay: 0.0001 -batch_size: 256 -beta1: 0.9 -beta2: 0.99 -eps: 1.0e-08 -eval_only: false -dataset_use_cache: false -max_checkpoints_to_keep: 5 -max_iteration: 50000 -warmup_length: 2000 -checkpointing_period: 500 -eval_freq: 5000 -gc_freq: 100 -eval_pretrained_weights: '' -output_dir: #Example: ~/tmp/dinov3_dinnotxt_vitl16 -seed: 11 -do_compile: true -use_fsdp: true -use_ac: true -use_full_ac: false -use_cuda_graphs: false -param_dtype_str: bf16 -reduce_dtype_str: fp32 -profiling: false -dtype_str: bf16 diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/text/configs/text_backbone.yaml b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/text/configs/text_backbone.yaml deleted file mode 100644 index 76f18233932d3671936bb72db3e7d628b6b1aa02..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/text/configs/text_backbone.yaml +++ /dev/null @@ -1,10 +0,0 @@ -model_name: 1280d20h24l -context_length: 77 -vocab_size: 49408 -dim: 1280 -num_heads: 20 -num_layers: 24 -ffn_ratio: 4.0 -is_causal: true -dropout_prob: 0 -ls_init_value: null diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/text/dinotxt_model.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/text/dinotxt_model.py deleted file mode 100644 index 1056dd09c4f890d3f780cf9725f39db49764a228..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/text/dinotxt_model.py +++ /dev/null @@ -1,138 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import math -from dataclasses import dataclass -from typing import Optional, Tuple - -import torch -import torch.nn.functional as F -from torch import nn - -from dinov3.eval.text.text_tower import build_text_model -from dinov3.eval.text.vision_tower import build_vision_model - - -@dataclass -class DINOTxtConfig: - embed_dim: int - vision_backbone_config: str | None = None - text_backbone_config: str | None = None - vision_backbone_pretrained_weights: str | None = None - text_backbone_pretrained_weights: str | None = None - vision_model_freeze_backbone: bool = True - vision_model_train_img_size: int = 224 - vision_model_use_class_token: bool = True - vision_model_use_patch_tokens: bool = False - vision_model_num_head_blocks: int = 0 - vision_model_head_blocks_drop_path: float = 0.3 - vision_model_use_linear_projection: bool = False - vision_model_patch_tokens_pooler_type: str = "mean" - vision_model_patch_token_layer: int = 1 # which layer to take patch tokens from - # 1 - last layer, 2 - second last layer, etc. - text_model_freeze_backbone: bool = False - text_model_num_head_blocks: int = 0 - text_model_head_blocks_is_causal: bool = False - text_model_head_blocks_drop_prob: float = 0.0 - text_model_tokens_pooler_type: str = "first" - text_model_use_linear_projection: bool = False - text_vocab_path_or_url: Optional[str] = None - init_logit_scale: float = math.log(1 / 0.07) - init_logit_bias: Optional[float] = None - freeze_logit_scale: bool = False - - -class DINOTxt(nn.Module): - def __init__( - self, - model_config: DINOTxtConfig, - vision_backbone: Optional[nn.Module] = None, - text_backbone: Optional[nn.Module] = None, - device=None, - ): - super().__init__() - self.model_config = model_config - self.visual_model = build_vision_model( - model_config.embed_dim, - model_config.vision_backbone_config, - model_config.vision_model_freeze_backbone, - model_config.vision_model_num_head_blocks, - model_config.vision_model_head_blocks_drop_path, - model_config.vision_model_use_class_token, - model_config.vision_model_use_patch_tokens, - model_config.vision_model_patch_token_layer, - model_config.vision_model_patch_tokens_pooler_type, - model_config.vision_model_use_linear_projection, - backbone=vision_backbone, - ) - self.text_model = build_text_model( - model_config.embed_dim, - model_config.text_backbone_config, - model_config.text_model_freeze_backbone, - model_config.text_model_num_head_blocks, - model_config.text_model_head_blocks_is_causal, - model_config.text_model_head_blocks_drop_prob, - model_config.text_model_tokens_pooler_type, - model_config.text_model_use_linear_projection, - backbone=text_backbone, - ) - self.logit_scale = nn.Parameter(torch.empty(1, device=device)) - if model_config.freeze_logit_scale: - self.logit_scale.requires_grad = False - - def init_weights(self): - torch.nn.init.constant(self.logit_scale, self.model_config.init_logit_scale) - self.visual_model.init_weights() - self.text_model.init_weights() - - def encode_image_with_patch_tokens( - self, - image: torch.Tensor, - normalize: bool = False, - ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: - features, patch_tokens, backbone_patch_tokens = self.visual_model(image) - return ( - F.normalize(features, dim=-1) if normalize else features, - patch_tokens, - backbone_patch_tokens, - ) - - def encode_image( - self, - image: torch.Tensor, - normalize: bool = False, - ) -> torch.Tensor: - features, _, _ = self.visual_model(image) - return F.normalize(features, dim=-1) if normalize else features - - def encode_text(self, text: torch.Tensor, normalize: bool = False) -> torch.Tensor: - features = self.text_model(text) - return F.normalize(features, dim=-1) if normalize else features - - def get_logits( - self, image: torch.Tensor, text: torch.Tensor - ) -> Tuple[torch.Tensor, torch.Tensor]: - text_features = self.encode_text(text, normalize=True) - image_features = self.encode_image(image, normalize=True) - image_logits = self.logit_scale.exp() * image_features @ text_features.T - text_logits = image_logits.T - return image_logits, text_logits - - def forward( - self, - image: torch.Tensor, - text: torch.Tensor, - ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: - text_features = self.encode_text(text, normalize=True) - image_features, patch_tokens, backbone_patch_tokens = ( - self.encode_image_with_patch_tokens(image, normalize=True) - ) - return ( - image_features, - text_features, - self.logit_scale.exp(), - patch_tokens, - backbone_patch_tokens, - ) diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/text/gram_loss.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/text/gram_loss.py deleted file mode 100644 index 5cc80795bb6d9d28d826780b97fda5a9dc32dfa4..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/text/gram_loss.py +++ /dev/null @@ -1,26 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import torch -import torch.nn.functional as F - - -def gram_loss_fn( - backbone_patch_tokens: torch.Tensor, - patch_tokens: torch.Tensor, - patch_sampling_rate: float = 1.0, - normalize: bool = True, -) -> torch.Tensor: - num_patches, dim = patch_tokens.shape[1:] - idx = torch.randperm(num_patches)[: int(num_patches * patch_sampling_rate)] - patch_tokens = patch_tokens[:, idx, :] - backbone_patch_tokens = backbone_patch_tokens[:, idx, :] - if normalize: - patch_tokens = F.normalize(patch_tokens, dim=-1) - backbone_patch_tokens = F.normalize(backbone_patch_tokens, dim=-1) - return torch.nn.MSELoss()( - patch_tokens @ patch_tokens.transpose(-2, -1), - backbone_patch_tokens @ backbone_patch_tokens.transpose(-2, -1), - ) diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/text/text_tower.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/text/text_tower.py deleted file mode 100644 index 77e24781ada31e50f457292588ce28dd85a56d56..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/text/text_tower.py +++ /dev/null @@ -1,170 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import logging -from typing import Optional - -import torch - -from dinov3.eval.text.text_transformer import TextTransformer -from dinov3.layers import CausalSelfAttentionBlock -from torch import nn - -logger = logging.getLogger("dinov3") - - -class TextHead(nn.Module): - def __init__( - self, - input_dim: int, - embed_dim: int, - num_heads: int, - num_blocks: int, - block_drop_prob: float, - is_causal: bool, - use_linear_projection: bool, - ): - super().__init__() - block_list = [nn.Identity()] - self.ln_final = nn.Identity() - if num_blocks > 0: - logger.info(f"Adding {num_blocks} text tower transformer head blocks") - block_list = [ - CausalSelfAttentionBlock( - dim=input_dim, - num_heads=num_heads, - is_causal=is_causal, - dropout_prob=block_drop_prob, - ) - for _ in range(num_blocks) - ] - self.ln_final = nn.LayerNorm(input_dim) - self.blocks = nn.ModuleList(block_list) - self.num_blocks = num_blocks - self.linear_projection = nn.Identity() - if input_dim != embed_dim or use_linear_projection: - logger.info( - f"Text tower : Using a linear projection from {input_dim} to {embed_dim}" - ) - self.linear_projection = nn.Linear(input_dim, embed_dim, bias=False) - - def init_weights(self): - if self.num_blocks > 0: - for i in range(self.num_blocks): - self.blocks[i].init_weights() - self.ln_final.reset_parameters() - if isinstance(self.linear_projection, nn.Linear): - nn.init.normal_( - self.linear_projection.weight, - std=self.linear_projection.in_features**-0.5, - ) - - def forward(self, text_tokens: torch.Tensor) -> torch.Tensor: - for block in self.blocks: - text_tokens = block(text_tokens) - text_tokens = self.ln_final(text_tokens) - return self.linear_projection(text_tokens) - - -class TextTower(nn.Module): - def __init__( - self, - backbone: nn.Module, - freeze_backbone: bool, - embed_dim: int, - num_head_blocks: int, - head_blocks_is_causal: bool, - head_blocks_block_drop_prob: float, - tokens_pooler_type: str, - use_linear_projection: bool, - ): - super().__init__() - self.backbone = backbone - self.freeze_backbone = freeze_backbone - backbone_out_dim = backbone.embed_dim - logger.info(f"Text backbone embedding dimension: {backbone_out_dim}") - self.backbone = backbone - self.head = TextHead( - backbone_out_dim, - embed_dim, - self.backbone.num_heads, - num_head_blocks, - head_blocks_block_drop_prob, - head_blocks_is_causal, - use_linear_projection, - ) - self.tokens_pooler_type = tokens_pooler_type - - def init_weights(self): - self.backbone.init_weights() - self.head.init_weights() - - def forward(self, token_indices: torch.Tensor) -> torch.Tensor: - text_tokens = self.backbone(token_indices) - text_tokens = self.head(text_tokens) - if self.tokens_pooler_type == "first": - features = text_tokens[:, 0] - elif self.tokens_pooler_type == "last": - features = text_tokens[:, -1] - elif self.tokens_pooler_type == "argmax": - assert token_indices is not None - features = text_tokens[ - torch.arange(text_tokens.shape[0]), token_indices.argmax(dim=-1) - ] - else: - raise ValueError(f"Unknown text tokens pooler type: {self.pooler_type}") - return features - - -def build_text_backbone( - cfg, -) -> torch.nn.Module: - logger.info("Setting up a text transformer") - model = TextTransformer( - context_length=cfg.context_length, - vocab_size=cfg.vocab_size, - dim=cfg.dim, - num_heads=cfg.num_heads, - num_layers=cfg.num_layers, - ffn_ratio=cfg.ffn_ratio, - is_causal=cfg.is_causal, - ls_init_value=cfg.ls_init_value, - dropout_prob=cfg.dropout_prob, - ) - logger.info(f"Setting upa custom text transformer {cfg.model_name}") - return model - - -def build_text_model( - embed_dim: int, - backbone_model_config: str, - freeze_backbone: bool, - num_head_blocks: int, - head_blocks_is_causal: bool, - head_blocks_drop_prob: float, - tokens_pooler_type: str, - use_linear_projection: bool, - backbone: Optional[nn.Module] = None, -): - if backbone is None: - if backbone_model_config is not None: - from omegaconf import OmegaConf - - cfg = OmegaConf.load(backbone_model_config) - backbone = build_text_backbone(cfg) - else: - raise RuntimeError( - "Failed to create, text backbone, either backbone or backbone_model_config should be not None" - ) - return TextTower( - backbone, - freeze_backbone, - embed_dim, - num_head_blocks, - head_blocks_is_causal, - head_blocks_drop_prob, - tokens_pooler_type, - use_linear_projection, - ) diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/text/text_transformer.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/text/text_transformer.py deleted file mode 100644 index 66183365f8198bf2a1213330087d6f2417868a9a..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/text/text_transformer.py +++ /dev/null @@ -1,70 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -from typing import Callable, Optional, Tuple - -import torch -import torch.nn as nn -from dinov3.layers import CausalSelfAttentionBlock - - -class TextTransformer(nn.Module): - def __init__( - self, - context_length: int, - vocab_size: int, - dim: int, - num_heads: int, - num_layers: int, - ffn_ratio: float, - is_causal: bool, - ls_init_value: Optional[float] = None, - act_layer: Callable = nn.GELU, - norm_layer: Callable = nn.LayerNorm, - dropout_prob: float = 0.0, - ): - super().__init__() - self.vocab_size = vocab_size - self.embed_dim = dim - self.num_heads = num_heads - - self.token_embedding = nn.Embedding(vocab_size, dim) - self.positional_embedding = nn.Parameter(torch.empty(context_length, dim)) - self.dropout = nn.Dropout(dropout_prob) - self.num_layers = num_layers - block_list = [ - CausalSelfAttentionBlock( - dim=dim, - num_heads=num_heads, - ffn_ratio=ffn_ratio, - ls_init_value=ls_init_value, - is_causal=is_causal, - act_layer=act_layer, - norm_layer=norm_layer, - dropout_prob=dropout_prob, - ) - for _ in range(num_layers) - ] - self.blocks = nn.ModuleList(block_list) - self.ln_final = norm_layer(dim) - - def init_weights(self): - nn.init.normal_(self.token_embedding.weight, std=0.02) - nn.init.normal_(self.positional_embedding, std=0.01) - init_attn_std = self.embed_dim**-0.5 - init_proj_std = (self.embed_dim**-0.5) * ((2 * self.num_layers) ** -0.5) - init_fc_std = (2 * self.embed_dim) ** -0.5 - for block in self.blocks: - block.init_weights(init_attn_std, init_proj_std, init_fc_std) - self.ln_final.reset_parameters() - - def forward(self, token_indices: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: - _, N = token_indices.size() - x = self.token_embedding(token_indices) + self.positional_embedding[:N] - x = self.dropout(x) - for block in self.blocks: - x = block(x) - x = self.ln_final(x) - return x diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/text/tokenizer.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/text/tokenizer.py deleted file mode 100644 index 5834418f0c8220280d6059c89bb6ae55f5972789..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/text/tokenizer.py +++ /dev/null @@ -1,67 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -from typing import List, Union - -import torch -from dinov3.thirdparty.CLIP.clip.simple_tokenizer import SimpleTokenizer - - -class Tokenizer(SimpleTokenizer): - def __init__(self, vocab_path: str): - SimpleTokenizer.__init__(self, bpe_path=vocab_path) - - def tokenize( - self, texts: Union[str, List[str]], context_length: int = 77 - ) -> torch.LongTensor: - """ - Returns the tokenized representation of given input string(s) - - Parameters - ---------- - texts : Union[str, List[str]] - An input string or a list of input strings to tokenize - context_length : int - The context length to use; all CLIP models use 77 as the context length - - Returns - ------- - A two-dimensional tensor containing the resulting tokens, shape = [number of input strings, context_length] - """ - if isinstance(texts, str): - texts = [texts] - sot_token = self.encoder["<|startoftext|>"] - eot_token = self.encoder["<|endoftext|>"] - all_tokens = [[sot_token] + self.encode(text) + [eot_token] for text in texts] - result = torch.zeros(len(all_tokens), context_length, dtype=torch.long) - - for i, tokens in enumerate(all_tokens): - if len(tokens) > context_length: - tokens = tokens[:context_length] # Truncate - tokens[-1] = eot_token - result[i, : len(tokens)] = torch.tensor(tokens) - - return result - - -def get_tokenizer(bpe_path_or_url: str) -> Tokenizer | None: - import urllib - from io import BytesIO - - from .tokenizer import Tokenizer - - if urllib.parse.urlparse(bpe_path_or_url).scheme: - try: - with urllib.request.urlopen(bpe_path_or_url) as response: - file_buf = BytesIO(response.read()) - return Tokenizer(vocab_path=file_buf) - except Exception as e: - raise FileNotFoundError( - f"Failed to download file from url {bpe_path_or_url} with error last: {e}" - ) - else: - with open(bpe_path_or_url, "rb") as f: - file_buf = BytesIO(f.read()) - return Tokenizer(vocab_path=file_buf) diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/text/train_dinotxt.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/text/train_dinotxt.py deleted file mode 100644 index 295b04c475719c6a75ee260f0cd1bbcc00de42dd..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/text/train_dinotxt.py +++ /dev/null @@ -1,339 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import gc -import logging -from functools import partial -import math -import os -import sys -from pathlib import Path -from typing import Callable - -import dinov3.distributed as distributed -import torch -from dinov3.checkpointer import ( - find_latest_checkpoint, - keep_last_n_checkpoints, - load_checkpoint, - save_checkpoint, -) -from dinov3.configs import setup_job -from dinov3.data import SamplerType, make_data_loader, make_dataset -from dinov3.eval.text.build_dinotxt import build_model_and_tokenizer -from dinov3.eval.text.clip_loss import memory_efficient_clip_loss -from dinov3.eval.text.dinotxt_model import DINOTxt, DINOTxtConfig -from dinov3.eval.text.gram_loss import gram_loss_fn -from dinov3.logging import MetricLogger, setup_logging -from dinov3.train.cosine_lr_scheduler import linear_warmup_cosine_decay -from omegaconf import OmegaConf -from torch import optim - -logger = logging.getLogger("dinov3") - - -def unwrap_model(model): - return getattr(model, "module", model) - - -def test( - model: DINOTxt, - iteration: str, - output_dir: str, -): - eval_dir = Path(output_dir) / "eval" / str(iteration) - if distributed.is_subgroup_main_process(): - eval_dir.mkdir(parents=True, exist_ok=True) - - ckpt_dir = eval_dir / str("sharded_model_checkpoint") - save_checkpoint(ckpt_dir, iteration=iteration, model=model) - - -def apply_learning_rate(optimizer, lr: float): - for param_group in optimizer.param_groups: - param_group["lr"] = lr - - -def exclude(n: str, p: torch.Tensor) -> bool: - return p.ndim < 2 or "bn" in n or "ln" in n or "bias" in n or "logit_scale" in n - - -def include(n: str, p: torch.Tensor) -> bool: - return not exclude(n, p) - - -def train( - train_dataset, - model: DINOTxt, - tokenizer: Callable, - max_iteration: int, - warmup_length: int, - checkpointing_period: int, - output_dir: str, - dtype_str: str, - sampler_type: SamplerType, - lr_scheduler_type: str, - lr: float, - weight_decay: float, - batch_size: int = 256, - beta1: float = 0.9, - beta2: float = 0.99, - eps: float = 1e-8, - num_workers: int = 10, - eval_freq: int = 1000, - gc_freq: int = 100, - use_gram_loss: bool = False, - patch_sampling_rate_for_gram_loss: float = 0.5, - normalize_patch_tokens_for_gram_loss: bool = False, - gram_loss_weight: float = 1.0, - max_checkpoints_to_keep: int = None, - resume: bool = False, - seed: int = 11, -): - named_parameters = list(model.named_parameters()) - gain_or_bias_params = [ - p for n, p in named_parameters if exclude(n, p) and p.requires_grad - ] - gain_or_bias_params_names = [ - n for n, p in named_parameters if exclude(n, p) and p.requires_grad - ] - rest_params = [p for n, p in named_parameters if include(n, p) and p.requires_grad] - rest_params_names = [ - n for n, p in named_parameters if include(n, p) and p.requires_grad - ] - logger.info(f"Gain or bias params: {gain_or_bias_params_names}") - logger.info(f"Rest params: {rest_params_names}") - logger.info( - f"Learning rate: {lr}, batch_size_per_gpu: {batch_size}, weight_decay: {weight_decay}" - ) - optimizer = optim.AdamW( - [ - {"params": gain_or_bias_params, "weight_decay": 0.0}, - {"params": rest_params, "weight_decay": weight_decay}, - ], - lr=lr, - betas=(beta1, beta2), - eps=eps, - ) - learning_rates = linear_warmup_cosine_decay( - 0, lr, 0, warmup_iterations=warmup_length, total_iterations=max_iteration - ) - logger.info( - f"Init lr scheduler: {lr_scheduler_type}, warmup length: {warmup_length}, base_lr: {lr}, max iter: {max_iteration}" - ) - - if ( - resume - and (ckpt_dir := find_latest_checkpoint(os.path.join(output_dir, "ckpt"))) - is not None - ): - iteration = load_checkpoint(ckpt_dir, model=model, optimizer=optimizer) - start_iteration = iteration + 1 - del iteration, ckpt_dir - else: - logger.info("Initializing from scratch") - start_iteration = 0 - - def collate_fn(batch): - images, captions = list(zip(*batch))[:2] - return torch.stack(images), tokenizer.tokenize(captions) - - train_data_loader = make_data_loader( - dataset=train_dataset, - batch_size=batch_size, - num_workers=num_workers, - shuffle=False, - seed=seed, - sampler_type=sampler_type, - sampler_advance=start_iteration, - drop_last=True, - persistent_workers=True, - collate_fn=collate_fn, - ) - rank = distributed.get_rank() - world_size = distributed.get_world_size() - logger.info( - f"Init loss function: rank: {distributed.get_rank()}, world size: {world_size}" - ) - clip_loss = partial(memory_efficient_clip_loss, group=torch.distributed.group.WORLD) - cur_iteration = start_iteration - logger.info(f"Starting training from iteration {start_iteration}...") - header = "Training" - metric_logger = MetricLogger(delimiter=" ") - gc.disable() - device_id = rank % torch.cuda.device_count() - - for batch in metric_logger.log_every( - train_data_loader, - 10, - header, - max_iteration, - start_iteration, - ): - images, text_tokens = batch - images = images.to(device=device_id, non_blocking=True) - text_tokens = text_tokens.to(device=device_id, non_blocking=True) - ( - image_embeddings, - text_embeddings, - logit_scale, - patch_tokens, - backbone_patch_tokens, - ) = model(images, text_tokens) - contrastive_loss = clip_loss(image_embeddings, text_embeddings, logit_scale) - total_loss = contrastive_loss - if use_gram_loss: - gram_loss = gram_loss_fn( - patch_tokens, - backbone_patch_tokens, - patch_sampling_rate_for_gram_loss, - normalize_patch_tokens_for_gram_loss, - ) - total_loss = contrastive_loss + gram_loss_weight * gram_loss - - if total_loss.isnan(): - msg = f"Loss is NaN at iteration {cur_iteration}, aborting..." - logger.error(msg) - raise RuntimeError(msg) - apply_learning_rate(optimizer=optimizer, lr=learning_rates[cur_iteration]) - optimizer.zero_grad() - total_loss.backward() - optimizer.step() - - # This clamping trick is from OpenCLIP reposistory which MetaCLIP follows. Orginally used in CLIP training. - # NOTE: we clamp to 4.6052 = ln(100), as in the original paper. - with torch.no_grad(): - unwrap_model(model).logit_scale.clamp_(0, math.log(100)) - metric_logger.update(contrastive_loss=contrastive_loss.item()) - if use_gram_loss: - metric_logger.update(gram_loss=gram_loss.item()) - metric_logger.update(lr=optimizer.param_groups[0]["lr"]) - metric_logger.update(logit_scale=logit_scale.item()) - is_last_iteration = (cur_iteration + 1) == max_iteration - is_ckpt_iteration = ( - (cur_iteration + 1) % checkpointing_period == 0 - ) or is_last_iteration - if is_ckpt_iteration: - ckpt_dir = Path(output_dir, "ckpt").expanduser() - save_checkpoint( - ckpt_dir / str(cur_iteration), - iteration=cur_iteration, - model=model, - optimizer=optimizer, - ) - if distributed.is_main_process(): - keep_last_n_checkpoints(ckpt_dir, max_checkpoints_to_keep) - if eval_freq > 0 and (cur_iteration + 1) % eval_freq == 0: - test( - model, - iteration=f"training_{cur_iteration}", - batch_size=batch_size, - num_workers=num_workers, - output_dir=output_dir, - dtype_str=dtype_str, - ) - torch.cuda.synchronize() - if (cur_iteration + 1) % gc_freq == 0: - logger.info("Garbage collection...") - gc.collect() - cur_iteration += 1 - - -def write_config( - model_config: DINOTxtConfig, output_dir, name="clip_model_config.yaml" -): - logger.info(OmegaConf.to_yaml(model_config)) - saved_cfg_path = os.path.join(output_dir, name) - with open(saved_cfg_path, "w") as f: - OmegaConf.save(config=model_config, f=f) - return saved_cfg_path - - -def main(argv=None): - if argv is None: - argv = sys.argv[1:] - args_dict = OmegaConf.to_container(OmegaConf.from_cli(argv)) - logger.info(args_dict) - config = OmegaConf.load(args_dict["trainer_config_file"]) - logger.info(config) - if "output_dir" in args_dict: - config.output_dir = args_dict["--output-dir"] - setup_job(output_dir=config.output_dir, seed=config.seed) - setup_logging(output=os.path.join(config.output_dir, "nan_logs"), name="nan_logger") - logger.info("Trainer config:") - logger.info(config) - model_config = DINOTxtConfig( - embed_dim=config.embed_dim, - text_backbone_config=config.text_backbone_config, - vision_backbone_config=config.vision_backbone_config, - text_backbone_pretrained_weights=config.text_backbone_pretrained_weights, - vision_backbone_pretrained_weights=config.vision_backbone_pretrained_weights, - vision_model_train_img_size=config.vision_model_train_img_size, - vision_model_use_class_token=config.vision_model_use_class_token, - vision_model_use_patch_tokens=config.vision_model_use_patch_tokens, - vision_model_num_head_blocks=config.vision_model_num_head_blocks, - vision_model_head_blocks_drop_path=config.vision_model_head_blocks_drop_path, - vision_model_use_linear_projection=config.vision_model_use_linear_projection, - vision_model_patch_tokens_pooler_type=config.vision_model_patch_tokens_pooler_type, - vision_model_patch_token_layer=config.vision_model_patch_token_layer, - text_model_freeze_backbone=config.text_model_freeze_backbone, - text_model_num_head_blocks=config.text_model_num_head_blocks, - text_model_head_blocks_is_causal=config.text_model_head_blocks_is_causal, - text_model_head_blocks_drop_prob=config.text_model_head_blocks_drop_prob, - text_model_tokens_pooler_type=config.text_model_tokens_pooler_type, - text_model_use_linear_projection=config.text_model_use_linear_projection, - text_vocab_path_or_url=config.text_vocab_path_or_url, - init_logit_scale=config.init_logit_scale, - freeze_logit_scale=config.freeze_logit_scale, - init_logit_bias=config.init_logit_bias, - ) - write_config(model_config=model_config, output_dir=config.output_dir) - model, transform, tokenizer = build_model_and_tokenizer( - model_config, - use_fsdp=config.use_fsdp, - do_compile=config.do_compile, - use_ac=config.use_ac, - use_cuda_graphs=config.use_cuda_graphs, - ) - - train_dataset = make_dataset( - dataset_str=config.train_dataset_str, - transform=transform, - ) - sampler_type = ( - SamplerType.SHARDED_INFINITE - if config.dataset_use_cache - else SamplerType.INFINITE - ) - train( - train_dataset=train_dataset, - model=model, - tokenizer=tokenizer, - max_iteration=config.max_iteration, - warmup_length=config.warmup_length, - checkpointing_period=config.checkpointing_period, - output_dir=config.output_dir, - dtype_str=config.dtype_str, - lr_scheduler_type=config.lr_scheduler_type, - lr=config.lr, - weight_decay=config.weight_decay, - batch_size=config.batch_size, - beta1=config.beta1, - beta2=config.beta2, - eps=config.eps, - sampler_type=sampler_type, - eval_freq=config.eval_freq, - gc_freq=config.gc_freq, - max_checkpoints_to_keep=config.max_checkpoints_to_keep, - use_gram_loss=config.vision_model_use_gram_loss, - patch_sampling_rate_for_gram_loss=config.vision_model_patch_sampling_rate_for_gram_loss, - normalize_patch_tokens_for_gram_loss=config.vision_model_normalize_patch_tokens_for_gram_loss, - gram_loss_weight=config.vision_model_gram_loss_weight, - resume=not config.no_resume, - ) - - -if __name__ == "__main__": - main() diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/text/vision_tower.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/text/vision_tower.py deleted file mode 100644 index b9b8b389ce8675f5d598921e535b92f82c5c4399..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/text/vision_tower.py +++ /dev/null @@ -1,207 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import logging -from functools import partial -from typing import Optional, Tuple - -import torch -from torch import nn - -from dinov3.layers import SelfAttentionBlock, SwiGLUFFN -from dinov3.models.vision_transformer import init_weights_vit -from dinov3.utils import named_apply - -logger = logging.getLogger("dinov3") - - -class VisionHead(nn.Module): - def __init__( - self, - input_dim: int, - embed_dim: int, - num_heads: int, - num_blocks: int, - blocks_drop_path: float, - use_class_token: bool, - use_patch_tokens: bool, - use_linear_projection: bool, - ): - super().__init__() - block_list = [nn.Identity()] - self.ln_final = nn.Identity() - if num_blocks > 0: - block_list = [ - SelfAttentionBlock( - input_dim, - num_heads, - ffn_layer=partial(SwiGLUFFN, align_to=64), - init_values=1e-5, - drop_path=blocks_drop_path, - ) - for _ in range(num_blocks) - ] - self.ln_final = nn.LayerNorm(input_dim) - self.blocks = nn.ModuleList(block_list) - self.num_blocks = num_blocks - multiplier = 2 if use_class_token and use_patch_tokens else 1 - self.linear_projection = nn.Identity() - if multiplier * input_dim != embed_dim or use_linear_projection: - logger.info( - f"Vision Tower: Using a linear projection from {input_dim} to {embed_dim}" - ) - assert embed_dim % multiplier == 0, ( - f"Expects {embed_dim} to be divisible by {multiplier}" - ) - self.linear_projection = nn.Linear( - input_dim, embed_dim // multiplier, bias=False - ) - - def init_weights(self): - if self.num_blocks > 0: - for i in range(self.num_blocks): - block = self.blocks[i] - named_apply(init_weights_vit, block) - self.ln_final.reset_parameters() - if isinstance(self.linear_projection, nn.Linear): - nn.init.normal_( - self.linear_projection.weight, - std=self.linear_projection.in_features**-0.5, - ) - - def forward(self, image_tokens: torch.Tensor) -> torch.Tensor: - # FIXME(cijose) ROPE embeddings are not used in DINOv2, refactor to use it in the future - for block in self.blocks: - image_tokens = block(image_tokens) - image_tokens = self.ln_final(image_tokens) - return self.linear_projection(image_tokens) - - -class VisionTower(nn.Module): - def __init__( - self, - backbone: nn.Module, - freeze_backbone: bool, - embed_dim: int, - num_head_blocks: int, - head_blocks_block_drop_path: float, - use_class_token: bool, - use_patch_tokens: bool, - patch_token_layer: int, - patch_tokens_pooler_type: str, - use_linear_projection: bool, - ): - super().__init__() - self.backbone = backbone - self.freeze_backbone = freeze_backbone - self.use_class_token = use_class_token - self.use_patch_tokens = use_patch_tokens - self.patch_token_layer = patch_token_layer - self.patch_tokens_pooler_type = patch_tokens_pooler_type - self.num_register_tokens = 0 - if hasattr(self.backbone, "num_register_tokens"): - self.num_register_tokens = self.backbone.num_register_tokens - elif hasattr(self.backbone, "n_storage_tokens"): - self.num_register_tokens = self.backbone.n_storage_tokens - backbone_out_dim = self.backbone.embed_dim - logger.info(f"Visual backbone embedding dimension: {backbone_out_dim}") - self.head = VisionHead( - backbone_out_dim, - embed_dim, - self.backbone.num_heads, - num_head_blocks, - head_blocks_block_drop_path, - use_class_token, - use_patch_tokens, - use_linear_projection, - ) - - def init_weights(self): - self.backbone.init_weights() - self.head.init_weights() - - def get_backbone_features( - self, images: torch.Tensor - ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: - tokens = self.backbone.get_intermediate_layers( - images, - n=self.patch_token_layer, - return_class_token=True, - return_extra_tokens=True, - ) - class_token = tokens[-1][1] - patch_tokens = tokens[0][0] - register_tokens = tokens[0][2] - return class_token, patch_tokens, register_tokens - - def get_class_and_patch_tokens( - self, images: torch.Tensor - ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: - class_token, patch_tokens, register_tokens = self.get_backbone_features(images) - image_tokens = self.head( - torch.cat([class_token.unsqueeze(1), register_tokens, patch_tokens], dim=1) - ) - return ( - image_tokens[:, 0], - image_tokens[:, self.num_register_tokens + 1 :], - patch_tokens, - ) - - def forward(self, images: torch.Tensor) -> torch.Tensor: - class_token, patch_tokens, backbone_patch_tokens = ( - self.get_class_and_patch_tokens(images) - ) - features = [] - if self.use_class_token: - features.append(class_token) - if self.use_patch_tokens: - if self.patch_tokens_pooler_type == "mean": - features.append(torch.mean(patch_tokens, dim=1)) - elif self.patch_tokens_pooler_type == "max": - features.append(torch.max(patch_tokens, dim=1).values) - else: - raise ValueError( - f"Unknown patch tokens pooler type: {self.patch_tokens_pooler_type}" - ) - return torch.cat(features, dim=-1), patch_tokens, backbone_patch_tokens - - -def build_vision_model( - embed_dim: int, - backbone_model_config: str, - freeze_backbone: bool, - num_head_blocks: int, - blocks_drop_path: float, - use_class_token: bool, - use_patch_tokens: bool, - patch_token_layer: int, - patch_tokens_pooler_type: str, - use_linear_projection: bool, - backbone: Optional[nn.Module] = None, -): - if backbone is None: - if backbone_model_config is not None: - from omegaconf import OmegaConf - - from dinov3.models import build_model_from_cfg as build_vision_backbone - - cfg = OmegaConf.load(backbone_model_config) - backbone, _ = build_vision_backbone(cfg, only_teacher=True) - else: - raise RuntimeError( - "Failed to create, vision backbone, either backbone or backbone_model_config should be not None" - ) - return VisionTower( - backbone, - freeze_backbone, - embed_dim, - num_head_blocks, - blocks_drop_path, - use_class_token, - use_patch_tokens, - patch_token_layer, - patch_tokens_pooler_type, - use_linear_projection, - ) diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/utils.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/utils.py deleted file mode 100644 index c72096a22f896b443f072f84ef1d01a1b19e09fd..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/eval/utils.py +++ /dev/null @@ -1,286 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import gc -import logging -import os -from enum import Enum -from typing import Any, Dict, List, Optional - -import numpy as np -import torch -from torch import nn -from torchmetrics import Metric - -import dinov3.distributed as distributed -from dinov3.data import DatasetWithEnumeratedTargets, SamplerType, make_data_loader -from dinov3.eval.accumulators import NoOpAccumulator, ResultsAccumulator -from dinov3.logging import MetricLogger - -logger = logging.getLogger("dinov3") - - -class LossType(Enum): - CROSS_ENTROPY = "cross_entropy" - BINARY_CROSS_ENTROPY = "binary_cross_entropy" - - -class ModelWithNormalize(torch.nn.Module): - def __init__(self, model: torch.nn.Module) -> None: - super().__init__() - self._model = model - - def forward(self, samples): - return nn.functional.normalize(self._model(samples), dim=1, p=2) - - -class ModelWithMultiScale(torch.nn.Module): - def __init__(self, model: torch.nn.Module, mode: str = "bilinear") -> None: - super().__init__() - self._model = model - self._mode = mode - - def forward(self, samples): - output = None - for scale in (1, 0.5**0.5, 0.5): - if scale == 1: - resized_samples = samples.clone() - else: - resized_samples = nn.functional.interpolate( - samples, scale_factor=scale, mode=self._mode, align_corners=False - ) - scale_output = self._model(resized_samples).clone() - if output is None: - output = scale_output - else: - output += scale_output - return output / 3 - - -def wrap_model( - model: nn.Module, - *, - normalize: bool = True, - multi_scale: bool = False, -) -> nn.Module: - logger.info("multi-scale: {}".format("enabled" if multi_scale else "disabled")) - if multi_scale: - model = ModelWithMultiScale(model) - - logger.info("normalize: {}".format("enabled" if normalize else "disabled")) - if normalize: - model = ModelWithNormalize(model) - return model - - -class ModelWithIntermediateLayers(nn.Module): - def __init__(self, feature_model, n, autocast_ctx, reshape=False, return_class_token=True): - super().__init__() - self.feature_model = feature_model - self.feature_model.eval() - self.n = n # Layer indices (Sequence) or n last layers (int) to take - self.autocast_ctx = autocast_ctx - self.reshape = reshape - self.return_class_token = return_class_token - - def forward(self, images): - with torch.inference_mode(): - with self.autocast_ctx(): - features = self.feature_model.get_intermediate_layers( - images, - n=self.n, - reshape=self.reshape, - return_class_token=self.return_class_token - ) - return features - - -@torch.inference_mode() -def evaluate( - model: nn.Module, - data_loader, - postprocessors: Dict[str, nn.Module], - metrics: Dict[str, Metric], - device: torch.device, - criterion: Optional[nn.Module] = None, - accumulate_results: bool = False, -): - gc.collect() # Avoids garbage collection errors in DataLoader workers - model.eval() - if criterion is not None: - criterion.eval() - - for metric in metrics.values(): - metric = metric.to(device) - - metric_logger = MetricLogger(delimiter=" ") - header = "Test:" - - accumulator_class = ResultsAccumulator if accumulate_results else NoOpAccumulator - accumulators = {k: accumulator_class() for k in postprocessors.keys()} - - # Dataset needs to be wrapped in fairvit.data.adapters.DatasetWithEnumeratedTargets - for samples, (index, targets), *_ in metric_logger.log_every(data_loader, 10, header): - samples, targets, index = samples[index >= 0], targets[index >= 0], index[index >= 0] - if len(index) == 0: - continue - - outputs = model(samples.to(device)) - index = index.to(device) - targets = targets.to(device) - - if criterion is not None: - loss = criterion(outputs, targets) - metric_logger.update(loss=loss.item()) - - for k, metric in metrics.items(): - metric_inputs = postprocessors[k](outputs, targets) - metric.update(**metric_inputs) - accumulators[k].update(preds=metric_inputs["preds"], target=metric_inputs["target"], index=index) - - metric_logger.synchronize_between_processes() - logger.info(f"Averaged stats: {metric_logger}") - stats = {k: metric.compute() for k, metric in metrics.items()} - metric_logger_stats = {k: meter.global_avg for k, meter in metric_logger.meters.items()} - - # accumulator.accumulate() returns None for the NoOpAccumulator - accumulated_results = {k: accumulator.accumulate() for k, accumulator in accumulators.items()} - - return metric_logger_stats, stats, accumulated_results - - -def all_gather_and_flatten(tensor_rank): - tensor_all_ranks = torch.empty( - distributed.get_world_size(), - *tensor_rank.shape, - dtype=tensor_rank.dtype, - device=tensor_rank.device, - ) - tensor_list = list(tensor_all_ranks.unbind(0)) - torch.distributed.all_gather(tensor_list, tensor_rank.contiguous()) - return tensor_all_ranks.flatten(end_dim=1) - - -def extract_features(model, dataset, batch_size, num_workers, gather_on_cpu=False): - dataset_with_enumerated_targets = DatasetWithEnumeratedTargets(dataset) - sample_count = len(dataset_with_enumerated_targets) - data_loader = make_data_loader( - dataset=dataset_with_enumerated_targets, - batch_size=batch_size, - num_workers=num_workers, - sampler_type=SamplerType.DISTRIBUTED, - drop_last=False, - shuffle=False, - ) - return extract_features_with_dataloader(model, data_loader, sample_count, gather_on_cpu) - - -@torch.inference_mode() -def extract_features_with_dataloader(model, data_loader, sample_count, gather_on_cpu=False): - gather_device = torch.device("cpu") if gather_on_cpu else torch.device("cuda") - metric_logger = MetricLogger(delimiter=" ") - features, all_labels = None, None - for samples, (index, labels_rank) in metric_logger.log_every(data_loader, 10): - samples = samples.cuda(non_blocking=True) - labels_rank = labels_rank.cuda(non_blocking=True) - index = index.cuda(non_blocking=True) - features_rank = model(samples).float() - - # init storage feature matrix - if features is None: - features = torch.zeros(sample_count, features_rank.shape[-1], device=gather_device) - labels_shape = list(labels_rank.shape) - labels_shape[0] = sample_count - all_labels = torch.full(labels_shape, fill_value=-1, device=gather_device) - logger.info(f"Storing features into tensor of shape {features.shape}") - - # share indexes, features and labels between processes - index_all = all_gather_and_flatten(index).to(gather_device) - features_all_ranks = all_gather_and_flatten(features_rank).to(gather_device) - labels_all_ranks = all_gather_and_flatten(labels_rank).to(gather_device) - - # update storage feature matrix - if len(index_all) > 0: - features.index_copy_(0, index_all, features_all_ranks) - all_labels.index_copy_(0, index_all, labels_all_ranks) - - logger.info(f"Features Shape {features.shape}") - logger.info(f"Labels Shape {all_labels.shape}") - - return features, all_labels - - -def save_features_dict(features_dict: Dict[str, torch.Tensor], path: str) -> None: - logger.info(f'saving features to "{path}"') - - for key, value in features_dict.items(): - assert isinstance(key, str) - assert isinstance(value, torch.Tensor) - - _, ext = os.path.splitext(path) - if ext == ".pt": - torch.save(features_dict, path) - elif ext == ".npy": - numpy_features_dict = { # Convert to NumPy arrays (if possible) - key: value.cpu().numpy() for key, value in features_dict.items() - } - np.save(path, numpy_features_dict, allow_pickle=True) - else: - raise ValueError(f'Unsupported features dict extension "{ext}"') - - -def load_features_dict(path: str) -> Dict[str, torch.Tensor]: - logger.info(f'loading features from "{path}"') - - _, ext = os.path.splitext(path) - if ext == ".pt": - features_dict = torch.load(path) - elif ext == ".npy": - numpy_features_dict = np.load(path, allow_pickle=True).item() - features_dict = {key: torch.from_numpy(value) for key, value in numpy_features_dict.items()} - else: - raise ValueError(f'Unsupported features dict extension "{ext}"') - - for key, value in features_dict.items(): - assert isinstance(key, str) - assert isinstance(value, torch.Tensor) - - return features_dict - - -def average_metrics(eval_metrics_dict: dict[Any, dict[str, torch.Tensor]], ignore_keys: List[str] = []): - """ - Function that computes the average and the std on a metrics dict. - A linear evaluation dictionary contains "best_classifier", - so this specific key is removed for computing aggregated metrics. - """ - output_metrics_dict = {} - metrics = [metric for metric in eval_metrics_dict[0].keys() if metric not in ignore_keys] - for metric in metrics: - stats_tensor = torch.tensor([stat[metric] for stat in eval_metrics_dict.values()]) - output_metrics_dict[metric + "_mean"] = stats_tensor.mean().item() - output_metrics_dict[metric + "_std"] = torch.std(stats_tensor).item() - - return output_metrics_dict - - -def save_results( - preds: torch.Tensor, - target: torch.Tensor, - output_dir: str, - filename_suffix: Optional[str] = None, -) -> None: - """ - Helper to save predictions from a model and their associated targets, aligned by their index - """ - filename_suffix = "" if filename_suffix is None else f"_{filename_suffix}" - preds_filename = f"preds{filename_suffix}.npy" - target_filename = f"target{filename_suffix}.npy" - preds_path = os.path.join(output_dir, preds_filename) - target_path = os.path.join(output_dir, target_filename) - logger.info(f"Saving to {preds_path}") - np.save(preds_path, preds.cpu().numpy()) - logger.info(f"Saving to {target_path}") - np.save(target_path, target.cpu().numpy()) diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/fsdp/ac_compile_parallelize.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/fsdp/ac_compile_parallelize.py deleted file mode 100644 index 1644f98593a225e6c91a4768e0c9245bf24c9638..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/fsdp/ac_compile_parallelize.py +++ /dev/null @@ -1,165 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import logging -from functools import partial -from typing import Any, List, Optional - -import torch -import torch.distributed as dist -import torch.nn as nn -from torch.distributed._composable.fsdp import MixedPrecisionPolicy, fully_shard -from torch.distributed.device_mesh import DeviceMesh, init_device_mesh -from torch.distributed.fsdp import register_fsdp_forward_method -from torch.distributed.fsdp._fully_shard._fsdp_state import FSDPState -from torch.utils.checkpoint import create_selective_checkpoint_contexts - -from dinov3.utils import utils - -logger = logging.getLogger("dinov3") - - -def map_modules_and_blocks(models: list[nn.ModuleDict], callable) -> None: - for m in models: - assert isinstance(m, nn.ModuleDict) - for k in m.keys(): - if k == "backbone": - assert isinstance(m[k].blocks, nn.ModuleList) - for block_id, block in enumerate(m[k].blocks): - m[k].blocks[block_id] = callable(block, is_backbone_block=True) - else: - m[k] = callable(m[k], is_backbone_block=False) - - -def ac_compile_parallelize( - trained_model: nn.ModuleDict, - inference_only_models: List[nn.ModuleDict], - cfg: Any, - trained_model_process_group: Optional[dist.ProcessGroup] = None, - inference_only_models_process_groups: Optional[List[dist.ProcessGroup]] = None, -) -> None: - """ - Order of the wrappers: - 1/ Activation checkpointing on blocks - 2/ Compile blocks - 3/ FSDP blocks + global model - """ - assert ( - isinstance(trained_model, nn.ModuleDict) and "backbone" in trained_model.keys() - ), f"{trained_model} does not contain a backbone?" - logger.info("DISTRIBUTED FSDP -- preparing model for distributed training") - if utils.has_batchnorms(trained_model): - raise NotImplementedError - - # 1/ AC on blocks - from torch.distributed.algorithms._checkpoint.checkpoint_wrapper import checkpoint_wrapper - - backbone = trained_model.backbone - if cfg.train.checkpointing: - if cfg.train.checkpointing_full: - _checkpointing_wrapper = checkpoint_wrapper - logger.info("using selective checkpointing on backbone with full checkpointing policy") - else: - _save_list = [ - # mm - torch.ops.aten.mm.default, - torch.ops.aten._scaled_mm.default, - # attentions - torch.ops.aten._scaled_dot_product_efficient_attention.default, - torch.ops.aten._scaled_dot_product_flash_attention.default, - torch.ops._c10d_functional.reduce_scatter_tensor.default, - ] - _checkpointing_wrapper = partial( - checkpoint_wrapper, - context_fn=partial(create_selective_checkpoint_contexts, _save_list), - preserve_rng_state=True, - ) - logger.info("using selective checkpointing on backbone with selective policy") - for i, b in enumerate(backbone.blocks): - backbone.blocks[i] = _checkpointing_wrapper(b) - - # 2/ Compile blocks - all_models = [trained_model] + inference_only_models - if trained_model_process_group is None and inference_only_models_process_groups is None: - all_pgs = [None] * len(all_models) - elif trained_model_process_group is None: - all_pgs = [None] + inference_only_models_process_groups - elif inference_only_models_process_groups is None: - all_pgs = [trained_model_process_group] + [None] * len(inference_only_models_process_groups) - else: - all_pgs = [trained_model_process_group] + inference_only_models_process_groups - - def wrap_compile_block(m: nn.Module, is_backbone_block: bool) -> nn.Module: - if cfg.train.compile: - if is_backbone_block and cfg.train.cudagraphs: - m.compile(fullgraph=True, dynamic=False, options={"triton.cudagraphs": True}) - else: - m.compile() - return m - - map_modules_and_blocks(all_models, wrap_compile_block) - - # 3/ Wrap submodules with FSDP - world_mesh = init_device_mesh( - "cuda", - mesh_shape=(dist.get_world_size(),), - mesh_dim_names=("dp",), - ) - DTYPE_MAP = { - "fp16": torch.float16, - "bf16": torch.bfloat16, - "fp32": torch.float32, - } - mp_policy = MixedPrecisionPolicy( - param_dtype=DTYPE_MAP[cfg.compute_precision.param_dtype], - reduce_dtype=DTYPE_MAP[cfg.compute_precision.reduce_dtype], - ) - - for m, pg in zip(all_models, all_pgs): - if pg is None: - world_mesh = init_device_mesh( - "cuda", - mesh_shape=(dist.get_world_size(),), - mesh_dim_names=("dp",), - ) - else: - world_mesh = DeviceMesh.from_group(pg, "cuda") - fsdp_config = {"mesh": world_mesh, "mp_policy": mp_policy} - for k in m.keys(): - if k != "backbone": - m[k] = fully_shard(m[k], **fsdp_config, reshard_after_forward=True) - m[k].set_reduce_scatter_divide_factor(1) - continue - # Backbone - FSDP every block - blocks = m[k].blocks - - assert isinstance(blocks, nn.ModuleList) - for block_id, block in enumerate(blocks): - block_reshard: int | bool = True - # if m is trained_model and dist.get_world_size() % 8 == 0 and dist.get_world_size() > 8: - # block_reshard = 8 - blocks[block_id] = fully_shard(block, **fsdp_config, reshard_after_forward=block_reshard) - blocks[block_id].set_reduce_scatter_divide_factor(1) - prev_block: FSDPState - next_block: FSDPState - for prev_block, next_block in zip(blocks, blocks[1:]): - prev_block.set_modules_to_forward_prefetch([next_block]) - next_block.set_modules_to_backward_prefetch([prev_block]) - fully_shard(m.backbone, **fsdp_config, reshard_after_forward=True).set_reduce_scatter_divide_factor(1) - register_fsdp_forward_method(m.backbone, "get_intermediate_layers") - - # 4/ Move to `cuda` device - for model in all_models: - model.to_empty(device="cuda") - - # 5/ FSDP2: Reshard immediately after forward for inference-only models - for model in inference_only_models: 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b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/hub/backbones.py deleted file mode 100644 index 1a2a9b2c832d5b13343eaad9d418176a73f63e71..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/hub/backbones.py +++ /dev/null @@ -1,614 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import os -from enum import Enum -from typing import List, Optional, Union -from urllib.parse import urlparse -from pathlib import Path - -import torch - -from .utils import DINOV3_BASE_URL - - -class Weights(Enum): - LVD1689M = "LVD1689M" - SAT493M = "SAT493M" - - -def is_url(path: str) -> bool: - parsed = urlparse(path) - return parsed.scheme in ("https", "file") - - -def convert_path_or_url_to_url(path: str) -> str: - if is_url(path): - return path - return Path(path).expanduser().resolve().as_uri() - - -def _make_dinov3_vit_model_arch( - *, - patch_size: int = 16, - compact_arch_name: str = "vitb", -): - if "plus" in compact_arch_name: - model_arch = compact_arch_name.replace("plus", f"{patch_size}plus") - else: - model_arch = f"{compact_arch_name}{patch_size}" - return model_arch - - -def _make_dinov3_vit_model_url( - *, - patch_size: int = 16, - compact_arch_name: str = "vitb", - version: Optional[str] = None, - weights: Union[Weights, str] = Weights.LVD1689M, - hash: Optional[str] = None, -): - model_name = "dinov3" - model_arch = _make_dinov3_vit_model_arch(patch_size=patch_size, compact_arch_name=compact_arch_name) - version_suffix = f"_{version}" if version else "" - weights_name = weights.value.lower() - hash_suffix = f"-{hash}" if hash else "" - model_dir = f"{model_name}_{model_arch}" - model_filename = f"{model_name}_{model_arch}_pretrain_{weights_name}{version_suffix}{hash_suffix}.pth" - return os.path.join(DINOV3_BASE_URL, model_dir, model_filename) - - -def _make_dinov3_vit( - *, - img_size: int = 224, - patch_size: int = 16, - in_chans: int = 3, - compact_arch_name: str = "vitb", - pos_embed_rope_base: float = 100.0, - pos_embed_rope_min_period: float | None = None, - pos_embed_rope_max_period: float | None = None, - pos_embed_rope_normalize_coords: str = "separate", - pos_embed_rope_shift_coords: float | None = None, - pos_embed_rope_jitter_coords: float | None = None, - pos_embed_rope_rescale_coords: float | None = None, - pos_embed_rope_dtype: str = "fp32", - embed_dim: int = 768, - depth: int = 12, - num_heads: int = 12, - ffn_ratio: float = 4.0, - qkv_bias: bool = True, - drop_path_rate: float = 0.0, - layerscale_init: float | None = None, - norm_layer: str = "layernorm", - ffn_layer: str = "mlp", - ffn_bias: bool = True, - proj_bias: bool = True, - n_storage_tokens: int = 0, - mask_k_bias: bool = False, - pretrained: bool = True, - version: Optional[str] = None, - weights: Union[Weights, str] = Weights.LVD1689M, - hash: Optional[str] = None, - check_hash: bool = False, - **kwargs, -): - from ..models.vision_transformer import DinoVisionTransformer - - vit_kwargs = dict( - img_size=img_size, - patch_size=patch_size, - in_chans=in_chans, - pos_embed_rope_base=pos_embed_rope_base, - pos_embed_rope_min_period=pos_embed_rope_min_period, - pos_embed_rope_max_period=pos_embed_rope_max_period, - pos_embed_rope_normalize_coords=pos_embed_rope_normalize_coords, - pos_embed_rope_shift_coords=pos_embed_rope_shift_coords, - pos_embed_rope_jitter_coords=pos_embed_rope_jitter_coords, - pos_embed_rope_rescale_coords=pos_embed_rope_rescale_coords, - pos_embed_rope_dtype=pos_embed_rope_dtype, - embed_dim=embed_dim, - depth=depth, - num_heads=num_heads, - ffn_ratio=ffn_ratio, - qkv_bias=qkv_bias, - drop_path_rate=drop_path_rate, - layerscale_init=layerscale_init, - norm_layer=norm_layer, - ffn_layer=ffn_layer, - ffn_bias=ffn_bias, - proj_bias=proj_bias, - n_storage_tokens=n_storage_tokens, - mask_k_bias=mask_k_bias, - ) - vit_kwargs.update(**kwargs) - model = DinoVisionTransformer(**vit_kwargs) - if pretrained: - if type(weights) is Weights and weights not in {Weights.LVD1689M, Weights.SAT493M}: - raise ValueError(f"Unsupported weights for the backbone: {weights}") - elif type(weights) is Weights: - url = _make_dinov3_vit_model_url( - patch_size=patch_size, - compact_arch_name=compact_arch_name, - version=version, - weights=weights, - hash=hash, - ) - else: - url = convert_path_or_url_to_url(weights) - state_dict = torch.hub.load_state_dict_from_url(url, map_location="cpu", check_hash=check_hash) - model.load_state_dict(state_dict, strict=True) - else: - model.init_weights() - return model - - -def _make_dinov3_convnext_model_url( - *, - compact_arch_name: str = "convnext_base", - weights: Union[Weights, str] = Weights.LVD1689M, - hash: Optional[str] = None, -): - model_name = "dinov3" - weights_name = weights.value.lower() - hash_suffix = f"-{hash}" if hash else "" - - model_dir = f"{model_name}_{compact_arch_name}" - model_filename = f"{model_name}_{compact_arch_name}_pretrain_{weights_name}{hash_suffix}.pth" - return os.path.join(DINOV3_BASE_URL, model_dir, model_filename) - - -def _make_dinov3_convnext( - in_chans: int = 3, - depths: List[int] = [3, 3, 27, 3], - dims: List[int] = [128, 256, 512, 1024], - compact_arch_name: str = "convnext_base", - drop_path_rate: float = 0.0, - layer_scale_init_value: float = 1e-6, - pretrained: bool = True, - weights: Union[Weights, str] = Weights.LVD1689M, - hash: Optional[str] = None, - **kwargs, -): - from ..models.convnext import ConvNeXt - - model_kwargs = dict( - in_chans=in_chans, - depths=depths, - dims=dims, - drop_path_rate=drop_path_rate, - layer_scale_init_value=layer_scale_init_value, - ) - model_kwargs.update(**kwargs) - model = ConvNeXt(**model_kwargs) - if pretrained: - if type(weights) is Weights and weights not in {Weights.LVD1689M, Weights.SAT493M}: - raise ValueError(f"Unsupported weights for the backbone: {weights}") - elif type(weights) is Weights: - url = _make_dinov3_convnext_model_url( - compact_arch_name=compact_arch_name, - weights=weights, - hash=hash, - ) - else: - url = convert_path_or_url_to_url(weights) - state_dict = torch.hub.load_state_dict_from_url(url, map_location="cpu") - model.load_state_dict(state_dict, strict=True) - return model - - -def dinov3_vits16( - *, - pretrained: bool = True, - weights: Union[Weights, str] = Weights.LVD1689M, - check_hash: bool = False, - **kwargs, -): - if "hash" not in kwargs: - kwargs["hash"] = "08c60483" - kwargs["version"] = None - return _make_dinov3_vit( - img_size=224, - patch_size=16, - in_chans=3, - pos_embed_rope_base=100, - pos_embed_rope_normalize_coords="separate", - pos_embed_rope_rescale_coords=2, - pos_embed_rope_dtype="fp32", - embed_dim=384, - depth=12, - num_heads=6, - ffn_ratio=4, - qkv_bias=True, - drop_path_rate=0.0, - layerscale_init=1.0e-05, - norm_layer="layernormbf16", - ffn_layer="mlp", - ffn_bias=True, - proj_bias=True, - n_storage_tokens=4, - mask_k_bias=True, - pretrained=pretrained, - weights=weights, - compact_arch_name="vits", - check_hash=check_hash, - **kwargs, - ) - - -def dinov3_vits16plus( - *, - pretrained: bool = True, - weights: Union[Weights, str] = Weights.LVD1689M, - check_hash: bool = False, - **kwargs, -): - if "hash" not in kwargs: - kwargs["hash"] = "4057cbaa" - kwargs["version"] = None - return _make_dinov3_vit( - img_size=224, - patch_size=16, - in_chans=3, - pos_embed_rope_base=100, - pos_embed_rope_normalize_coords="separate", - pos_embed_rope_rescale_coords=2, - pos_embed_rope_dtype="fp32", - embed_dim=384, - depth=12, - num_heads=6, - ffn_ratio=6, - qkv_bias=True, - drop_path_rate=0.0, - layerscale_init=1.0e-05, - norm_layer="layernormbf16", - ffn_layer="swiglu", - ffn_bias=True, - proj_bias=True, - n_storage_tokens=4, - mask_k_bias=True, - pretrained=pretrained, - weights=weights, - compact_arch_name="vitsplus", - check_hash=check_hash, - **kwargs, - ) - - -def dinov3_vitb16( - *, - pretrained: bool = True, - weights: Union[Weights, str] = Weights.LVD1689M, - check_hash: bool = False, - **kwargs, -): - if "hash" not in kwargs: - kwargs["hash"] = "73cec8be" - kwargs["version"] = None - return _make_dinov3_vit( - img_size=224, - patch_size=16, - in_chans=3, - pos_embed_rope_base=100, - pos_embed_rope_normalize_coords="separate", - pos_embed_rope_rescale_coords=2, - pos_embed_rope_dtype="fp32", - embed_dim=768, - depth=12, - num_heads=12, - ffn_ratio=4, - qkv_bias=True, - drop_path_rate=0.0, - layerscale_init=1.0e-05, - norm_layer="layernormbf16", - ffn_layer="mlp", - ffn_bias=True, - proj_bias=True, - n_storage_tokens=4, - mask_k_bias=True, - pretrained=pretrained, - weights=weights, - compact_arch_name="vitb", - check_hash=check_hash, - **kwargs, - ) - - -def dinov3_vitl16( - *, - pretrained: bool = True, - weights: Union[Weights, str] = Weights.LVD1689M, - check_hash: bool = False, - **kwargs, -): - untie_global_and_local_cls_norm = False - if weights == Weights.LVD1689M: - if "hash" not in kwargs: - kwargs["hash"] = "8aa4cbdd" - elif weights == Weights.SAT493M: - if "hash" not in kwargs: - kwargs["hash"] = "eadcf0ff" - untie_global_and_local_cls_norm = True - elif type(weights) is str: - import re - - pattern = r"-(.{8}).pth" - matches = re.findall(pattern, weights) - if len(matches) != 1: - raise ValueError(f"Unexpected weights specification for the ViT-L backbone: {weights}") - hash = matches[0] - if hash == "eadcf0ff": - untie_global_and_local_cls_norm = True - kwargs["version"] = None - return _make_dinov3_vit( - img_size=224, - patch_size=16, - in_chans=3, - pos_embed_rope_base=100, - pos_embed_rope_normalize_coords="separate", - pos_embed_rope_rescale_coords=2, - pos_embed_rope_dtype="fp32", - embed_dim=1024, - depth=24, - num_heads=16, - ffn_ratio=4, - qkv_bias=True, - drop_path_rate=0.0, - layerscale_init=1.0e-05, - norm_layer="layernormbf16", - ffn_layer="mlp", - ffn_bias=True, - proj_bias=True, - n_storage_tokens=4, - mask_k_bias=True, - untie_global_and_local_cls_norm=untie_global_and_local_cls_norm, - pretrained=pretrained, - weights=weights, - compact_arch_name="vitl", - check_hash=check_hash, - **kwargs, - ) - - -def dinov3_vitl16plus( - *, - pretrained: bool = True, - weights: Union[Weights, str] = Weights.LVD1689M, - check_hash: bool = False, - **kwargs, -): - if "hash" not in kwargs: - kwargs["hash"] = "46503df0" - - return _make_dinov3_vit( - img_size=224, - patch_size=16, - in_chans=3, - pos_embed_rope_base=100, - pos_embed_rope_normalize_coords="separate", - pos_embed_rope_rescale_coords=2, - pos_embed_rope_dtype="fp32", - embed_dim=1024, - depth=24, - num_heads=16, - ffn_ratio=6.0, - qkv_bias=True, - drop_path_rate=0.0, - layerscale_init=1.0e-05, - norm_layer="layernormbf16", - ffn_layer="swiglu", - ffn_bias=True, - proj_bias=True, - n_storage_tokens=4, - mask_k_bias=True, - pretrained=pretrained, - weights=weights, - compact_arch_name="vitlplus", - check_hash=check_hash, - **kwargs, - ) - - -def dinov3_vith16plus( - *, - pretrained: bool = True, - weights: Union[Weights, str] = Weights.LVD1689M, - check_hash: bool = False, - **kwargs, -): - if "hash" not in kwargs: - kwargs["hash"] = "7c1da9a5" - - return _make_dinov3_vit( - img_size=224, - patch_size=16, - in_chans=3, - pos_embed_rope_base=100, - pos_embed_rope_normalize_coords="separate", - pos_embed_rope_rescale_coords=2, - pos_embed_rope_dtype="fp32", - embed_dim=1280, - depth=32, - num_heads=20, - ffn_ratio=6.0, - qkv_bias=True, - drop_path_rate=0.0, - layerscale_init=1.0e-05, - norm_layer="layernormbf16", - ffn_layer="swiglu", - ffn_bias=True, - proj_bias=True, - n_storage_tokens=4, - mask_k_bias=True, - pretrained=pretrained, - weights=weights, - compact_arch_name="vithplus", - check_hash=check_hash, - **kwargs, - ) - - -def dinov3_vit7b16( - *, - pretrained: bool = True, - weights: Union[Weights, str] = Weights.LVD1689M, - check_hash: bool = False, - **kwargs, -): - if weights == Weights.LVD1689M: - if "hash" not in kwargs: - kwargs["hash"] = "a955f4ea" - elif weights == Weights.SAT493M: - if "hash" not in kwargs: - kwargs["hash"] = "a6675841" - kwargs["version"] = None - untie_global_and_local_cls_norm = True - return _make_dinov3_vit( - img_size=224, - patch_size=16, - in_chans=3, - pos_embed_rope_base=100, - pos_embed_rope_normalize_coords="separate", - pos_embed_rope_rescale_coords=2, - pos_embed_rope_dtype="fp32", - embed_dim=4096, - depth=40, - num_heads=32, - ffn_ratio=3, - qkv_bias=False, - drop_path_rate=0.0, - layerscale_init=1.0e-05, - norm_layer="layernormbf16", - ffn_layer="swiglu64", - ffn_bias=True, - proj_bias=True, - n_storage_tokens=4, - mask_k_bias=True, - untie_global_and_local_cls_norm=untie_global_and_local_cls_norm, - pretrained=pretrained, - weights=weights, - compact_arch_name="vit7b", - check_hash=check_hash, - **kwargs, - ) - - -def dinov3_convnext_tiny( - *, - pretrained: bool = True, - weights: Union[Weights, str] = Weights.LVD1689M, - **kwargs, -): - _hash_convnext = "21b726bb" - if "hash" not in kwargs: - kwargs["hash"] = _hash_convnext - - from ..models.convnext import convnext_sizes - - size_dict = convnext_sizes["tiny"] - - model = _make_dinov3_convnext( - in_chans=3, - depths=size_dict["depths"], - dims=size_dict["dims"], - compact_arch_name="convnext_tiny", - drop_path_rate=0, - layer_scale_init_value=1e-6, - pretrained=pretrained, - weights=weights, - **kwargs, - ) - if not pretrained: - model.init_weights() - return model - - -def dinov3_convnext_small( - *, - pretrained: bool = True, - weights: Union[Weights, str] = Weights.LVD1689M, - **kwargs, -): - _hash_convnext = "296db49d" - if "hash" not in kwargs: - kwargs["hash"] = _hash_convnext - - from ..models.convnext import convnext_sizes - - size_dict = convnext_sizes["small"] - - model = _make_dinov3_convnext( - in_chans=3, - depths=size_dict["depths"], - dims=size_dict["dims"], - compact_arch_name="convnext_small", - drop_path_rate=0, - layer_scale_init_value=1e-6, - pretrained=pretrained, - weights=weights, - **kwargs, - ) - if not pretrained: - model.init_weights() - return model - - -def dinov3_convnext_base( - *, - pretrained: bool = True, - weights: Union[Weights, str] = Weights.LVD1689M, - **kwargs, -): - _hash_convnext = "801f2ba9" - if "hash" not in kwargs: - kwargs["hash"] = _hash_convnext - - from ..models.convnext import convnext_sizes - - size_dict = convnext_sizes["base"] - - model = _make_dinov3_convnext( - in_chans=3, - depths=size_dict["depths"], - dims=size_dict["dims"], - compact_arch_name="convnext_base", - drop_path_rate=0, - layer_scale_init_value=1e-6, - pretrained=pretrained, - weights=weights, - **kwargs, - ) - if not pretrained: - model.init_weights() - return model - - -def dinov3_convnext_large( - *, - pretrained: bool = True, - weights: Union[Weights, str] = Weights.LVD1689M, - **kwargs, -): - _hash_convnext = "61fa432d" - if "hash" not in kwargs: - kwargs["hash"] = _hash_convnext - - from ..models.convnext import convnext_sizes - - size_dict = convnext_sizes["large"] - - model = _make_dinov3_convnext( - in_chans=3, - depths=size_dict["depths"], - dims=size_dict["dims"], - compact_arch_name="convnext_large", - drop_path_rate=0, - layer_scale_init_value=1e-6, - pretrained=pretrained, - weights=weights, - **kwargs, - ) - if not pretrained: - model.init_weights() - return model diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/hub/classifiers.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/hub/classifiers.py deleted file mode 100644 index 1595f38cf5b5064ac6341bbb2a989617dda4e199..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/hub/classifiers.py +++ /dev/null @@ -1,114 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import os -from enum import Enum -from typing import Optional - -import torch -import torch.nn as nn - -from .backbones import ( - dinov3_vit7b16, - Weights as BackboneWeights, - convert_path_or_url_to_url, -) - -from .utils import DINOV3_BASE_URL - - -class ClassifierWeights(Enum): - IMAGENET1K = "IMAGENET1K" - - -def _make_dinov3_linear_classification_head( - *, - backbone_name: str = "dinov3_vit7b16", - embed_dim: int = 8192, - pretrained: bool = True, - classifier_weights: ClassifierWeights | str = ClassifierWeights.IMAGENET1K, - check_hash: bool = False, - **kwargs, -): - linear_head = nn.Linear(embed_dim, 1_000) - if pretrained: - if type(classifier_weights) is ClassifierWeights: - assert classifier_weights == ClassifierWeights.IMAGENET1K, ( - f"Unsupported weights for linear classifier: {classifier_weights}" - ) - weights_name = classifier_weights.value.lower() - hash = kwargs["hash"] if "hash" in kwargs else "90d8ed92" - model_filename = f"{backbone_name}_{weights_name}_linear_head-{hash}.pth" - url = os.path.join(DINOV3_BASE_URL, backbone_name, model_filename) - else: - url = convert_path_or_url_to_url(classifier_weights) - state_dict = torch.hub.load_state_dict_from_url(url, map_location="cpu", check_hash=check_hash) - linear_head.load_state_dict(state_dict, strict=True) - return linear_head - - -class _LinearClassifierWrapper(nn.Module): - def __init__(self, *, backbone: nn.Module, linear_head: nn.Module): - super().__init__() - self.backbone = backbone - self.linear_head = linear_head - - def forward(self, x): - x = self.backbone.forward_features(x) - cls_token = x["x_norm_clstoken"] - patch_tokens = x["x_norm_patchtokens"] - linear_input = torch.cat( - [ - cls_token, - patch_tokens.mean(dim=1), - ], - dim=1, - ) - return self.linear_head(linear_input) - - -def _make_dinov3_linear_classifier( - *, - backbone_name: str = "dinov3_vit7b16", - pretrained: bool = True, - classifier_weights: ClassifierWeights | str = ClassifierWeights.IMAGENET1K, - backbone_weights: BackboneWeights | str = BackboneWeights.LVD1689M, - check_hash: bool = False, - **kwargs, -): - if backbone_name == "dinov3_vit7b16": - backbone = dinov3_vit7b16(pretrained=pretrained, weights=backbone_weights, check_hash=check_hash) - else: - raise AssertionError(f"Unsupported backbone: {backbone_name}, linear classifiers are provided only for ViT-7b") - embed_dim = backbone.embed_dim - linear_head = _make_dinov3_linear_classification_head( - backbone_name=backbone_name, - embed_dim=2 * embed_dim, - pretrained=pretrained, - classifier_weights=classifier_weights, - **kwargs, - ) - return _LinearClassifierWrapper(backbone=backbone, linear_head=linear_head) - - -def dinov3_vit7b16_lc( - *, - pretrained: bool = True, - weights: ClassifierWeights | str = ClassifierWeights.IMAGENET1K, - backbone_weights: BackboneWeights | str = BackboneWeights.LVD1689M, - check_hash: bool = False, - **kwargs, -): - """ - Linear classifier on top of a DINOv3 ViT-7B/16 backbone pretrained on the LVD-1689M dataset and trained on ImageNet-1k. - """ - return _make_dinov3_linear_classifier( - backbone_name="dinov3_vit7b16", - pretrained=pretrained, - classifier_weights=weights, - backbone_weights=backbone_weights, - check_hash=check_hash, - **kwargs, - ) diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/hub/depthers.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/hub/depthers.py deleted file mode 100644 index 25c67e080be4f33fbf9b4a956e27c4be5184821a..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/hub/depthers.py +++ /dev/null @@ -1,145 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -from enum import Enum -from typing import Optional, Tuple - -import torch -from dinov3.eval.depth.models import DecoderConfig, make_depther_from_config - -from .utils import DINOV3_BASE_URL -from .backbones import ( - Weights as BackboneWeights, - dinov3_vitl16, - dinov3_vit7b16, - convert_path_or_url_to_url, -) - - -class DepthWeights(Enum): - SYNTHMIX = "SYNTHMIX" - - -def _get_depth_range(dataset: DepthWeights): - depth_ranges = { - DepthWeights.SYNTHMIX: (0.001, 100.0), - } - return depth_ranges[dataset] - - -_DPT_HEAD_CONFIG_DICT = dict( - use_backbone_norm=True, - use_batchnorm=True, - use_cls_token=False, - n_output_channels=256, - depth_weights=DepthWeights.SYNTHMIX, - backbone_weights=BackboneWeights.LVD1689M, -) - - -def _get_out_layers(backbone_name): - if "vitl" in backbone_name: - return [4, 11, 17, 23] - elif "vit7b" in backbone_name: - return [9, 19, 29, 39] - else: - raise ValueError(f"Unrecognized backbone name {backbone_name}") - - -def _get_post_process_channels(backbone_name): - if "vitl" in backbone_name: - return [1024, 1024, 1024, 1024] - elif "vit7b" in backbone_name: - return [2048, 2048, 2048, 2048] - - -_BACKBONE_DICT = { - "dinov3_vit7b16": dinov3_vit7b16, - "dinov3_vitl16": dinov3_vitl16, -} - - -def _get_depther_config( - backbone_name: str = "dinov3_vit7b16", - depth_range: Optional[Tuple[float, float]] = None, - **kwargs, -): - out_index = _get_out_layers(backbone_name) - post_process_channels = _get_post_process_channels(backbone_name) - - depth_range = depth_range or _get_depth_range(DepthWeights(_DPT_HEAD_CONFIG_DICT["depth_weights"])) - min_depth, max_depth = depth_range - depther_config = DecoderConfig( - min_depth=min_depth, - max_depth=max_depth, - backbone_out_layers=out_index, - n_output_channels=_DPT_HEAD_CONFIG_DICT["n_output_channels"], # type: ignore - use_backbone_norm=bool(_DPT_HEAD_CONFIG_DICT["use_backbone_norm"]), - use_batchnorm=bool(_DPT_HEAD_CONFIG_DICT["use_batchnorm"]), - use_cls_token=bool(_DPT_HEAD_CONFIG_DICT["use_cls_token"]), - type="dpt", - # DPTHead args - head_kwargs=dict( - channels=512, - post_process_channels=post_process_channels, - ), - **kwargs, - ) - return depther_config - - -def _make_dinov3_dpt_depther( - *, - backbone_name: str = "dinov3_vit7b16", - pretrained: bool = True, - depther_weights: DepthWeights | str = DepthWeights.SYNTHMIX, - backbone_weights: BackboneWeights | str = BackboneWeights.LVD1689M, - depth_range: Optional[Tuple[float, float]] = None, - check_hash: bool = False, - autocast_dtype: torch.dtype = torch.float32, - **kwargs, -): - backbone: torch.nn.Module = _BACKBONE_DICT[backbone_name]( - pretrained=pretrained, - weights=backbone_weights, - ) - - depther = make_depther_from_config( - backbone, - config=_get_depther_config(backbone_name, depth_range), - autocast_dtype=autocast_dtype, - ) - - if pretrained: - if isinstance(depther_weights, DepthWeights): - assert depther_weights == DepthWeights.SYNTHMIX, f"Unsupported depther weights {depther_weights}" - weights_name = depther_weights.value.lower() - hash = kwargs["hash"] if "hash" in kwargs else "02040be1" - url = DINOV3_BASE_URL + f"/{backbone_name}/{backbone_name}_{weights_name}_dpt_head-{hash}.pth" - else: - url = convert_path_or_url_to_url(depther_weights) - checkpoint = torch.hub.load_state_dict_from_url(url, map_location="cpu", check_hash=check_hash) - depther.decoder.load_state_dict(checkpoint, strict=True) - return depther - - -def dinov3_vit7b16_dd( - *, - pretrained: bool = True, - weights: DepthWeights | str = DepthWeights.SYNTHMIX, - backbone_weights: BackboneWeights | str = BackboneWeights.LVD1689M, - check_hash: bool = False, - autocast_dtype: torch.dtype = torch.float32, - **kwargs, -): - return _make_dinov3_dpt_depther( - backbone_name="dinov3_vit7b16", - pretrained=pretrained, - depther_weights=weights, - backbone_weights=backbone_weights, - check_hash=check_hash, - autocast_dtype=autocast_dtype, - **kwargs, - ) diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/hub/detectors.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/hub/detectors.py deleted file mode 100644 index f4861af0187570f8a8dc3fd55c4d8d860e58dcc2..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/hub/detectors.py +++ /dev/null @@ -1,132 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import os -from enum import Enum - -import torch - -from dinov3.eval.detection.config import DetectionHeadConfig -from dinov3.eval.detection.models.detr import PostProcess, build_model -from dinov3.eval.detection.models.position_encoding import PositionEncoding - -from .backbones import Weights as BackboneWeights, dinov3_vit7b16, dinov3_vitl16plus, convert_path_or_url_to_url -from .utils import DINOV3_BASE_URL - - -class DetectionWeights(Enum): - COCO2017 = "COCO2017" - - -class DetectorWithProcessor(torch.nn.Module): - """ - takes as input a list of (3, H, W) normalized image tensors and outputs - a list of dicts with keys "scores", "labels" and "boxes" (format XYXY) - """ - - def __init__(self, detector, postprocessor): - super().__init__() - self.detector = detector - self.postprocessor = postprocessor - - def forward(self, samples: list[torch.Tensor]): - outputs = self.detector(samples) - sizes_tensor = torch.tensor([sample.shape[1:] for sample in samples], device=samples[0].device) # N * [3, H, W] - return self.postprocessor(outputs, target_sizes=sizes_tensor, original_target_sizes=sizes_tensor) - - -def _make_dinov3_detector( - *, - backbone_name: str, - pretrained: bool = True, - detector_weights: str | DetectionWeights, - backbone_weights: str | BackboneWeights, - check_hash: bool = False, - **kwargs, -): - detection_kwargs = dict( - with_box_refine=True, - two_stage=True, - mixed_selection=True, - look_forward_twice=True, - k_one2many=6, - lambda_one2many=1.0, - num_queries_one2one=1500, - num_queries_one2many=1500, - reparam=True, - position_embedding=PositionEncoding.SINE, - num_feature_levels=1, - dec_layers=6, - dim_feedforward=2048, - dropout=0.0, - norm_type="pre_norm", - proposal_feature_levels=4, - proposal_min_size=50, - decoder_type="global_rpe_decomp", - decoder_use_checkpoint=False, - decoder_rpe_hidden_dim=512, - decoder_rpe_type="linear", - layers_to_use=None, - blocks_to_train=None, - add_transformer_encoder=True, - num_encoder_layers=6, - backbone_use_layernorm=False, - num_classes=91, # 91 classes in COCO - aux_loss=True, - topk=1500, - hidden_dim=768, - nheads=8, - ) - config = DetectionHeadConfig(**detection_kwargs) - backbone_class = dict(dinov3_vit7b16=dinov3_vit7b16, dinov3_vitl16plus=dinov3_vitl16plus)[backbone_name] - n_windows_sqrt = dict(dinov3_vit7b16=3, dinov3_vitl16plus=2)[backbone_name] - backbone = backbone_class(pretrained=pretrained, weights=backbone_weights, check_hash=check_hash) - backbone.eval() - - config.n_windows_sqrt = n_windows_sqrt - config.proposal_in_stride = backbone.patch_size - config.proposal_tgt_strides = [int(m * backbone.patch_size) for m in (0.5, 1, 2, 4)] - - if config.layers_to_use is None: - # e.g. [2, 5, 8, 11] for a backbone with 12 blocks, similar to depth evaluation - config.layers_to_use = [m * backbone.n_blocks // 4 - 1 for m in range(1, 5)] - - detector = build_model(backbone, config) - if pretrained: - if type(detector_weights) is DetectionWeights and detector_weights == DetectionWeights.COCO2017: - assert detector_weights == DetectionWeights.COCO2017, f"Unsupported detector weights {detector_weights}" - detection_weights_name = detector_weights.value.lower() - hash = kwargs["hash"] if "hash" in kwargs else "b0235ff7" - model_filename = f"{backbone_name}_{detection_weights_name}_detr_head-{hash}.pth" - url = os.path.join(DINOV3_BASE_URL, backbone_name, model_filename) - else: - url = convert_path_or_url_to_url(detector_weights) - state_dict = torch.hub.load_state_dict_from_url(url, map_location="cpu", check_hash=check_hash)["model"] - detector.load_state_dict(state_dict, strict=False) - # Necessary for inference - detector.num_queries = detector.num_queries_one2one - detector.transformer.two_stage_num_proposals = detector.num_queries - - postprocessor = PostProcess(config.topk, config.reparam) - model = DetectorWithProcessor(detector=detector, postprocessor=postprocessor) - return model - - -def dinov3_vit7b16_de( - *, - pretrained: bool = True, - weights: DetectionWeights | str = DetectionWeights.COCO2017, - backbone_weights: BackboneWeights | str = BackboneWeights.LVD1689M, - check_hash: bool = False, - **kwargs, -): - return _make_dinov3_detector( - backbone_name="dinov3_vit7b16", - pretrained=pretrained, - detector_weights=weights, - backbone_weights=backbone_weights, - check_hash=check_hash, - **kwargs, - ) diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/hub/dinotxt.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/hub/dinotxt.py deleted file mode 100644 index cff38f562453a2c5cf2411c8d9c379f8e7b407e9..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/hub/dinotxt.py +++ /dev/null @@ -1,82 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import math -from typing import Any, Tuple, Union -from enum import Enum - -import torch -from torch import nn - -from .backbones import dinov3_vitl16, Weights as BackboneWeights, convert_path_or_url_to_url -from .utils import DINOV3_BASE_URL - - -class DINOTxtWeights(Enum): - LVTD2300M = "LVTD2300M" - - -# returns dinotxt model and tokenizer -def dinov3_vitl16_dinotxt_tet1280d20h24l( - *, - pretrained: bool = True, - weights: Union[DINOTxtWeights, str] = DINOTxtWeights.LVTD2300M, - backbone_weights: Union[BackboneWeights, str] = BackboneWeights.LVD1689M, - bpe_path_or_url: str = "https://dl.fbaipublicfiles.com/dinov3/thirdparty/bpe_simple_vocab_16e6.txt.gz", - check_hash: bool = False, -) -> Tuple[nn.Module, Any]: - from dinov3.eval.text.dinotxt_model import DINOTxt, DINOTxtConfig - from dinov3.eval.text.text_transformer import TextTransformer - from dinov3.eval.text.tokenizer import get_tokenizer - - dinotxt_config = DINOTxtConfig( - embed_dim=2048, - vision_model_freeze_backbone=True, - vision_model_train_img_size=224, - vision_model_use_class_token=True, - vision_model_use_patch_tokens=True, - vision_model_num_head_blocks=2, - vision_model_head_blocks_drop_path=0.3, - vision_model_use_linear_projection=False, - vision_model_patch_tokens_pooler_type="mean", - vision_model_patch_token_layer=1, # which layer to take patch tokens from - # 1 - last layer, 2 - second last layer, etc. - text_model_freeze_backbone=False, - text_model_num_head_blocks=0, - text_model_head_blocks_is_causal=False, - text_model_head_blocks_drop_prob=0.0, - text_model_tokens_pooler_type="argmax", - text_model_use_linear_projection=True, - init_logit_scale=math.log(1 / 0.07), - init_logit_bias=None, - freeze_logit_scale=False, - ) - vision_backbone = dinov3_vitl16(pretrained=pretrained, weights=backbone_weights) - text_backbone = TextTransformer( - context_length=77, - vocab_size=49408, - dim=1280, - num_heads=20, - num_layers=24, - ffn_ratio=4, - is_causal=True, - ls_init_value=None, - dropout_prob=0.0, - ) - model = DINOTxt(model_config=dinotxt_config, vision_backbone=vision_backbone, text_backbone=text_backbone) - if pretrained: - model.visual_model.backbone = vision_backbone - model.eval() - if type(weights) is DINOTxtWeights and weights == DINOTxtWeights.LVTD2300M: - url = f"{DINOV3_BASE_URL}/dinov3_vitl16/dinov3_vitl16_dinotxt_vision_head_and_text_encoder-a442d8f5.pth" - elif type(weights) is DINOTxtWeights and weights != DINOTxtWeights.LVTD2300M: - raise AssertionError(f"Unsuported weights for DINOTxt: {weights}") - else: - url = convert_path_or_url_to_url(weights) - vision_head_and_text_encoder_state_dict = torch.hub.load_state_dict_from_url(url, check_hash=check_hash) - model.load_state_dict(vision_head_and_text_encoder_state_dict, strict=False) - else: - model.init_weights() - return model, get_tokenizer(bpe_path_or_url=bpe_path_or_url) diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/hub/segmentors.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/hub/segmentors.py deleted file mode 100644 index d6b40b7e4362f6938945c97be0c2e0d993d8a6f0..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/hub/segmentors.py +++ /dev/null @@ -1,83 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import os -from enum import Enum - -import torch -from dinov3.eval.segmentation.models import build_segmentation_decoder - -from .backbones import ( - dinov3_vit7b16, - dinov3_vitl16, - Weights as BackboneWeights, - convert_path_or_url_to_url, -) -from .utils import DINOV3_BASE_URL - - -class SegmentorWeights(Enum): - ADE20K = "ADE20K" - - -def _make_dinov3_m2f_segmentor( - *, - backbone_name: str = "dinov3_vit7b16", - pretrained: bool = True, - segmentor_weights: SegmentorWeights | str = SegmentorWeights.ADE20K, - backbone_weights: BackboneWeights | str = BackboneWeights.LVD1689M, - check_hash: bool = False, - autocast_dtype: torch.dtype = torch.bfloat16, - **kwargs, -): - if backbone_name == "dinov3_vit7b16": - backbone_model = dinov3_vit7b16(pretrained=pretrained, weights=backbone_weights, check_hash=check_hash) - elif backbone_name == "dinov3_vitl16": - backbone_model = dinov3_vitl16(pretrained=pretrained, weights=backbone_weights, check_hash=check_hash) - else: - raise AssertionError(f"No pretrained segmentation checkpoint available for {backbone_name}") - - hidden_dim = 2048 if "hidden_dim" not in kwargs else kwargs["hidden_dim"] - segmentor = build_segmentation_decoder( - backbone_model=backbone_model, - decoder_type="m2f", - hidden_dim=hidden_dim, - autocast_dtype=autocast_dtype, - ) - if pretrained: - if type(segmentor_weights) is SegmentorWeights: - assert segmentor_weights == SegmentorWeights.ADE20K, f"Unsupported weights for segmentor: {segmentor_weights}" - segmentor_weights_name = segmentor_weights.value.lower() - hash = kwargs["hash"] if "hash" in kwargs else "bf307cb1" - model_filename = f"{backbone_name}_{segmentor_weights_name}_m2f_head-{hash}.pth" - url = os.path.join(DINOV3_BASE_URL, backbone_name, model_filename) - else: - url = convert_path_or_url_to_url(segmentor_weights) - state_dict = torch.hub.load_state_dict_from_url(url, map_location="cpu", check_hash=check_hash) - missing_keys, unexpected_keys = segmentor.load_state_dict(state_dict, strict=False) - assert len([k for k in missing_keys if "backbone" not in k]) == 0 - assert len(unexpected_keys) == 0 - - return segmentor - - -def dinov3_vit7b16_ms( - *, - pretrained: bool = True, - weights: SegmentorWeights | str = SegmentorWeights.ADE20K, - backbone_weights: BackboneWeights | str = BackboneWeights.LVD1689M, - check_hash: bool = False, - autocast_dtype: torch.dtype = torch.bfloat16, - **kwargs, -): - return _make_dinov3_m2f_segmentor( - backbone_name="dinov3_vit7b16", - pretrained=pretrained, - segmentor_weights=weights, - backbone_weights=backbone_weights, - check_hash=check_hash, - autocast_dtype=autocast_dtype, - **kwargs, - ) diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/hub/utils.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/hub/utils.py deleted file mode 100644 index 3de79fcf1f032fe3d5d382c27c089cec5c1a0cb9..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/hub/utils.py +++ /dev/null @@ -1,6 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -DINOV3_BASE_URL = "https://dl.fbaipublicfiles.com/dinov3" diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/layers/__init__.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/layers/__init__.py deleted file mode 100644 index 5b82c261aaba6f0b7b871662f3549062e69928f8..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/layers/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -from .attention import CausalSelfAttention, LinearKMaskedBias, SelfAttention -from .block import CausalSelfAttentionBlock, SelfAttentionBlock -from .ffn_layers import Mlp, SwiGLUFFN -from .fp8_linear import convert_linears_to_fp8 -from .layer_scale import LayerScale -from .patch_embed import PatchEmbed -from .rms_norm import RMSNorm -from .rope_position_encoding import RopePositionEmbedding diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/layers/__pycache__/__init__.cpython-310.pyc b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/layers/__pycache__/__init__.cpython-310.pyc deleted file mode 100644 index 49b3a154394eda4212dfce5654b1c23ae639cbdd..0000000000000000000000000000000000000000 Binary files a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/layers/__pycache__/__init__.cpython-310.pyc and /dev/null differ diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/layers/__pycache__/attention.cpython-310.pyc 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Tensor: - # x: [ x0 x1 x2 x3 x4 x5] - # out: [-x3 -x4 -x5 x0 x1 x2] - x1, x2 = x.chunk(2, dim=-1) - return torch.cat([-x2, x1], dim=-1) - - -def rope_apply(x: Tensor, sin: Tensor, cos: Tensor) -> Tensor: - # x: [..., D], eg [x0, x1, x2, x3, x4, x5] - # sin: [..., D], eg [sin0, sin1, sin2, sin0, sin1, sin2] - # cos: [..., D], eg [cos0, cos1, cos2, cos0, cos1, cos2] - return (x * cos) + (rope_rotate_half(x) * sin) - - -class LinearKMaskedBias(nn.Linear): - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) - o = self.out_features - assert o % 3 == 0 - if self.bias is not None: - self.register_buffer("bias_mask", torch.full_like(self.bias, fill_value=math.nan)) - - def forward(self, input: Tensor) -> Tensor: - masked_bias = self.bias * self.bias_mask.to(self.bias.dtype) if self.bias is not None else None - return F.linear(input, self.weight, masked_bias) - - -class SelfAttention(nn.Module): - def __init__( - self, - dim: int, - num_heads: int = 8, - qkv_bias: bool = False, - proj_bias: bool = True, - attn_drop: float = 0.0, - proj_drop: float = 0.0, - mask_k_bias: bool = False, - device=None, - ) -> None: - super().__init__() - self.num_heads = num_heads - head_dim = dim // num_heads - self.scale = head_dim**-0.5 - - linear_class = LinearKMaskedBias if mask_k_bias else nn.Linear - self.qkv = linear_class(dim, dim * 3, bias=qkv_bias, device=device) - self.attn_drop = nn.Dropout(attn_drop) - self.proj = nn.Linear(dim, dim, bias=proj_bias, device=device) - self.proj_drop = nn.Dropout(proj_drop) - - def apply_rope(self, q: Tensor, k: Tensor, rope: Tensor | Tuple[Tensor, Tensor]) -> Tuple[Tensor, Tensor]: - # All operations will use the dtype of rope, the output is cast back to the dtype of q and k - q_dtype = q.dtype - k_dtype = k.dtype - sin, cos = rope - rope_dtype = sin.dtype - q = q.to(dtype=rope_dtype) - k = k.to(dtype=rope_dtype) - N = q.shape[-2] - prefix = N - sin.shape[-2] - assert prefix >= 0 - q_prefix = q[:, :, :prefix, :] - q = rope_apply(q[:, :, prefix:, :], sin, cos) # [B, head, hw, D//head] - q = torch.cat((q_prefix, q), dim=-2) # [B, head, N, D//head] - k_prefix = k[:, :, :prefix, :] - k = rope_apply(k[:, :, prefix:, :], sin, cos) # [B, head, hw, D//head] - k = torch.cat((k_prefix, k), dim=-2) # [B, head, N, D//head] - q = q.to(dtype=q_dtype) - k = k.to(dtype=k_dtype) - return q, k - - def forward(self, x: Tensor, attn_bias=None, rope: Tensor = None) -> Tensor: - qkv = self.qkv(x) - attn_v = self.compute_attention(qkv=qkv, attn_bias=attn_bias, rope=rope) - x = self.proj(attn_v) - x = self.proj_drop(x) - return x - - def forward_list(self, x_list, attn_bias=None, rope_list=None) -> List[Tensor]: - assert len(x_list) == len(rope_list) # should be enforced by the Block - x_flat, shapes, num_tokens = cat_keep_shapes(x_list) - qkv_flat = self.qkv(x_flat) - qkv_list = uncat_with_shapes(qkv_flat, shapes, num_tokens) - att_out = [] - for _, (qkv, _, rope) in enumerate(zip(qkv_list, shapes, rope_list)): - att_out.append(self.compute_attention(qkv, attn_bias=attn_bias, rope=rope)) - x_flat, shapes, num_tokens = cat_keep_shapes(att_out) - x_flat = self.proj(x_flat) - return uncat_with_shapes(x_flat, shapes, num_tokens) - - def compute_attention(self, qkv: Tensor, attn_bias=None, rope=None) -> Tensor: - assert attn_bias is None - B, N, _ = qkv.shape - C = self.qkv.in_features - - qkv = qkv.reshape(B, N, 3, self.num_heads, C // self.num_heads) - q, k, v = torch.unbind(qkv, 2) - q, k, v = [t.transpose(1, 2) for t in [q, k, v]] - if rope is not None: - q, k = self.apply_rope(q, k, rope) - x = torch.nn.functional.scaled_dot_product_attention(q, k, v) - x = x.transpose(1, 2) - return x.reshape([B, N, C]) - - -class CausalSelfAttention(nn.Module): - def __init__( - self, - dim: int, - num_heads: int = 8, - qkv_bias: bool = False, - proj_bias: bool = True, - attn_drop: float = 0.0, - proj_drop: float = 0.0, - ) -> None: - super().__init__() - self.dim = dim - self.num_heads = num_heads - head_dim = dim // num_heads - self.scale = head_dim**-0.5 - - self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) - self.attn_drop = attn_drop - self.proj = nn.Linear(dim, dim, bias=proj_bias) - self.proj_drop = nn.Dropout(proj_drop) - - def init_weights( - self, init_attn_std: float | None = None, init_proj_std: float | None = None, factor: float = 1.0 - ) -> None: - init_attn_std = init_attn_std or (self.dim**-0.5) - init_proj_std = init_proj_std or init_attn_std * factor - nn.init.normal_(self.qkv.weight, std=init_attn_std) - nn.init.normal_(self.proj.weight, std=init_proj_std) - if self.qkv.bias is not None: - nn.init.zeros_(self.qkv.bias) - if self.proj.bias is not None: - nn.init.zeros_(self.proj.bias) - - def forward(self, x: Tensor, is_causal: bool = True) -> Tensor: - B, N, C = x.shape - qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads) - q, k, v = torch.unbind(qkv, 2) - q, k, v = [t.transpose(1, 2) for t in [q, k, v]] - x = torch.nn.functional.scaled_dot_product_attention( - q, k, v, attn_mask=None, dropout_p=self.attn_drop if self.training else 0, is_causal=is_causal - ) - x = x.transpose(1, 2).contiguous().view(B, N, C) - x = self.proj_drop(self.proj(x)) - return x diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/layers/block.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/layers/block.py deleted file mode 100644 index 2051a1a607ab8c10c2476fa9d019225f4ba6ab5b..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/layers/block.py +++ /dev/null @@ -1,269 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -from typing import Callable, List, Optional - -import torch -from torch import Tensor, nn - -from dinov3.utils import cat_keep_shapes, uncat_with_shapes - -from .attention import CausalSelfAttention, SelfAttention -from .ffn_layers import Mlp -from .layer_scale import LayerScale # , DropPath - -torch._dynamo.config.automatic_dynamic_shapes = False -torch._dynamo.config.accumulated_cache_size_limit = 1024 - - -class SelfAttentionBlock(nn.Module): - def __init__( - self, - dim: int, - num_heads: int, - ffn_ratio: float = 4.0, - qkv_bias: bool = False, - proj_bias: bool = True, - ffn_bias: bool = True, - drop: float = 0.0, - attn_drop: float = 0.0, - init_values=None, - drop_path: float = 0.0, - act_layer: Callable[..., nn.Module] = nn.GELU, - norm_layer: Callable[..., nn.Module] = nn.LayerNorm, - attn_class: Callable[..., nn.Module] = SelfAttention, - ffn_layer: Callable[..., nn.Module] = Mlp, - mask_k_bias: bool = False, - device=None, - ) -> None: - super().__init__() - # print(f"biases: qkv: {qkv_bias}, proj: {proj_bias}, ffn: {ffn_bias}") - self.norm1 = norm_layer(dim) - self.attn = attn_class( - dim, - num_heads=num_heads, - qkv_bias=qkv_bias, - proj_bias=proj_bias, - attn_drop=attn_drop, - proj_drop=drop, - mask_k_bias=mask_k_bias, - device=device, - ) - self.ls1 = LayerScale(dim, init_values=init_values, device=device) if init_values else nn.Identity() - - self.norm2 = norm_layer(dim) - mlp_hidden_dim = int(dim * ffn_ratio) - self.mlp = ffn_layer( - in_features=dim, - hidden_features=mlp_hidden_dim, - act_layer=act_layer, - drop=drop, - bias=ffn_bias, - device=device, - ) - self.ls2 = LayerScale(dim, init_values=init_values, device=device) if init_values else nn.Identity() - - self.sample_drop_ratio = drop_path - - @staticmethod - def _maybe_index_rope(rope: tuple[Tensor, Tensor] | None, indices: Tensor) -> tuple[Tensor, Tensor] | None: - if rope is None: - return None - - sin, cos = rope - assert sin.ndim == cos.ndim - if sin.ndim == 4: - # If the rope embedding has a batch dimension (is different for each batch element), index into it - return sin[indices], cos[indices] # [batch, heads, patches, embed_dim] - else: - # No batch dimension, do not index - return sin, cos # [heads, patches, embed_dim] or [patches, embed_dim] - - def _forward(self, x: Tensor, rope=None) -> Tensor: - """ - This is the reference implementation for a single tensor, matching what is done below for a list. - We call the list op on [x] instead of this function. - """ - b, _, _ = x.shape - sample_subset_size = max(int(b * (1 - self.sample_drop_ratio)), 1) - residual_scale_factor = b / sample_subset_size - - if self.training and self.sample_drop_ratio > 0.0: - indices_1 = (torch.randperm(b, device=x.device))[:sample_subset_size] - - x_subset_1 = x[indices_1] - rope_subset = self._maybe_index_rope(rope, indices_1) - residual_1 = self.attn(self.norm1(x_subset_1), rope=rope_subset) - - x_attn = torch.index_add( - x, - dim=0, - source=self.ls1(residual_1), - index=indices_1, - alpha=residual_scale_factor, - ) - - indices_2 = (torch.randperm(b, device=x.device))[:sample_subset_size] - - x_subset_2 = x_attn[indices_2] - residual_2 = self.mlp(self.norm2(x_subset_2)) - - x_ffn = torch.index_add( - x_attn, - dim=0, - source=self.ls2(residual_2), - index=indices_2, - alpha=residual_scale_factor, - ) - else: - x_attn = x + self.ls1(self.attn(self.norm1(x), rope=rope)) - x_ffn = x_attn + self.ls2(self.mlp(self.norm2(x_attn))) - - return x_ffn - - def _forward_list(self, x_list: List[Tensor], rope_list=None) -> List[Tensor]: - """ - This list operator concatenates the tokens from the list of inputs together to save - on the elementwise operations. Torch-compile memory-planning allows hiding the overhead - related to concat ops. - """ - b_list = [x.shape[0] for x in x_list] - sample_subset_sizes = [max(int(b * (1 - self.sample_drop_ratio)), 1) for b in b_list] - residual_scale_factors = [b / sample_subset_size for b, sample_subset_size in zip(b_list, sample_subset_sizes)] - - if self.training and self.sample_drop_ratio > 0.0: - indices_1_list = [ - (torch.randperm(b, device=x.device))[:sample_subset_size] - for x, b, sample_subset_size in zip(x_list, b_list, sample_subset_sizes) - ] - x_subset_1_list = [x[indices_1] for x, indices_1 in zip(x_list, indices_1_list)] - - if rope_list is not None: - rope_subset_list = [ - self._maybe_index_rope(rope, indices_1) for rope, indices_1 in zip(rope_list, indices_1_list) - ] - else: - rope_subset_list = rope_list - - flattened, shapes, num_tokens = cat_keep_shapes(x_subset_1_list) - norm1 = uncat_with_shapes(self.norm1(flattened), shapes, num_tokens) - residual_1_list = self.attn.forward_list(norm1, rope_list=rope_subset_list) - - x_attn_list = [ - torch.index_add( - x, - dim=0, - source=self.ls1(residual_1), - index=indices_1, - alpha=residual_scale_factor, - ) - for x, residual_1, indices_1, residual_scale_factor in zip( - x_list, residual_1_list, indices_1_list, residual_scale_factors - ) - ] - - indices_2_list = [ - (torch.randperm(b, device=x.device))[:sample_subset_size] - for x, b, sample_subset_size in zip(x_list, b_list, sample_subset_sizes) - ] - x_subset_2_list = [x[indices_2] for x, indices_2 in zip(x_attn_list, indices_2_list)] - flattened, shapes, num_tokens = cat_keep_shapes(x_subset_2_list) - norm2_flat = self.norm2(flattened) - norm2_list = uncat_with_shapes(norm2_flat, shapes, num_tokens) - - residual_2_list = self.mlp.forward_list(norm2_list) - - x_ffn = [ - torch.index_add( - x_attn, - dim=0, - source=self.ls2(residual_2), - index=indices_2, - alpha=residual_scale_factor, - ) - for x_attn, residual_2, indices_2, residual_scale_factor in zip( - x_attn_list, residual_2_list, indices_2_list, residual_scale_factors - ) - ] - else: - x_out = [] - for x, rope in zip(x_list, rope_list): - x_attn = x + self.ls1(self.attn(self.norm1(x), rope=rope)) - x_ffn = x_attn + self.ls2(self.mlp(self.norm2(x_attn))) - x_out.append(x_ffn) - x_ffn = x_out - - return x_ffn - - def forward(self, x_or_x_list, rope_or_rope_list=None) -> List[Tensor]: - if isinstance(x_or_x_list, Tensor): - # for reference: - # return self._forward(x_or_x_list, rope=rope_or_rope_list) - # in order to match implementations we call the list op: - return self._forward_list([x_or_x_list], rope_list=[rope_or_rope_list])[0] - elif isinstance(x_or_x_list, list): - if rope_or_rope_list is None: - rope_or_rope_list = [None for x in x_or_x_list] - # return [self._forward(x, rope=rope) for x, rope in zip(x_or_x_list, rope_or_rope_list)] - return self._forward_list(x_or_x_list, rope_list=rope_or_rope_list) - else: - raise AssertionError - - -class CausalSelfAttentionBlock(nn.Module): - def __init__( - self, - dim: int, - num_heads: int, - ffn_ratio: float = 4.0, - ls_init_value: Optional[float] = None, - is_causal: bool = True, - act_layer: Callable = nn.GELU, - norm_layer: Callable = nn.LayerNorm, - dropout_prob: float = 0.0, - ): - super().__init__() - - self.dim = dim - self.is_causal = is_causal - self.ls1 = LayerScale(dim, init_values=ls_init_value) if ls_init_value else nn.Identity() - self.attention_norm = norm_layer(dim) - self.attention = CausalSelfAttention(dim, num_heads, attn_drop=dropout_prob, proj_drop=dropout_prob) - - self.ffn_norm = norm_layer(dim) - ffn_hidden_dim = int(dim * ffn_ratio) - self.feed_forward = Mlp( - in_features=dim, - hidden_features=ffn_hidden_dim, - drop=dropout_prob, - act_layer=act_layer, - ) - - self.ls2 = LayerScale(dim, init_values=ls_init_value) if ls_init_value else nn.Identity() - - def init_weights( - self, - init_attn_std: float | None = None, - init_proj_std: float | None = None, - init_fc_std: float | None = None, - factor: float = 1.0, - ) -> None: - init_attn_std = init_attn_std or (self.dim**-0.5) - init_proj_std = init_proj_std or init_attn_std * factor - init_fc_std = init_fc_std or (2 * self.dim) ** -0.5 - self.attention.init_weights(init_attn_std, init_proj_std) - self.attention_norm.reset_parameters() - nn.init.normal_(self.feed_forward.fc1.weight, std=init_fc_std) - nn.init.normal_(self.feed_forward.fc2.weight, std=init_proj_std) - self.ffn_norm.reset_parameters() - - def forward( - self, - x: torch.Tensor, - ): - - x_attn = x + self.ls1(self.attention(self.attention_norm(x), self.is_causal)) - x_ffn = x_attn + self.ls2(self.feed_forward(self.ffn_norm(x_attn))) - return x_ffn diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/layers/dino_head.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/layers/dino_head.py deleted file mode 100644 index bb71f35fc7ecf15e31963eb76d21626ccdea9b90..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/layers/dino_head.py +++ /dev/null @@ -1,67 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import torch -import torch.nn as nn -from torch.nn.init import trunc_normal_ - - -class DINOHead(nn.Module): - def __init__( - self, - in_dim, - out_dim, - use_bn=False, - nlayers=3, - hidden_dim=2048, - bottleneck_dim=256, - mlp_bias=True, - ): - super().__init__() - nlayers = max(nlayers, 1) - self.mlp = _build_mlp( - nlayers, - in_dim, - bottleneck_dim, - hidden_dim=hidden_dim, - use_bn=use_bn, - bias=mlp_bias, - ) - self.last_layer = nn.Linear(bottleneck_dim, out_dim, bias=False) - - def init_weights(self) -> None: - self.apply(self._init_weights) - - def _init_weights(self, m): - if isinstance(m, nn.Linear): - trunc_normal_(m.weight, std=0.02) - if m.bias is not None: - nn.init.constant_(m.bias, 0) - - def forward(self, x, no_last_layer=False, only_last_layer=False): - if not only_last_layer: - x = self.mlp(x) - eps = 1e-6 if x.dtype == torch.float16 else 1e-12 - x = nn.functional.normalize(x, dim=-1, p=2, eps=eps) - if not no_last_layer: - x = self.last_layer(x) - return x - - -def _build_mlp(nlayers, in_dim, bottleneck_dim, hidden_dim=None, use_bn=False, bias=True): - if nlayers == 1: - return nn.Linear(in_dim, bottleneck_dim, bias=bias) - else: - layers = [nn.Linear(in_dim, hidden_dim, bias=bias)] - if use_bn: - layers.append(nn.BatchNorm1d(hidden_dim)) - layers.append(nn.GELU()) - for _ in range(nlayers - 2): - layers.append(nn.Linear(hidden_dim, hidden_dim, bias=bias)) - if use_bn: - layers.append(nn.BatchNorm1d(hidden_dim)) - layers.append(nn.GELU()) - layers.append(nn.Linear(hidden_dim, bottleneck_dim, bias=bias)) - return nn.Sequential(*layers) diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/layers/ffn_layers.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/layers/ffn_layers.py deleted file mode 100644 index 749e6be2cd2cd8040bb3233a61a9a306eaea6ca8..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/layers/ffn_layers.py +++ /dev/null @@ -1,77 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -from typing import Callable, List, Optional - -import torch.nn.functional as F -from torch import Tensor, nn - -from dinov3.utils import cat_keep_shapes, uncat_with_shapes - - -class ListForwardMixin(object): - def forward(self, x: Tensor): - raise NotImplementedError - - def forward_list(self, x_list: List[Tensor]) -> List[Tensor]: - x_flat, shapes, num_tokens = cat_keep_shapes(x_list) - x_flat = self.forward(x_flat) - return uncat_with_shapes(x_flat, shapes, num_tokens) - - -class Mlp(nn.Module, ListForwardMixin): - def __init__( - self, - in_features: int, - hidden_features: Optional[int] = None, - out_features: Optional[int] = None, - act_layer: Callable[..., nn.Module] = nn.GELU, - drop: float = 0.0, - bias: bool = True, - device=None, - ) -> None: - super().__init__() - out_features = out_features or in_features - hidden_features = hidden_features or in_features - self.fc1 = nn.Linear(in_features, hidden_features, bias=bias, device=device) - self.act = act_layer() - self.fc2 = nn.Linear(hidden_features, out_features, bias=bias, device=device) - self.drop = nn.Dropout(drop) - - def forward(self, x: Tensor) -> Tensor: - x = self.fc1(x) - x = self.act(x) - x = self.drop(x) - x = self.fc2(x) - x = self.drop(x) - return x - - -class SwiGLUFFN(nn.Module, ListForwardMixin): - def __init__( - self, - in_features: int, - hidden_features: Optional[int] = None, - out_features: Optional[int] = None, - act_layer: Optional[Callable[..., nn.Module]] = None, - drop: float = 0.0, - bias: bool = True, - align_to: int = 8, - device=None, - ) -> None: - super().__init__() - out_features = out_features or in_features - hidden_features = hidden_features or in_features - d = int(hidden_features * 2 / 3) - swiglu_hidden_features = d + (-d % align_to) - self.w1 = nn.Linear(in_features, swiglu_hidden_features, bias=bias, device=device) - self.w2 = nn.Linear(in_features, swiglu_hidden_features, bias=bias, device=device) - self.w3 = nn.Linear(swiglu_hidden_features, out_features, bias=bias, device=device) - - def forward(self, x: Tensor) -> Tensor: - x1 = self.w1(x) - x2 = self.w2(x) - hidden = F.silu(x1) * x2 - return self.w3(hidden) diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/layers/fp8_linear.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/layers/fp8_linear.py deleted file mode 100644 index 5fd88a179ef7b6d638dc211a1e592ac8a726b4e9..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/layers/fp8_linear.py +++ /dev/null @@ -1,141 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import re - -import torch - -from dinov3.layers.attention import LinearKMaskedBias -from dinov3.utils import named_replace - -# avoid division by zero when calculating scale -EPS = 1e-12 - - -def scale(t, amax_t): - max_v = torch.finfo(torch.float8_e4m3fn).max - scale_t = torch.clamp(amax_t.float(), min=EPS) / max_v - t_fp8 = (t / scale_t).to(torch.float8_e4m3fn) - return t_fp8, scale_t - - -def matmul(first, amax_first, second_t, amax_second_t, bias): - first_fp8, scale_first = scale(first, amax_first) - second_t_fp8, scale_second_t = scale(second_t, amax_second_t) - # PyTorch's row-wise scaled matmul kernel is based on CUTLASS and is quite - # slow. Hence we fall back to an "unscaled" matmul, which uses cuBLAS, and - # apply the scale manually afterwards. - output = torch._scaled_mm( - first_fp8, - second_t_fp8.t(), - scale_a=scale_first.new_ones((1, 1)), - scale_b=scale_second_t.t().new_ones((1, 1)), - bias=None, - out_dtype=torch.bfloat16, - use_fast_accum=False, - ) - output = (output * scale_first * scale_second_t.t()).to(torch.bfloat16) - if bias is not None: - output = output + bias - return output - - -@torch.compiler.allow_in_graph -class Fp8LinearFn(torch.autograd.Function): - @staticmethod - def forward(ctx, a, b_t, bias): - amax_a = a.abs().amax(dim=-1, keepdim=True) - amax_b_t = b_t.abs().amax(dim=-1, keepdim=True) - out = matmul(a, amax_a, b_t, amax_b_t, bias) - - ctx.a_requires_grad = a.requires_grad - ctx.b_requires_grad = b_t.requires_grad - ctx.bias_requires_grad = bias.requires_grad if bias is not None else False - - ctx.save_for_backward(a, b_t, amax_b_t.max()) - - return out - - @staticmethod - def backward(ctx, grad_out): - a, b_t, amax_b = ctx.saved_tensors - - if ctx.a_requires_grad: - b = b_t.t().contiguous() - amax_grad_out = grad_out.abs().amax(dim=-1, keepdim=True) - amax_b = amax_b.repeat(b.shape[0], 1) - grad_a = matmul(grad_out, amax_grad_out, b, amax_b, None) - else: - grad_a = None - if ctx.b_requires_grad: - grad_b = grad_out.t() @ a - else: - grad_b = None - if ctx.bias_requires_grad: - grad_bias = grad_out.sum(dim=0) - else: - grad_bias = None - - return grad_a, grad_b, grad_bias - - -class Fp8Linear(torch.nn.Linear): - def forward(self, input: torch.Tensor) -> torch.Tensor: - out = Fp8LinearFn.apply(input.flatten(end_dim=-2), self.weight, self.bias) - out = out.unflatten(0, input.shape[:-1]) - return out - - -class Fp8LinearKMaskedBias(LinearKMaskedBias): - def forward(self, input: torch.Tensor) -> torch.Tensor: - masked_bias = self.bias * self.bias_mask if self.bias is not None else None - out = Fp8LinearFn.apply(input.flatten(end_dim=-2), self.weight, masked_bias) - out = out.unflatten(0, input.shape[:-1]) - return out - - -def convert_linears_to_fp8(root_module: torch.nn.Module, *, filter: str) -> torch.nn.Module: - filter_re = re.compile(filter) - total_count = 0 - - def replace(module: torch.nn.Module, name: str) -> torch.nn.Module: - nonlocal total_count - if not isinstance(module, torch.nn.Linear) or not filter_re.search(name): - return module - if type(module) == torch.nn.Linear: - new_cls = Fp8Linear - elif type(module) == LinearKMaskedBias: - new_cls = Fp8LinearKMaskedBias - else: - assert False, str(type(module)) - if module.in_features % 64 != 0 or module.out_features % 64 != 0: - # This is not a strict requirement, but H100 TensorCores for fp8 - # operate on tiles of 64 elements anyways, and Inductor sometimes - # pads inner dims to become multiples of 64. Also, if one day we - # switch back to cuBLAS, it artificially requires dims to be - # multiples of 16. - raise RuntimeError( - "fp8 requires all dimensions to be multiples of 64 " "(consider using ffn_layer=swiglu64 or higher)" - ) - new_module = new_cls( - in_features=module.in_features, - out_features=module.out_features, - bias=module.bias is not None, - dtype=module.weight.dtype, - device=module.weight.device, - ) - new_module.weight = module.weight - new_module.bias = module.bias - total_count += 1 - return new_module - - out = named_replace(replace, root_module) - assert total_count > 0, "fp8: no layer found to convert" - # Force re-compile everything - torch._dynamo.reset_code_caches() - from torch._inductor.cudagraph_trees import reset_cudagraph_trees - - reset_cudagraph_trees() - return out diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/layers/layer_scale.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/layers/layer_scale.py deleted file mode 100644 index 0b72b7c64c9cc38fd4e3db63e9c90f0158caa36c..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/layers/layer_scale.py +++ /dev/null @@ -1,29 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -from typing import Union - -import torch -from torch import Tensor, nn - - -class LayerScale(nn.Module): - def __init__( - self, - dim: int, - init_values: Union[float, Tensor] = 1e-5, - inplace: bool = False, - device=None, - ) -> None: - super().__init__() - self.inplace = inplace - self.gamma = nn.Parameter(torch.empty(dim, device=device)) - self.init_values = init_values - - def reset_parameters(self): - nn.init.constant_(self.gamma, self.init_values) - - def forward(self, x: Tensor) -> Tensor: - return x.mul_(self.gamma) if self.inplace else x * self.gamma diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/layers/patch_embed.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/layers/patch_embed.py deleted file mode 100644 index 760343f14cd0c1c2bbb2c70d43f82eb0bb1fddf4..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/layers/patch_embed.py +++ /dev/null @@ -1,89 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import math -from typing import Callable, Tuple, Union - -from torch import Tensor, nn - - -def make_2tuple(x): - if isinstance(x, tuple): - assert len(x) == 2 - return x - - assert isinstance(x, int) - return (x, x) - - -class PatchEmbed(nn.Module): - """ - 2D image to patch embedding: (B,C,H,W) -> (B,N,D) - - Args: - img_size: Image size. - patch_size: Patch token size. - in_chans: Number of input image channels. - embed_dim: Number of linear projection output channels. - norm_layer: Normalization layer. - """ - - def __init__( - self, - img_size: Union[int, Tuple[int, int]] = 224, - patch_size: Union[int, Tuple[int, int]] = 16, - in_chans: int = 3, - embed_dim: int = 768, - norm_layer: Callable | None = None, - flatten_embedding: bool = True, - ) -> None: - super().__init__() - - image_HW = make_2tuple(img_size) - patch_HW = make_2tuple(patch_size) - patch_grid_size = ( - image_HW[0] // patch_HW[0], - image_HW[1] // patch_HW[1], - ) - - self.img_size = image_HW - self.patch_size = patch_HW - self.patches_resolution = patch_grid_size - self.num_patches = patch_grid_size[0] * patch_grid_size[1] - - self.in_chans = in_chans - self.embed_dim = embed_dim - - self.flatten_embedding = flatten_embedding - - self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_HW, stride=patch_HW) - self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity() - - def forward(self, x: Tensor) -> Tensor: - _, _, H, W = x.shape - # patch_H, patch_W = self.patch_size - # assert H % patch_H == 0, f"Input image height {H} is not a multiple of patch height {patch_H}" - # assert W % patch_W == 0, f"Input image width {W} is not a multiple of patch width: {patch_W}" - - x = self.proj(x) # B C H W - H, W = x.size(2), x.size(3) - x = x.flatten(2).transpose(1, 2) # B HW C - x = self.norm(x) - if not self.flatten_embedding: - x = x.reshape(-1, H, W, self.embed_dim) # B H W C - return x - - def flops(self) -> float: - Ho, Wo = self.patches_resolution - flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1]) - if self.norm is not None: - flops += Ho * Wo * self.embed_dim - return flops - - def reset_parameters(self): - k = 1 / (self.in_chans * (self.patch_size[0] ** 2)) - nn.init.uniform_(self.proj.weight, -math.sqrt(k), math.sqrt(k)) - if self.proj.bias is not None: - nn.init.uniform_(self.proj.bias, -math.sqrt(k), math.sqrt(k)) diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/layers/rms_norm.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/layers/rms_norm.py deleted file mode 100644 index 1d0a89c47c5e71687cadbf47fef567b2c6a2b3b4..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/layers/rms_norm.py +++ /dev/null @@ -1,24 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import torch -from torch import Tensor, nn - - -class RMSNorm(nn.Module): - def __init__(self, dim: int, eps: float = 1e-5): - super().__init__() - self.weight = nn.Parameter(torch.ones(dim)) - self.eps = eps - - def reset_parameters(self) -> None: - nn.init.constant_(self.weight, 1) - - def _norm(self, x: Tensor) -> Tensor: - return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps) - - def forward(self, x: Tensor) -> Tensor: - output = self._norm(x.float()).type_as(x) - return output * self.weight diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/layers/rope_position_encoding.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/layers/rope_position_encoding.py deleted file mode 100644 index 2635d09e7732fb4c146f57d3aef19c2d3e5668ec..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/layers/rope_position_encoding.py +++ /dev/null @@ -1,121 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import math -from typing import Literal - -import numpy as np -import torch -from torch import Tensor, nn - - -# RoPE positional embedding with no mixing of coordinates (axial) and no learnable weights -# Supports two parametrizations of the rope parameters: either using `base` or `min_period` and `max_period`. -class RopePositionEmbedding(nn.Module): - def __init__( - self, - embed_dim: int, - *, - num_heads: int, - base: float | None = 100.0, - min_period: float | None = None, - max_period: float | None = None, - normalize_coords: Literal["min", "max", "separate"] = "separate", - shift_coords: float | None = None, - jitter_coords: float | None = None, - rescale_coords: float | None = None, - dtype: torch.dtype | None = None, - device: torch.device | None = None, - ): - super().__init__() - assert embed_dim % (4 * num_heads) == 0 - both_periods = min_period is not None and max_period is not None - if (base is None and not both_periods) or (base is not None and both_periods): - raise ValueError("Either `base` or `min_period`+`max_period` must be provided.") - - D_head = embed_dim // num_heads - self.base = base - self.min_period = min_period - self.max_period = max_period - self.D_head = D_head - self.normalize_coords = normalize_coords - self.shift_coords = shift_coords - self.jitter_coords = jitter_coords - self.rescale_coords = rescale_coords - - # Needs persistent=True because we do teacher.load_state_dict(student.state_dict()) to initialize the teacher - self.dtype = dtype # Don't rely on self.periods.dtype - self.register_buffer( - "periods", - torch.empty(D_head // 4, device=device, dtype=dtype), - persistent=True, - ) - self._init_weights() - - def forward(self, *, H: int, W: int) -> tuple[Tensor, Tensor]: - device = self.periods.device - dtype = self.dtype - dd = {"device": device, "dtype": dtype} - - # Prepare coords in range [-1, +1] - if self.normalize_coords == "max": - max_HW = max(H, W) - coords_h = torch.arange(0.5, H, **dd) / max_HW # [H] - coords_w = torch.arange(0.5, W, **dd) / max_HW # [W] - elif self.normalize_coords == "min": - min_HW = min(H, W) - coords_h = torch.arange(0.5, H, **dd) / min_HW # [H] - coords_w = torch.arange(0.5, W, **dd) / min_HW # [W] - elif self.normalize_coords == "separate": - coords_h = torch.arange(0.5, H, **dd) / H # [H] - coords_w = torch.arange(0.5, W, **dd) / W # [W] - else: - raise ValueError(f"Unknown normalize_coords: {self.normalize_coords}") - coords = torch.stack(torch.meshgrid(coords_h, coords_w, indexing="ij"), dim=-1) # [H, W, 2] - coords = coords.flatten(0, 1) # [HW, 2] - coords = 2.0 * coords - 1.0 # Shift range [0, 1] to [-1, +1] - - # Shift coords by adding a uniform value in [-shift, shift] - if self.training and self.shift_coords is not None: - shift_hw = torch.empty(2, **dd).uniform_(-self.shift_coords, self.shift_coords) - coords += shift_hw[None, :] - - # Jitter coords by multiplying the range [-1, 1] by a log-uniform value in [1/jitter, jitter] - if self.training and self.jitter_coords is not None: - jitter_max = np.log(self.jitter_coords) - jitter_min = -jitter_max - jitter_hw = torch.empty(2, **dd).uniform_(jitter_min, jitter_max).exp() - coords *= jitter_hw[None, :] - - # Rescale coords by multiplying the range [-1, 1] by a log-uniform value in [1/rescale, rescale] - if self.training and self.rescale_coords is not None: - rescale_max = np.log(self.rescale_coords) - rescale_min = -rescale_max - rescale_hw = torch.empty(1, **dd).uniform_(rescale_min, rescale_max).exp() - coords *= rescale_hw - - # Prepare angles and sin/cos - angles = 2 * math.pi * coords[:, :, None] / self.periods[None, None, :] # [HW, 2, D//4] - angles = angles.flatten(1, 2) # [HW, D//2] - angles = angles.tile(2) # [HW, D] - cos = torch.cos(angles) # [HW, D] - sin = torch.sin(angles) # [HW, D] - - return (sin, cos) # 2 * [HW, D] - - def _init_weights(self): - device = self.periods.device - dtype = self.dtype - if self.base is not None: - periods = self.base ** ( - 2 * torch.arange(self.D_head // 4, device=device, dtype=dtype) / (self.D_head // 2) - ) # [D//4] - else: - base = self.max_period / self.min_period - exponents = torch.linspace(0, 1, self.D_head // 4, device=device, dtype=dtype) # [D//4] range [0, 1] - periods = base**exponents # range [1, max_period / min_period] - periods = periods / base # range [min_period / max_period, 1] - periods = periods * self.max_period # range [min_period, max_period] - self.periods.data = periods diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/layers/sparse_linear.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/layers/sparse_linear.py deleted file mode 100644 index fbb9e103182d10ed94a2dfa13e58060516ff3dbe..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/layers/sparse_linear.py +++ /dev/null @@ -1,90 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import logging -from typing import Callable - -import torch -import torch.nn as nn -import torch.nn.functional as F -import xformers.ops as xops - -from dinov3.utils import named_apply, named_replace - -logger = logging.getLogger("dinov3") - - -class LinearW24(torch.nn.Linear): - ALGO = "largest_abs_values_greedy" - - def __init__(self, *args, **kwargs) -> None: - super().__init__(*args, **kwargs) - self.sparsity_enabled = False - - def forward(self, input: torch.Tensor) -> torch.Tensor: - if not self.sparsity_enabled: - return super().forward(input) - - input_shape = input.shape - input = input.flatten(end_dim=-2) - dim0 = input.shape[0] - if dim0 % 8 != 0: - # NOTE: This should be torch-compiled away - input = F.pad(input, [0, 0, 0, -dim0 % 8]) - w_sparse = xops.sparsify24( - self.weight, - algo=self.ALGO, - gradient="ste", - backend="cusparselt", - ) - return F.linear(input, w_sparse, self.bias,)[ - :dim0 - ].unflatten(dim=0, sizes=input_shape[:-1]) - - -def replace_linears_with_sparse_linear(root_module: nn.Module, *, filter_fn: Callable[[str], bool]) -> nn.Module: - total_count = 0 - - def replace(module: nn.Module, name: str) -> nn.Module: - nonlocal total_count - if not isinstance(module, nn.Linear) or not filter_fn(name): - return module - assert type(module) == nn.Linear, "Subtypes not supported" - new_module = LinearW24( - in_features=module.in_features, - out_features=module.out_features, - bias=module.bias is not None, - dtype=module.weight.dtype, - device=module.weight.device, - ) - new_module.weight = module.weight - new_module.bias = module.bias - total_count += 1 - return new_module - - out = named_replace(replace, root_module) - assert total_count > 0, "2:4 sparsity: no layer found to sparsify" - return out - - -def update_24sparsity(root_module: nn.Module, enabled: bool) -> int: - num_modified = 0 - - def maybe_apply_sparsity(module: nn.Module, name: str) -> nn.Module: - nonlocal num_modified - if not isinstance(module, LinearW24): - return module - num_modified += 1 - module.sparsity_enabled = enabled - logger.info(f"- {'' if module.sparsity_enabled else 'de'}sparsifying {name}") - return module - - named_apply(maybe_apply_sparsity, root_module) - # Force re-compile everything - torch._dynamo.reset_code_caches() - from torch._inductor.cudagraph_trees import reset_cudagraph_trees - - reset_cudagraph_trees() - return num_modified diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/logging/__init__.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/logging/__init__.py deleted file mode 100644 index 118925070ffda3ae73b6c15b0549a772f07d063d..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/logging/__init__.py +++ /dev/null @@ -1,180 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import functools -import logging -import os -import sys -from typing import Optional - -from termcolor import colored - -from dinov3.distributed import TorchDistributedEnvironment - -from dinov3.logging.helpers import MetricLogger, SmoothedValue - -_LEVEL_COLORED_KWARGS = { - logging.DEBUG: {"color": "green", "attrs": ["bold"]}, - logging.INFO: {"color": "green"}, - logging.WARNING: {"color": "yellow"}, - logging.ERROR: {"color": "red"}, - logging.CRITICAL: {"color": "red", "attrs": ["bold"]}, -} - - -class _LevelColoredFormatter(logging.Formatter): - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) - - def formatMessage(self, record): - log = super().formatMessage(record) - - colored_kwargs = _LEVEL_COLORED_KWARGS.get(record.levelno) - if colored_kwargs is None: - return log - - msg = record.msg % record.args if record.msg == "%s" else record.msg - index = log.rfind(msg, len(log) - len(msg)) - # Can happen in some cases, like if the msg contains `%s` which - # have been replaced in `formatMessage`. Fallback to no colors - if index == -1: - return log - prefix = log[:index] - prefix = colored(prefix, **colored_kwargs) - return prefix + msg - - -# So that calling _configure_logger multiple times won't add many handlers -@functools.lru_cache() -def _configure_logger( - name: Optional[str] = None, - *, - level: int = logging.DEBUG, - output: Optional[str] = None, - color: bool = True, - log_to_stdout_only_in_main_process: bool = True, -): - """ - Configure a logger. - - Adapted from Detectron2. - - Args: - name: The name of the logger to configure. - level: The logging level to use. - output: A file name or a directory to save log. If None, will not save log file. - If ends with ".txt" or ".log", assumed to be a file name. - Otherwise, logs will be saved to `output/log.txt`. - color: Whether stdout output should be colored (ignored if stdout is not a terminal). - log_to_stdout_only_in_main_process: The main process (rank 0) always logs to stdout, - regardless of this flag. If False, other ranks will also log to their stdout. - - Returns: - The configured logger. - """ - - # Disable colored output if the stdout is not a terminal - color = color and os.isatty(sys.stdout.fileno()) - - logger = logging.getLogger(name) - logger.setLevel(level) - logger.propagate = False - - # Loosely match Google glog format: - # [IWEF]yyyymmdd hh:mm:ss.uuuuuu threadid file:line] msg - # but use a shorter timestamp and include the logger name: - # [IWEF]yyyymmdd hh:mm:ss logger threadid file:line] msg - fmt_prefix = "%(levelname).1s%(asctime)s %(process)s %(name)s %(filename)s:%(lineno)s] " - fmt_message = "%(message)s" - fmt = fmt_prefix + fmt_message - datefmt = "%Y%m%d %H:%M:%S" - plain_formatter = logging.Formatter(fmt=fmt, datefmt=datefmt) - - torch_env = TorchDistributedEnvironment() - - # rank 0 always logs to stdout, for other ranks it depends on log_to_stdout_only_in_main_process - should_log_to_stdout = torch_env.is_main_process or not log_to_stdout_only_in_main_process - if should_log_to_stdout: - handler = logging.StreamHandler(stream=sys.stdout) - handler.setLevel(logging.DEBUG) - - formatter: logging.Formatter - if color: - formatter = _LevelColoredFormatter( - fmt=fmt, - datefmt=datefmt, - ) - else: - formatter = plain_formatter - - handler.setFormatter(formatter) - logger.addHandler(handler) - - # file logging for all workers - if output: - if os.path.splitext(output)[-1] in (".txt", ".log"): - filename = output - else: - filename = os.path.join(output, "logs", "log.txt") - - if not torch_env.is_main_process: - filename = filename + f".rank{torch_env.rank}" - - os.makedirs(os.path.dirname(filename), exist_ok=True) - - handler = logging.StreamHandler(open(filename, "a")) - handler.setLevel(logging.DEBUG) - handler.setFormatter(plain_formatter) - logger.addHandler(handler) - - logger.debug(f"PyTorch distributed environment: {torch_env}") - return logger - - -def setup_logging( - output: Optional[str] = None, - *, - name: Optional[str] = None, - level: int = logging.DEBUG, - color: bool = True, - capture_warnings: bool = True, - log_to_stdout_only_in_main_process: bool = True, -) -> None: - """ - Setup logging. - - Args: - output: A file name or a directory to save log files. If None, log - files will not be saved. If output ends with ".txt" or ".log", it - is assumed to be a file name. - Otherwise, logs will be saved to `output/log.txt`. - name: The name of the logger to configure, by default the root logger. - level: The logging level to use. - color: Whether stdout output should be colored (ignored if stdout is not a terminal). - capture_warnings: Whether warnings should be captured as logs. - log_to_stdout_only_in_main_process: The main process (rank 0) always logs to stdout, - regardless of this flag. If False, other ranks will also log to their stdout. - """ - logging.captureWarnings(capture_warnings) - # Ensure the path is canonical to properly use the cache of `_configure_logger` - output = output if output is None else os.path.realpath(output) - _configure_logger( - name, - level=level, - output=output, - color=color, - log_to_stdout_only_in_main_process=log_to_stdout_only_in_main_process, - ) - - -def cleanup_logging(*, name: Optional[str] = None) -> None: - logger = logging.getLogger(name) - for handler in logger.handlers: - handler.flush() - handler.close() - logger.removeHandler(handler) - - # clears the cache of `_configure_logger` to allow re-initialization - _configure_logger.cache_clear() diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/logging/__pycache__/__init__.cpython-310.pyc b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/logging/__pycache__/__init__.cpython-310.pyc deleted file mode 100644 index eba24c2b230f2dc6f820cc32651b3fad9cb33717..0000000000000000000000000000000000000000 Binary files a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/logging/__pycache__/__init__.cpython-310.pyc and /dev/null differ diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/logging/__pycache__/helpers.cpython-310.pyc b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/logging/__pycache__/helpers.cpython-310.pyc deleted file mode 100644 index 721afbbfd382b6aca7c91f76ee0969f4b0ac7d06..0000000000000000000000000000000000000000 Binary files a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/logging/__pycache__/helpers.cpython-310.pyc and /dev/null differ diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/logging/helpers.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/logging/helpers.py deleted file mode 100644 index b01e1f8352a4235ef1707ebb0eddbc6d80d729ef..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/logging/helpers.py +++ /dev/null @@ -1,203 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import datetime -import json -import logging -import time -from collections import defaultdict, deque - -import torch - -import dinov3.distributed as distributed - -logger = logging.getLogger("dinov3") - - -class MetricLogger(object): - def __init__(self, delimiter="\t", output_file=None): - self.meters = defaultdict(SmoothedValue) - self.delimiter = delimiter - self.output_file = output_file - - def update(self, **kwargs): - for k, v in kwargs.items(): - if isinstance(v, torch.Tensor): - v = v.item() - assert isinstance(v, (float, int)) - self.meters[k].update(v) - - def __getattr__(self, attr): - if attr in self.meters: - return self.meters[attr] - if attr in self.__dict__: - return self.__dict__[attr] - raise AttributeError("'{}' object has no attribute '{}'".format(type(self).__name__, attr)) - - def __str__(self): - loss_str = [] - for name, meter in self.meters.items(): - loss_str.append("{}: {}".format(name, str(meter))) - return self.delimiter.join(loss_str) - - def synchronize_between_processes(self): - for meter in self.meters.values(): - meter.synchronize_between_processes() - - def add_meter(self, name, meter): - self.meters[name] = meter - - def dump_in_output_file(self, iteration, iter_time, data_time): - if self.output_file is None or not distributed.is_main_process(): - return - dict_to_dump = dict( - iteration=iteration, - iter_time=iter_time, - data_time=data_time, - ) - dict_to_dump.update({k: v.median for k, v in self.meters.items()}) - with open(self.output_file, "a") as f: - f.write(json.dumps(dict_to_dump) + "\n") - pass - - def log_every(self, iterable, print_freq, header=None, n_iterations=None, start_iteration=0): - i = start_iteration - if not header: - header = "" - start_time = time.time() - end = time.time() - iter_time = SmoothedValue(fmt="{avg:.6f}") - data_time = SmoothedValue(fmt="{avg:.6f}") - - if n_iterations is None: - n_iterations = len(iterable) - - space_fmt = ":" + str(len(str(n_iterations))) + "d" - - log_list = [ - header, - "[{0" + space_fmt + "}/{1}]", - "eta: {eta}", - "{meters}", - "time: {time}", - "data: {data}", - ] - if torch.cuda.is_available(): - log_list += ["mem: {current_memory:.0f}"] - log_list += ["(max mem: {max_memory:.0f})"] - - log_msg = self.delimiter.join(log_list) - MB = 1024.0 * 1024.0 - for obj in iterable: - if i >= n_iterations: - break - - data_time.update(time.time() - end) - yield obj - iter_time.update(time.time() - end) - if i % print_freq == 0 or i == n_iterations - 1: - self.dump_in_output_file(iteration=i, iter_time=iter_time.avg, data_time=data_time.avg) - eta_seconds = iter_time.global_avg * (n_iterations - i) - eta_string = str(datetime.timedelta(seconds=int(eta_seconds))) - if torch.cuda.is_available(): - logger.info( - log_msg.format( - i, - n_iterations, - eta=eta_string, - meters=str(self), - time=str(iter_time), - data=str(data_time), - current_memory=torch.cuda.memory_allocated() / MB, - max_memory=torch.cuda.max_memory_allocated() / MB, - ) - ) - else: - logger.info( - log_msg.format( - i, - n_iterations, - eta=eta_string, - meters=str(self), - time=str(iter_time), - data=str(data_time), - ) - ) - i += 1 - end = time.time() - total_time = time.time() - start_time - total_time_str = str(datetime.timedelta(seconds=int(total_time))) - s_it = total_time / n_iterations if n_iterations > 0 else 0 - logger.info("{} Total time: {} ({:.6f} s / it)".format(header, total_time_str, s_it)) - - -class SmoothedValue: - """Track a series of values and provide access to smoothed values over a - window or the global series average. - """ - - def __init__(self, window_size=20, fmt=None): - if fmt is None: - fmt = "{median:.4f} ({global_avg:.4f})" - self.deque = deque(maxlen=window_size) - self.total = 0.0 - self.count = 0 - self.fmt = fmt - - def update(self, value, num=1): - self.deque.append(value) - self.count += num - self.total += value * num - - def synchronize_between_processes(self): - """ - Distributed synchronization of the metric - Warning: does not synchronize the deque! - """ - if not distributed.is_enabled(): - return - t = torch.tensor([self.count, self.total], dtype=torch.float64, device="cuda") - torch.distributed.barrier() - torch.distributed.all_reduce(t) - t = t.tolist() - self.count = int(t[0]) - self.total = t[1] - - @property - def median(self): - d = torch.tensor(list(self.deque)) - return d.median().item() # returns float("nan") when d is empty - - @property - def avg(self): - d = torch.tensor(list(self.deque), dtype=torch.float32) - return d.mean().item() # returns float("nan") when d is empty - - @property - def global_avg(self): - if self.count == 0: - return float("nan") - return self.total / self.count - - @property - def max(self): - if len(self.deque) == 0: - return float("nan") - return max(self.deque) - - @property - def value(self): - if len(self.deque) == 0: - return float("nan") - return self.deque[-1] - - def __str__(self): - return self.fmt.format( - median=self.median, - avg=self.avg, - global_avg=self.global_avg, - max=self.max, - value=self.value, - ) diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/loss/__init__.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/loss/__init__.py deleted file mode 100644 index 9f52a54a7da4f5a48c22b71008136f4e6f6c3a92..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/loss/__init__.py +++ /dev/null @@ -1,9 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -from .dino_clstoken_loss import DINOLoss -from .gram_loss import GramLoss -from .ibot_patch_loss import iBOTPatchLoss -from .koleo_loss import KoLeoLoss, KoLeoLossDistributed diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/loss/dino_clstoken_loss.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/loss/dino_clstoken_loss.py deleted file mode 100644 index 40bc68d91b67e42090d6d1ff61569bcdab0f0a24..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/loss/dino_clstoken_loss.py +++ /dev/null @@ -1,124 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import math - -import torch -import torch.distributed as dist -import torch.nn.functional as F -from torch import nn - -from dinov3.distributed import get_process_subgroup, get_subgroup_size - - -class DINOLoss(nn.Module): - def __init__( - self, - out_dim, - student_temp=0.1, - center_momentum=0.9, - ): - super().__init__() - self.student_temp = student_temp - self.center_momentum = center_momentum - self.register_buffer("center", torch.full((1, out_dim), math.nan)) - self.updated = True - self.reduce_handle = None - self.len_teacher_output = None - self.async_batch_center = None - - def init_weights(self) -> None: - self.center.zero_() - - @torch.no_grad() - def softmax_center_teacher(self, teacher_output, teacher_temp, update_centers=True): - if update_centers: - self.apply_center_update() - # teacher centering and sharpening - return F.softmax((teacher_output - self.center) / teacher_temp, dim=-1) - - @torch.no_grad() - def sinkhorn_knopp_teacher(self, teacher_output, teacher_temp, n_iterations=3): - # teacher_output: [batch, prototypes] - teacher_output = teacher_output.float() - world_size = get_subgroup_size() if dist.is_initialized() else 1 - Q = torch.exp(teacher_output / teacher_temp).t() # Q is K-by-B for consistency with notations from our paper - B = Q.shape[1] * world_size # number of samples to assign - K = Q.shape[0] # how many prototypes - - # make the matrix sums to 1 - sum_Q = torch.sum(Q) - if dist.is_initialized(): - dist.all_reduce(sum_Q, group=get_process_subgroup()) - Q /= sum_Q - - for _ in range(n_iterations): - # normalize each row: total weight per prototype must be 1/K - sum_of_rows = torch.sum(Q, dim=1, keepdim=True) - if dist.is_initialized(): - dist.all_reduce(sum_of_rows, group=get_process_subgroup()) - Q /= sum_of_rows - Q /= K - - # normalize each column: total weight per sample must be 1/B - Q /= torch.sum(Q, dim=0, keepdim=True) - Q /= B - - Q *= B # the colomns must sum to 1 so that Q is an assignment - return Q.t() - - def forward(self, student_logits, teacher_probs, ignore_diagonal=False): - """ - Cross-entropy between softmax outputs of the teacher and student networks. - student_logits: [student crops, batch, prototypes] - teacher_probs: [teacher crops, batch, prototypes] must sum to 1 over the last dim - - loss = 0 - count = 0 - for each sample `b` in the batch: - for each student crop `s` of this sample: - for each teacher crop `t` of this sample: - if ignore_diagonal and s == t: - continue - loss += cross_entropy(softmax(student_logits[s, b] / student_temp), teacher_probs[t, b]) - count += 1 - return loss / count - """ - student_crops, B, K = student_logits.shape - teacher_crops, _, _ = teacher_probs.shape - student_logits = F.log_softmax(student_logits.float() / self.student_temp, dim=-1) - if not ignore_diagonal: - loss = -torch.einsum("s b k, t b k -> ", student_logits, teacher_probs) - return loss / (B * student_crops * teacher_crops) - else: - loss = -torch.einsum("s b k, t b k -> s t", student_logits, teacher_probs) - min_st = min(student_crops, teacher_crops) - loss = torch.diagonal_scatter(loss, loss.new_zeros(min_st)) - return loss.sum() / (B * student_crops * teacher_crops - B * min_st) - - @torch.no_grad() - def update_center(self, teacher_output): - self.reduce_center_update(teacher_output) - - @torch.no_grad() - def reduce_center_update(self, teacher_output): - self.updated = False - self.len_teacher_output = len(teacher_output) - self.async_batch_center = torch.sum(teacher_output, dim=0, keepdim=True) - if dist.is_initialized(): - self.reduce_handle = dist.all_reduce(self.async_batch_center, async_op=True, group=get_process_subgroup()) - - @torch.no_grad() - def apply_center_update(self): - if self.updated is False: - world_size = get_subgroup_size() if dist.is_initialized() else 1 - - if self.reduce_handle is not None: - self.reduce_handle.wait() - _t = self.async_batch_center / (self.len_teacher_output * world_size) - - self.center = self.center * self.center_momentum + _t * (1 - self.center_momentum) - - self.updated = True diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/loss/gram_loss.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/loss/gram_loss.py deleted file mode 100644 index 4c2f0ac8726a137b0e963aba2a0f6bb1d19c3e6b..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/loss/gram_loss.py +++ /dev/null @@ -1,84 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import torch -import torch.nn as nn -import torch.nn.functional as F - - -class GramLoss(nn.Module): - """Implementation of the gram loss""" - - def __init__( - self, - apply_norm=True, - img_level=True, - remove_neg=True, - remove_only_teacher_neg=False, - ): - super().__init__() - - # Loss - self.mse_loss = torch.nn.MSELoss() - - # Parameters - self.apply_norm = apply_norm - self.remove_neg = remove_neg - self.remove_only_teacher_neg = remove_only_teacher_neg - - if self.remove_neg or self.remove_only_teacher_neg: - assert self.remove_neg != self.remove_only_teacher_neg - - def forward(self, output_feats, target_feats, img_level=True): - """Compute the MSE loss between the gram matrix of the input and target features. - - Args: - output_feats: Pytorch tensor (B, N, dim) or (B*N, dim) if img_level == False - target_feats: Pytorch tensor (B, N, dim) or (B*N, dim) if img_level == False - img_level: bool, if true gram computed at the image level only else over the entire batch - Returns: - loss: scalar - """ - - # Dimensions of the tensor should be (B, N, dim) - if img_level: - assert len(target_feats.shape) == 3 and len(output_feats.shape) == 3 - - # Float casting - output_feats = output_feats.float() - target_feats = target_feats.float() - - # SSL correlation - if self.apply_norm: - target_feats = F.normalize(target_feats, dim=-1) - - if not img_level and len(target_feats.shape) == 3: - # Flatten (B, N, D) into (B*N, D) - target_feats = target_feats.flatten(0, 1) - - # Compute similarities - target_sim = torch.matmul(target_feats, target_feats.transpose(-1, -2)) - - # Patch correlation - if self.apply_norm: - output_feats = F.normalize(output_feats, dim=-1) - - if not img_level and len(output_feats.shape) == 3: - # Flatten (B, N, D) into (B*N, D) - output_feats = output_feats.flatten(0, 1) - - # Compute similarities - student_sim = torch.matmul(output_feats, output_feats.transpose(-1, -2)) - - if self.remove_neg: - target_sim[target_sim < 0] = 0.0 - student_sim[student_sim < 0] = 0.0 - - elif self.remove_only_teacher_neg: - # Remove only the negative sim values of the teacher - target_sim[target_sim < 0] = 0.0 - student_sim[(student_sim < 0) & (target_sim < 0)] = 0.0 - - return self.mse_loss(student_sim, target_sim) diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/loss/ibot_patch_loss.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/loss/ibot_patch_loss.py deleted file mode 100644 index 5412f0abf1675cb2f4d945bb4ef9e30ba32061c2..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/loss/ibot_patch_loss.py +++ /dev/null @@ -1,142 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import math - -import torch -import torch.distributed as dist -import torch.nn.functional as F -from torch import nn - -from dinov3.distributed import get_process_subgroup, get_subgroup_size - - -def lossfunc(t, s, temp): # noqa: F811 - return torch.sum(t.float() * F.log_softmax(s.float() / temp, dim=-1), dim=-1) - - -class SinkhornKnoppTeacher(nn.Module): - """ - NOTE: This is a module and not a function in the `iBOTPatchLoss` class - This is because we want to torch.compile it, and torch.compil-ing a single - function with the `@torch.compile` decorator is bad. - It's better to `module.compile()` it, as we can control when we enable or - disable compilation globally. - """ - - @torch.no_grad() - def forward(self, teacher_output, teacher_temp, n_masked_patches_tensor, n_iterations=3): - teacher_output = teacher_output.float() - # world_size = dist.get_world_size() if dist.is_initialized() else 1 - Q = torch.exp(teacher_output / teacher_temp).t() # Q is K-by-B for consistency with notations from our paper - # B = Q.shape[1] * world_size # number of samples to assign - B = n_masked_patches_tensor - dist.all_reduce(B, group=get_process_subgroup()) - K = Q.shape[0] # how many prototypes - - # make the matrix sums to 1 - sum_Q = torch.sum(Q) - if dist.is_initialized(): - dist.all_reduce(sum_Q, group=get_process_subgroup()) - Q /= sum_Q - - for _ in range(n_iterations): - # normalize each row: total weight per prototype must be 1/K - sum_of_rows = torch.sum(Q, dim=1, keepdim=True) - if dist.is_initialized(): - dist.all_reduce(sum_of_rows, group=get_process_subgroup()) - Q /= sum_of_rows - Q /= K - - # normalize each column: total weight per sample must be 1/B - Q /= torch.sum(Q, dim=0, keepdim=True) - Q /= B - - Q *= B # the colomns must sum to 1 so that Q is an assignment - return Q.t() - - -class iBOTPatchLoss(nn.Module): - def __init__(self, patch_out_dim, student_temp=0.1, center_momentum=0.9): - super().__init__() - self.student_temp = student_temp - self.center_momentum = center_momentum - self.register_buffer("center", torch.full((1, 1, patch_out_dim), math.nan)) - self.updated = True - self.reduce_handle = None - self.len_teacher_patch_tokens = None - self.async_batch_center = None - self.sinkhorn_knopp_teacher = SinkhornKnoppTeacher() - self.sinkhorn_knopp_teacher.compile() - - def init_weights(self) -> None: - self.center.zero_() - - @torch.no_grad() - def softmax_center_teacher(self, teacher_patch_tokens, teacher_temp, update_centers=True): - if update_centers: - self.apply_center_update() - return F.softmax((teacher_patch_tokens - self.center) / teacher_temp, dim=-1) - - def forward(self, student_patch_tokens, teacher_patch_tokens, student_masks_flat): - """ - Cross-entropy between softmax outputs of the teacher and student networks. - student_patch_tokens: (B, N, D) tensor - teacher_patch_tokens: (B, N, D) tensor - student_masks_flat: (B, N) tensor - """ - t = teacher_patch_tokens - s = student_patch_tokens - loss = lossfunc(t, s, self.student_temp) - loss = torch.sum(loss * student_masks_flat.float(), dim=-1) / student_masks_flat.sum(dim=-1).clamp(min=1.0) - return -loss.mean() - - def forward_masked( - self, - student_patch_tokens_masked, - teacher_patch_tokens_masked, - student_masks_flat, - n_masked_patches=None, - masks_weight=None, - ): - t = teacher_patch_tokens_masked - s = student_patch_tokens_masked - # loss = torch.sum(t * F.log_softmax(s / self.student_temp, dim=-1), dim=-1) - loss = lossfunc(t, s, self.student_temp) - if masks_weight is None: - masks_weight = ( - (1 / student_masks_flat.sum(-1).clamp(min=1.0)) - .unsqueeze(-1) - .expand_as(student_masks_flat)[student_masks_flat] - ) - if n_masked_patches is not None: - loss = loss[:n_masked_patches] - loss = loss * masks_weight - return -loss.sum() / student_masks_flat.shape[0] - - @torch.no_grad() - def update_center(self, teacher_patch_tokens): - self.reduce_center_update(teacher_patch_tokens) - - @torch.no_grad() - def reduce_center_update(self, teacher_patch_tokens): - self.updated = False - self.len_teacher_patch_tokens = len(teacher_patch_tokens) - self.async_batch_center = torch.sum(teacher_patch_tokens.mean(1), dim=0, keepdim=True) - if dist.is_initialized(): - self.reduce_handle = dist.all_reduce(self.async_batch_center, async_op=True, group=get_process_subgroup()) - - @torch.no_grad() - def apply_center_update(self): - if self.updated is False: - world_size = get_subgroup_size() if dist.is_initialized() else 1 - - if self.reduce_handle is not None: - self.reduce_handle.wait() - _t = self.async_batch_center / (self.len_teacher_patch_tokens * world_size) - - self.center = self.center * self.center_momentum + _t * (1 - self.center_momentum) - - self.updated = True diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/loss/koleo_loss.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/loss/koleo_loss.py deleted file mode 100644 index dee9194dbb0533b01a2012af6841606b6186847f..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/loss/koleo_loss.py +++ /dev/null @@ -1,113 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import torch -import torch.distributed as torch_dist -import torch.nn as nn -import torch.nn.functional as F - -import dinov3.distributed as dist - - -class KoLeoLoss(nn.Module): - """Kozachenko-Leonenko entropic loss regularizer from Sablayrolles et al. - 2018 - Spreading vectors for similarity search""" - - def __init__(self): - super().__init__() - self.pdist = nn.PairwiseDistance(2, eps=1e-8) - - def pairwise_NNs_inner(self, x): - """ - Pairwise nearest neighbors for L2-normalized vectors. - Uses Torch rather than Faiss to remain on GPU. - """ - # parwise dot products (= inverse distance) - dots = torch.mm(x, x.t()) - n = x.shape[0] - dots.view(-1)[:: (n + 1)].fill_(-1) # Trick to fill diagonal with -1 - _, indices = torch.max(dots, dim=1) # max inner prod -> min distance - return indices - - def forward(self, student_output, eps=1e-8): - """ - Args: - student_output (BxD): backbone output of student - """ - with torch.autocast("cuda", enabled=False): - student_output = F.normalize(student_output, eps=eps, p=2, dim=-1) - indices = self.pairwise_NNs_inner(student_output) - distances = self.pdist(student_output, student_output[indices]) # BxD, BxD -> B - loss = -torch.log(distances + eps).mean() - return loss - - -class KoLeoLossDistributed(nn.Module): - """Kozachenko-Leonenko entropic loss regularizer from Sablayrolles et al. - 2018 - Spreading vectors for similarity search""" - - def __init__(self, topk=1, loss_group_size: int | None = None): - super().__init__() - self.pdist = nn.PairwiseDistance(2, eps=1e-8) - self.topk = topk - self.loss_group_size = loss_group_size # Size of the nearest neighbor set. If None, uses global batch size. - - def pairwise_NNs_inner(self, x, all_x, rank): - """ - Pairwise nearest neighbors for L2-normalized vectors. - Uses Torch rather than Faiss to remain on GPU. - """ - # parwise dot products (= inverse distance) - dots = torch.mm(x, all_x.t()) # local_B x global_B - local_B, global_B = dots.shape - dots.view(-1)[rank * local_B :: (global_B + 1)].fill_(-1) # Trick to fill diagonal with -1 - _, indices = torch.topk(dots, dim=1, k=self.topk) # max inner prod -> min distance - return indices - - def forward(self, student_output, eps=1e-8): - """ - Args: - student_output (BxD): backbone output of student - """ - with torch.autocast("cuda", enabled=False): - student_output = F.normalize(student_output, eps=eps, p=2, dim=-1) # local_B x D - - if dist.is_enabled(): - all_student_outputs = torch.cat(torch_dist.nn.all_gather(student_output), dim=0) # global_B x D - world_size = dist.get_world_size() - rank = dist.get_rank() - else: - all_student_outputs = student_output - world_size = 1 - rank = 0 - - # Group the global batch into groups of size `loss_group_size` and use the features of the group - # the local rank falls into as the nearest neighbor set for the local rank - local_B = len(student_output) - global_B = len(all_student_outputs) - loss_group_size = self.loss_group_size if self.loss_group_size is not None else global_B - if loss_group_size % local_B != 0: - raise ValueError( - f"Loss group size size {loss_group_size} must be a multiple of local batch size {local_B}." - ) - if global_B % loss_group_size != 0: - raise ValueError( - f"Global batch size {global_B} must be divisible by loss group size {loss_group_size}." - ) - n_groups = global_B // loss_group_size - ranks_per_group = world_size // n_groups - rank_in_group = rank % ranks_per_group - group = rank // ranks_per_group - all_student_outputs = all_student_outputs.view(n_groups, loss_group_size, student_output.shape[1]) - all_student_outputs = all_student_outputs[group] # loss_group_size x D - - with torch.no_grad(): - indices = self.pairwise_NNs_inner(student_output, all_student_outputs, rank_in_group) # local_B x topk - - student_output_expanded = ( - student_output.unsqueeze(1).repeat(1, self.topk, 1).flatten(0, 1) - ) # (local_B * topk) x D - distances = self.pdist(student_output_expanded, all_student_outputs[indices].flatten(0, 1)) # BxD, BxD -> B - loss = -torch.log(distances.float() + eps).mean() - - return loss diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/models/__init__.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/models/__init__.py deleted file mode 100644 index b5427e2d3d729a3ac43af96cd48fd7ffeb33a868..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/models/__init__.py +++ /dev/null @@ -1,124 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import logging -from pathlib import Path - -from typing import Union - -import torch -import torch.nn as nn - -from dinov3.layers.fp8_linear import convert_linears_to_fp8 - -from . import vision_transformer as vits - -logger = logging.getLogger("dinov3") - - -def init_fp8(model: nn.Module, args) -> nn.Module: - if not args.fp8_enabled: - logger.info("fp8 matmuls: OFF (disabled in config)") - return model - logger.info("fp8 matmuls: ON") - # Multi-kernel makes Inductor auto-tune between a regular "streaming"-based - # reduction kernel and a "persistent" reduction kernel. Since fp8 has some - # multi-pass steps (e.g., first get amax, then scale), persistent kernels - # should perform better. - torch._inductor.config.triton.multi_kernel = 1 - return convert_linears_to_fp8(model, filter=args.fp8_filter) - - -def build_model(args, only_teacher=False, img_size=224, device=None): - if "vit" in args.arch: - vit_kwargs = dict( - img_size=img_size, - patch_size=args.patch_size, - pos_embed_rope_base=args.pos_embed_rope_base, - pos_embed_rope_min_period=args.pos_embed_rope_min_period, - pos_embed_rope_max_period=args.pos_embed_rope_max_period, - pos_embed_rope_normalize_coords=args.pos_embed_rope_normalize_coords, - pos_embed_rope_shift_coords=args.pos_embed_rope_shift_coords, - pos_embed_rope_jitter_coords=args.pos_embed_rope_jitter_coords, - pos_embed_rope_rescale_coords=args.pos_embed_rope_rescale_coords, - qkv_bias=args.qkv_bias, - layerscale_init=args.layerscale, - norm_layer=args.norm_layer, - ffn_layer=args.ffn_layer, - ffn_bias=args.ffn_bias, - proj_bias=args.proj_bias, - n_storage_tokens=args.n_storage_tokens, - mask_k_bias=args.mask_k_bias, - untie_cls_and_patch_norms=args.untie_cls_and_patch_norms, - untie_global_and_local_cls_norm=args.untie_global_and_local_cls_norm, - device=device, - ) - teacher = vits.__dict__[args.arch](**vit_kwargs) - teacher = init_fp8(teacher, args) - if only_teacher: - return teacher, teacher.embed_dim - student = vits.__dict__[args.arch]( - **vit_kwargs, - drop_path_rate=args.drop_path_rate, - ) - embed_dim = student.embed_dim - else: - raise NotImplementedError(f"Unrecognized architecture {args.arch}") - student = init_fp8(student, args) - return student, teacher, embed_dim - - -def build_model_from_cfg(cfg, only_teacher: bool = False): - outputs = build_model( - cfg.student, - only_teacher=only_teacher, - img_size=cfg.crops.global_crops_size - if isinstance(cfg.crops.global_crops_size, int) - else max(cfg.crops.global_crops_size), - device="meta", - ) - if only_teacher: - teacher, embed_dim = outputs - return teacher, embed_dim - else: - student, teacher, embed_dim = outputs - return student, teacher, embed_dim - - -def build_model_for_eval( - config, - pretrained_weights: Union[str, Path] | None, - shard_unsharded_model: bool = False, # If the model is not sharded, shard it. No effect if already sharded on disk -): - model, _ = build_model_from_cfg(config, only_teacher=True) - if pretrained_weights is None or pretrained_weights == "": - logger.info("No pretrained weights") - model.init_weights() - elif Path(pretrained_weights).is_dir(): - logger.info("PyTorch DCP checkpoint") - from dinov3.checkpointer import load_checkpoint - from dinov3.fsdp.ac_compile_parallelize import ac_compile_parallelize - - moduledict = nn.ModuleDict({"backbone": model}) - # Wrap with FSDP - ac_compile_parallelize(moduledict, inference_only_models=[], cfg=config) - # Move to CUDA - model.to_empty(device="cuda") - # Load checkpoint - load_checkpoint(pretrained_weights, model=moduledict, strict_loading=True) - shard_unsharded_model = False - else: - logger.info("PyTorch consolidated checkpoint") - from dinov3.checkpointer import init_model_from_checkpoint_for_evals - - # consolidated checkpoint codepath - model.to_empty(device="cuda") - init_model_from_checkpoint_for_evals(model, pretrained_weights, "teacher") - if shard_unsharded_model: - logger.info("Sharding model") - moduledict = nn.ModuleDict({"backbone": model}) - ac_compile_parallelize(moduledict, inference_only_models=[], cfg=config) - model.eval() - return model diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/models/__pycache__/__init__.cpython-310.pyc b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/models/__pycache__/__init__.cpython-310.pyc deleted file mode 100644 index 083c67394f805c7b7437147e6ac4a25351e17feb..0000000000000000000000000000000000000000 Binary files a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/models/__pycache__/__init__.cpython-310.pyc and /dev/null differ diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/models/__pycache__/vision_transformer.cpython-310.pyc b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/models/__pycache__/vision_transformer.cpython-310.pyc deleted file mode 100644 index 04ba1b6a2bdb11f503d373f5a32d0e1696b2c564..0000000000000000000000000000000000000000 Binary files a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/models/__pycache__/vision_transformer.cpython-310.pyc and /dev/null differ diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/models/convnext.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/models/convnext.py deleted file mode 100644 index 7271ae51863e0215a6533624b396e955b7349f99..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/models/convnext.py +++ /dev/null @@ -1,340 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import logging -from functools import partial -from typing import Dict, List, Optional, Sequence, Union - -import numpy as np -import torch -import torch.nn.functional as F -import torch.nn.init -from torch import Tensor, nn - - -logger = logging.getLogger("dinov3") - - -def drop_path(x: Tensor, drop_prob: float = 0.0, training: bool = False) -> Tensor: - if drop_prob == 0.0 or not training: - return x - keep_prob = 1 - drop_prob - shape = (x.shape[0],) + (1,) * (x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets - random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device) - random_tensor.floor_() # binarize - output = x.div(keep_prob) * random_tensor - return output - - -class DropPath(nn.Module): - """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).""" - - def __init__(self, drop_prob=None) -> None: - super(DropPath, self).__init__() - self.drop_prob = drop_prob - - def forward(self, x: Tensor) -> Tensor: - return drop_path(x, self.drop_prob, self.training) - - -class Block(nn.Module): - r"""ConvNeXt Block. There are two equivalent implementations: - (1) DwConv -> LayerNorm (channels_first) -> 1x1 Conv -> GELU -> 1x1 Conv; all in (N, C, H, W) - (2) DwConv -> Permute to (N, H, W, C); LayerNorm (channels_last) -> Linear -> GELU -> Linear; Permute back - We use (2) as we find it slightly faster in PyTorch - - Args: - dim (int): Number of input channels. - drop_path (float): Stochastic depth rate. Default: 0.0 - layer_scale_init_value (float): Init value for Layer Scale. Default: 1e-6. - - Source: https://github.com/facebookresearch/ConvNeXt/blob/main/models/convnext.py - """ - - def __init__(self, dim, drop_path=0.0, layer_scale_init_value=1e-6): - super().__init__() - self.dwconv = nn.Conv2d(dim, dim, kernel_size=7, padding=3, groups=dim) # depthwise conv - self.norm = LayerNorm(dim, eps=1e-6) - self.pwconv1 = nn.Linear(dim, 4 * dim) # pointwise/1x1 convs, implemented with linear layers - self.act = nn.GELU() - self.pwconv2 = nn.Linear(4 * dim, dim) - self.gamma = ( - nn.Parameter(layer_scale_init_value * torch.ones((dim)), requires_grad=True) - if layer_scale_init_value > 0 - else None - ) - self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() - - def forward(self, x): - input = x - x = self.dwconv(x) - x = x.permute(0, 2, 3, 1) # (N, C, H, W) -> (N, H, W, C) - x = self.norm(x) - x = self.pwconv1(x) - x = self.act(x) - x = self.pwconv2(x) - if self.gamma is not None: - x = self.gamma * x - x = x.permute(0, 3, 1, 2) # (N, H, W, C) -> (N, C, H, W) - - x = input + self.drop_path(x) - return x - - -class LayerNorm(nn.Module): - r"""LayerNorm that supports two data formats: channels_last (default) or channels_first. - The ordering of the dimensions in the inputs. channels_last corresponds to inputs with - shape (batch_size, height, width, channels) while channels_first corresponds to inputs - with shape (batch_size, channels, height, width). - - Source: https://github.com/facebookresearch/ConvNeXt/blob/main/models/convnext.py - """ - - def __init__(self, normalized_shape, eps=1e-6, data_format="channels_last"): - super().__init__() - self.weight = nn.Parameter(torch.ones(normalized_shape)) - self.bias = nn.Parameter(torch.zeros(normalized_shape)) - self.eps = eps - self.data_format = data_format - if self.data_format not in ["channels_last", "channels_first"]: - raise NotImplementedError - self.normalized_shape = (normalized_shape,) - - def forward(self, x): - if self.data_format == "channels_last": - return F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps) - elif self.data_format == "channels_first": - u = x.mean(1, keepdim=True) - s = (x - u).pow(2).mean(1, keepdim=True) - x = (x - u) / torch.sqrt(s + self.eps) - x = self.weight[:, None, None] * x + self.bias[:, None, None] - return x - - -class ConvNeXt(nn.Module): - r""" - Code adapted from https://github.com/facebookresearch/ConvNeXt/blob/main/models/convnext.pyConvNeXt - - A PyTorch impl of : `A ConvNet for the 2020s` - - https://arxiv.org/pdf/2201.03545.pdf - - Args: - in_chans (int): Number of input image channels. Default: 3 - num_classes (int): Number of classes for classification head. Default: 1000 - depths (tuple(int)): Number of blocks at each stage. Default: [3, 3, 9, 3] - dims (int): Feature dimension at each stage. Default: [96, 192, 384, 768] - drop_path_rate (float): Stochastic depth rate. Default: 0. - layer_scale_init_value (float): Init value for Layer Scale. Default: 1e-6. - patch_size (int | None): Pseudo patch size. Used to resize feature maps to those of a ViT with a given patch size. If None, no resizing is performed - """ - - def __init__( - self, - # original ConvNeXt arguments - in_chans: int = 3, - depths: List[int] = [3, 3, 9, 3], - dims: List[int] = [96, 192, 384, 768], - drop_path_rate: float = 0.0, - layer_scale_init_value: float = 1e-6, - # DINO arguments - patch_size: int | None = None, - **ignored_kwargs, - ): - super().__init__() - if len(ignored_kwargs) > 0: - logger.warning(f"Ignored kwargs: {ignored_kwargs}") - del ignored_kwargs - - # ==== ConvNeXt's original init ===== - self.downsample_layers = nn.ModuleList() # stem and 3 intermediate downsampling conv layers - stem = nn.Sequential( - nn.Conv2d(in_chans, dims[0], kernel_size=4, stride=4), - LayerNorm(dims[0], eps=1e-6, data_format="channels_first"), - ) - self.downsample_layers.append(stem) - for i in range(3): - downsample_layer = nn.Sequential( - LayerNorm(dims[i], eps=1e-6, data_format="channels_first"), - nn.Conv2d(dims[i], dims[i + 1], kernel_size=2, stride=2), - ) - self.downsample_layers.append(downsample_layer) - - self.stages = nn.ModuleList() # 4 feature resolution stages, each consisting of multiple residual blocks - dp_rates = [x for x in np.linspace(0, drop_path_rate, sum(depths))] - cur = 0 - for i in range(4): - stage = nn.Sequential( - *[ - Block(dim=dims[i], drop_path=dp_rates[cur + j], layer_scale_init_value=layer_scale_init_value) - for j in range(depths[i]) - ] - ) - self.stages.append(stage) - cur += depths[i] - - self.norm = nn.LayerNorm(dims[-1], eps=1e-6) # final norm layer - # ==== End of ConvNeXt's original init ===== - - # ==== DINO adaptation ==== - self.head = nn.Identity() # remove classification head - self.embed_dim = dims[-1] - self.embed_dims = dims # per layer dimensions - self.n_blocks = len(self.downsample_layers) # 4 - self.chunked_blocks = False - self.n_storage_tokens = 0 # no registers - - self.norms = nn.ModuleList([nn.Identity() for i in range(3)]) - self.norms.append(self.norm) - - self.patch_size = patch_size - self.input_pad_size = 4 # first convolution with kernel_size = 4, stride = 4 - - def init_weights(self): - self.apply(self._init_weights) - - def _init_weights(self, module): - if isinstance(module, nn.LayerNorm): - module.reset_parameters() - if isinstance(module, LayerNorm): - module.weight = nn.Parameter(torch.ones(module.normalized_shape)) - module.bias = nn.Parameter(torch.zeros(module.normalized_shape)) - if isinstance(module, (nn.Conv2d, nn.Linear)): - torch.nn.init.trunc_normal_(module.weight, std=0.02) - nn.init.constant_(module.bias, 0) - - def forward_features(self, x: Tensor | List[Tensor], masks: Optional[Tensor] = None) -> List[Dict[str, Tensor]]: - if isinstance(x, torch.Tensor): - return self.forward_features_list([x], [masks])[0] - else: - return self.forward_features_list(x, masks) - - def forward_features_list(self, x_list: List[Tensor], masks_list: List[Tensor]) -> List[Dict[str, Tensor]]: - output = [] - for x, masks in zip(x_list, masks_list): - h, w = x.shape[-2:] - for i in range(4): - x = self.downsample_layers[i](x) - x = self.stages[i](x) - x_pool = x.mean([-2, -1]) # global average pooling, (N, C, H, W) -> (N, C) - x = torch.flatten(x, 2).transpose(1, 2) - - # concat [CLS] and patch tokens as (N, HW + 1, C), then normalize - x_norm = self.norm(torch.cat([x_pool.unsqueeze(1), x], dim=1)) - output.append( - { - "x_norm_clstoken": x_norm[:, 0], - "x_storage_tokens": x_norm[:, 1 : self.n_storage_tokens + 1], - "x_norm_patchtokens": x_norm[:, self.n_storage_tokens + 1 :], - "x_prenorm": x, - "masks": masks, - } - ) - - return output - - def forward(self, *args, is_training=False, **kwargs): - ret = self.forward_features(*args, **kwargs) - if is_training: - return ret - else: - return self.head(ret["x_norm_clstoken"]) - - def _get_intermediate_layers(self, x, n=1): - h, w = x.shape[-2:] - output, total_block_len = [], len(self.downsample_layers) - blocks_to_take = range(total_block_len - n, total_block_len) if isinstance(n, int) else n - for i in range(total_block_len): - x = self.downsample_layers[i](x) - x = self.stages[i](x) - if i in blocks_to_take: - x_pool = x.mean([-2, -1]) - x_patches = x - if self.patch_size is not None: - # Resize output feature maps to that of a ViT with given patch_size - x_patches = nn.functional.interpolate( - x, - size=(h // self.patch_size, w // self.patch_size), - mode="bilinear", - antialias=True, - ) - output.append( - [ - x_pool, # CLS (B x C) - x_patches, # B x C x H x W - ] - ) - assert len(output) == len(blocks_to_take), f"only {len(output)} / {len(blocks_to_take)} blocks found" - return output - - def get_intermediate_layers( - self, - x, - n: Union[int, Sequence] = 1, # Layers or n last layers to take, - reshape: bool = False, - return_class_token: bool = False, - norm: bool = True, - ): - outputs = self._get_intermediate_layers(x, n) - - if norm: - nchw_shapes = [out[-1].shape for out in outputs] - if isinstance(n, int): - norms = self.norms[-n:] - else: - norms = [self.norms[i] for i in n] - outputs = [ - ( - norm(cls_token), # N x C - norm(patches.flatten(-2, -1).permute(0, 2, 1)), # N x HW x C - ) - for (cls_token, patches), norm in zip(outputs, norms) - ] - if reshape: - outputs = [ - (cls_token, patches.permute(0, 2, 1).reshape(*nchw).contiguous()) - for (cls_token, patches), nchw in zip(outputs, nchw_shapes) - ] - elif not reshape: - # force B x N x C format for patch tokens - outputs = [(cls_token, patches.flatten(-2, -1).permute(0, 2, 1)) for (cls_token, patches) in outputs] - class_tokens = [out[0] for out in outputs] - outputs = [out[1] for out in outputs] - if return_class_token: - return tuple(zip(outputs, class_tokens)) - return tuple(outputs) - - -convnext_sizes = { - "tiny": dict( - depths=[3, 3, 9, 3], - dims=[96, 192, 384, 768], - ), - "small": dict( - depths=[3, 3, 27, 3], - dims=[96, 192, 384, 768], - ), - "base": dict( - depths=[3, 3, 27, 3], - dims=[128, 256, 512, 1024], - ), - "large": dict( - depths=[3, 3, 27, 3], - dims=[192, 384, 768, 1536], - ), -} - - -def get_convnext_arch(arch_name): - size_dict = None - query_sizename = arch_name.split("_")[1] - try: - size_dict = convnext_sizes[query_sizename] - except KeyError: - raise NotImplementedError("didn't recognize vit size string") - - return partial( - ConvNeXt, - **size_dict, - ) diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/models/vision_transformer.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/models/vision_transformer.py deleted file mode 100644 index 51316e518850cd607ffddc40c40bd39e8f2a03c2..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/models/vision_transformer.py +++ /dev/null @@ -1,416 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import logging -from functools import partial -from typing import Any, Dict, List, Literal, Optional, Sequence, Tuple, Union - -import torch -import torch.nn.init -from torch import Tensor, nn - -from dinov3.layers import LayerScale, Mlp, PatchEmbed, RMSNorm, RopePositionEmbedding, SelfAttentionBlock, SwiGLUFFN -from dinov3.utils import named_apply - -logger = logging.getLogger("dinov3") - -ffn_layer_dict = { - "mlp": Mlp, - "swiglu": SwiGLUFFN, - "swiglu32": partial(SwiGLUFFN, align_to=32), - "swiglu64": partial(SwiGLUFFN, align_to=64), - "swiglu128": partial(SwiGLUFFN, align_to=128), -} - -norm_layer_dict = { - "layernorm": partial(nn.LayerNorm, eps=1e-6), - "layernormbf16": partial(nn.LayerNorm, eps=1e-5), - "rmsnorm": RMSNorm, -} - -dtype_dict = { - "fp32": torch.float32, - "fp16": torch.float16, - "bf16": torch.bfloat16, -} - - -def init_weights_vit(module: nn.Module, name: str = ""): - if isinstance(module, nn.Linear): - torch.nn.init.trunc_normal_(module.weight, std=0.02) - if module.bias is not None: - nn.init.zeros_(module.bias) - if hasattr(module, "bias_mask") and module.bias_mask is not None: - o = module.out_features - module.bias_mask.fill_(1) - module.bias_mask[o // 3 : 2 * o // 3].fill_(0) - if isinstance(module, nn.LayerNorm): - module.reset_parameters() - if isinstance(module, LayerScale): - module.reset_parameters() - if isinstance(module, PatchEmbed): - module.reset_parameters() - if isinstance(module, RMSNorm): - module.reset_parameters() - - -class DinoVisionTransformer(nn.Module): - def __init__( - self, - *, - img_size: int = 224, - patch_size: int = 16, - in_chans: int = 3, - pos_embed_rope_base: float = 100.0, - pos_embed_rope_min_period: float | None = None, - pos_embed_rope_max_period: float | None = None, - pos_embed_rope_normalize_coords: Literal["min", "max", "separate"] = "separate", - pos_embed_rope_shift_coords: float | None = None, - pos_embed_rope_jitter_coords: float | None = None, - pos_embed_rope_rescale_coords: float | None = None, - pos_embed_rope_dtype: str = "bf16", - embed_dim: int = 768, - depth: int = 12, - num_heads: int = 12, - ffn_ratio: float = 4.0, - qkv_bias: bool = True, - drop_path_rate: float = 0.0, - layerscale_init: float | None = None, - norm_layer: str = "layernorm", - ffn_layer: str = "mlp", - ffn_bias: bool = True, - proj_bias: bool = True, - n_storage_tokens: int = 0, - mask_k_bias: bool = False, - untie_cls_and_patch_norms: bool = False, - untie_global_and_local_cls_norm: bool = False, - device: Any | None = None, - **ignored_kwargs, - ): - super().__init__() - if len(ignored_kwargs) > 0: - logger.warning(f"Ignored kwargs: {ignored_kwargs}") - del ignored_kwargs - - norm_layer_cls = norm_layer_dict[norm_layer] - - self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models - self.n_blocks = depth - self.num_heads = num_heads - self.patch_size = patch_size - - self.patch_embed = PatchEmbed( - img_size=img_size, - patch_size=patch_size, - in_chans=in_chans, - embed_dim=embed_dim, - flatten_embedding=False, - ) - - self.cls_token = nn.Parameter(torch.empty(1, 1, embed_dim, device=device)) - self.n_storage_tokens = n_storage_tokens - if self.n_storage_tokens > 0: - self.storage_tokens = nn.Parameter(torch.empty(1, n_storage_tokens, embed_dim, device=device)) - logger.info(f"using base={pos_embed_rope_base} for rope new") - logger.info(f"using min_period={pos_embed_rope_min_period} for rope new") - logger.info(f"using max_period={pos_embed_rope_max_period} for rope new") - logger.info(f"using normalize_coords={pos_embed_rope_normalize_coords} for rope new") - logger.info(f"using shift_coords={pos_embed_rope_shift_coords} for rope new") - logger.info(f"using rescale_coords={pos_embed_rope_rescale_coords} for rope new") - logger.info(f"using jitter_coords={pos_embed_rope_jitter_coords} for rope new") - logger.info(f"using dtype={pos_embed_rope_dtype} for rope new") - self.rope_embed = RopePositionEmbedding( - embed_dim=embed_dim, - num_heads=num_heads, - base=pos_embed_rope_base, - min_period=pos_embed_rope_min_period, - max_period=pos_embed_rope_max_period, - normalize_coords=pos_embed_rope_normalize_coords, - shift_coords=pos_embed_rope_shift_coords, - jitter_coords=pos_embed_rope_jitter_coords, - rescale_coords=pos_embed_rope_rescale_coords, - dtype=dtype_dict[pos_embed_rope_dtype], - device=device, - ) - logger.info(f"using {ffn_layer} layer as FFN") - ffn_layer_cls = ffn_layer_dict[ffn_layer] - ffn_ratio_sequence = [ffn_ratio] * depth - blocks_list = [ - SelfAttentionBlock( - dim=embed_dim, - num_heads=num_heads, - ffn_ratio=ffn_ratio_sequence[i], - qkv_bias=qkv_bias, - proj_bias=proj_bias, - ffn_bias=ffn_bias, - drop_path=drop_path_rate, - norm_layer=norm_layer_cls, - act_layer=nn.GELU, - ffn_layer=ffn_layer_cls, - init_values=layerscale_init, - mask_k_bias=mask_k_bias, - device=device, - ) - for i in range(depth) - ] - - self.chunked_blocks = False - self.blocks = nn.ModuleList(blocks_list) - - # This norm is applied to everything, or when untying, to patch and mask tokens. - self.norm = norm_layer_cls(embed_dim) - - self.untie_cls_and_patch_norms = untie_cls_and_patch_norms - if untie_cls_and_patch_norms: - # When untying, this norm is applied to CLS tokens and registers. - self.cls_norm = norm_layer_cls(embed_dim) - else: - self.cls_norm = None - - self.untie_global_and_local_cls_norm = untie_global_and_local_cls_norm - if untie_global_and_local_cls_norm: - # When untying, this norm is applied to local CLS tokens and registers. - # This norm is never used during eval. - self.local_cls_norm = norm_layer_cls(embed_dim) - else: - self.local_cls_norm = None - self.head = nn.Identity() - self.mask_token = nn.Parameter(torch.empty(1, embed_dim, device=device)) - - def init_weights(self): - self.rope_embed._init_weights() - nn.init.normal_(self.cls_token, std=0.02) - if self.n_storage_tokens > 0: - nn.init.normal_(self.storage_tokens, std=0.02) - nn.init.zeros_(self.mask_token) - named_apply(init_weights_vit, self) - - def prepare_tokens_with_masks(self, x: Tensor, masks=None) -> Tuple[Tensor, Tuple[int]]: - x = self.patch_embed(x) - B, H, W, _ = x.shape - x = x.flatten(1, 2) - - if masks is not None: - x = torch.where(masks.unsqueeze(-1), self.mask_token.to(x.dtype).unsqueeze(0), x) - cls_token = self.cls_token - else: - cls_token = self.cls_token + 0 * self.mask_token - if self.n_storage_tokens > 0: - storage_tokens = self.storage_tokens - else: - storage_tokens = torch.empty( - 1, - 0, - cls_token.shape[-1], - dtype=cls_token.dtype, - device=cls_token.device, - ) - - x = torch.cat( - [ - cls_token.expand(B, -1, -1), - storage_tokens.expand(B, -1, -1), - x, - ], - dim=1, - ) - - return x, (H, W) - - def forward_features_list(self, x_list: List[Tensor], masks_list: List[Tensor]) -> List[Dict[str, Tensor]]: - x = [] - rope = [] - for t_x, t_masks in zip(x_list, masks_list): - t2_x, hw_tuple = self.prepare_tokens_with_masks(t_x, t_masks) - x.append(t2_x) - rope.append(hw_tuple) - for _, blk in enumerate(self.blocks): - if self.rope_embed is not None: - rope_sincos = [self.rope_embed(H=H, W=W) for H, W in rope] - else: - rope_sincos = [None for r in rope] - x = blk(x, rope_sincos) - all_x = x - output = [] - for idx, (x, masks) in enumerate(zip(all_x, masks_list)): - if self.untie_cls_and_patch_norms or self.untie_global_and_local_cls_norm: - if self.untie_global_and_local_cls_norm and self.training and idx == 1: - # Assume second entry of list corresponds to local crops. - # We only ever apply this during training. - x_norm_cls_reg = self.local_cls_norm(x[:, : self.n_storage_tokens + 1]) - elif self.untie_cls_and_patch_norms: - x_norm_cls_reg = self.cls_norm(x[:, : self.n_storage_tokens + 1]) - else: - x_norm_cls_reg = self.norm(x[:, : self.n_storage_tokens + 1]) - x_norm_patch = self.norm(x[:, self.n_storage_tokens + 1 :]) - else: - x_norm = self.norm(x) - x_norm_cls_reg = x_norm[:, : self.n_storage_tokens + 1] - x_norm_patch = x_norm[:, self.n_storage_tokens + 1 :] - output.append( - { - "x_norm_clstoken": x_norm_cls_reg[:, 0], - "x_storage_tokens": x_norm_cls_reg[:, 1:], - "x_norm_patchtokens": x_norm_patch, - "x_prenorm": x, - "masks": masks, - } - ) - return output - - def forward_features(self, x: Tensor | List[Tensor], masks: Optional[Tensor] = None) -> List[Dict[str, Tensor]]: - if isinstance(x, torch.Tensor): - return self.forward_features_list([x], [masks])[0] - else: - return self.forward_features_list(x, masks) - - def _get_intermediate_layers_not_chunked(self, x: Tensor, n: int = 1) -> List[Tensor]: - x, (H, W) = self.prepare_tokens_with_masks(x) - # If n is an int, take the n last blocks. If it's a list, take them - output, total_block_len = [], len(self.blocks) - blocks_to_take = range(total_block_len - n, total_block_len) if isinstance(n, int) else n - for i, blk in enumerate(self.blocks): - if self.rope_embed is not None: - rope_sincos = self.rope_embed(H=H, W=W) - else: - rope_sincos = None - x = blk(x, rope_sincos) - if i in blocks_to_take: - output.append(x) - assert len(output) == len(blocks_to_take), f"only {len(output)} / {len(blocks_to_take)} blocks found" - return output - - def get_intermediate_layers( - self, - x: torch.Tensor, - *, - n: Union[int, Sequence] = 1, # Layers or n last layers to take - reshape: bool = False, - return_class_token: bool = False, - return_extra_tokens: bool = False, - norm: bool = True, - ) -> Tuple[Union[torch.Tensor, Tuple[torch.Tensor, ...]]]: - outputs = self._get_intermediate_layers_not_chunked(x, n) - if norm: - outputs_normed = [] - for out in outputs: - if self.untie_cls_and_patch_norms: - x_norm_cls_reg = self.cls_norm(out[:, : self.n_storage_tokens + 1]) - x_norm_patch = self.norm(out[:, self.n_storage_tokens + 1 :]) - outputs_normed.append(torch.cat((x_norm_cls_reg, x_norm_patch), dim=1)) - else: - outputs_normed.append(self.norm(out)) - outputs = outputs_normed - class_tokens = [out[:, 0] for out in outputs] - extra_tokens = [out[:, 1 : self.n_storage_tokens + 1] for out in outputs] - outputs = [out[:, self.n_storage_tokens + 1 :] for out in outputs] - if reshape: - B, _, h, w = x.shape - outputs = [ - out.reshape(B, h // self.patch_size, w // self.patch_size, -1).permute(0, 3, 1, 2).contiguous() - for out in outputs - ] - if not return_class_token and not return_extra_tokens: - return tuple(outputs) - elif return_class_token and not return_extra_tokens: - return tuple(zip(outputs, class_tokens)) - elif not return_class_token and return_extra_tokens: - return tuple(zip(outputs, extra_tokens)) - elif return_class_token and return_extra_tokens: - return tuple(zip(outputs, class_tokens, extra_tokens)) - - def forward(self, *args, is_training: bool = False, **kwargs) -> List[Dict[str, Tensor]] | Tensor: - ret = self.forward_features(*args, **kwargs) - if is_training: - return ret - else: - return self.head(ret["x_norm_clstoken"]) - - -def vit_small(patch_size=16, **kwargs): - model = DinoVisionTransformer( - patch_size=patch_size, - embed_dim=384, - depth=12, - num_heads=6, - ffn_ratio=4, - **kwargs, - ) - return model - - -def vit_base(patch_size=16, **kwargs): - model = DinoVisionTransformer( - patch_size=patch_size, - embed_dim=768, - depth=12, - num_heads=12, - ffn_ratio=4, - **kwargs, - ) - return model - - -def vit_large(patch_size=16, **kwargs): - model = DinoVisionTransformer( - patch_size=patch_size, - embed_dim=1024, - depth=24, - num_heads=16, - ffn_ratio=4, - **kwargs, - ) - return model - - -def vit_so400m(patch_size=16, **kwargs): - model = DinoVisionTransformer( - patch_size=patch_size, - embed_dim=1152, - depth=27, - num_heads=18, - ffn_ratio=3.777777778, - **kwargs, - ) - return model - - -def vit_huge2(patch_size=16, **kwargs): - model = DinoVisionTransformer( - patch_size=patch_size, - embed_dim=1280, - depth=32, - num_heads=20, - ffn_ratio=4, - **kwargs, - ) - return model - - -def vit_giant2(patch_size=16, **kwargs): - """ - Close to ViT-giant, with embed-dim 1536 and 24 heads => embed-dim per head 64 - """ - model = DinoVisionTransformer( - patch_size=patch_size, - embed_dim=1536, - depth=40, - num_heads=24, - ffn_ratio=4, - **kwargs, - ) - return model - - -def vit_7b(patch_size=16, **kwargs): - model = DinoVisionTransformer( - patch_size=patch_size, - embed_dim=4096, - depth=40, - num_heads=32, - ffn_ratio=3, - **kwargs, - ) - return model diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/run/init.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/run/init.py deleted file mode 100644 index b3d96d718f5b3b63702cd0b40bbaefcab0dfecca..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/run/init.py +++ /dev/null @@ -1,33 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import contextlib -from datetime import timedelta -from typing import Optional - -from dinov3.configs import exit_job, setup_job - - -@contextlib.contextmanager -def job_context( - output_dir: Optional[str] = None, - distributed_enabled: bool = True, - logging_enabled: bool = True, - seed: Optional[int] = 0, - restrict_print_to_main_process: bool = True, - distributed_timeout: timedelta | None = None, -): - setup_job( - output_dir=output_dir, - distributed_enabled=distributed_enabled, - logging_enabled=logging_enabled, - seed=seed, - restrict_print_to_main_process=restrict_print_to_main_process, - distributed_timeout=distributed_timeout, - ) - try: - yield - finally: - exit_job(distributed_enabled=distributed_enabled, logging_enabled=logging_enabled) diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/run/submit.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/run/submit.py deleted file mode 100644 index 5ad14edc26b02dd16e82bb636ac9cd43467082cd..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/run/submit.py +++ /dev/null @@ -1,208 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import argparse -import logging -import os -from pathlib import Path - -from dinov3.logging import setup_logging -from dinov3.utils.cluster import ( - get_slurm_account, - get_slurm_executor_parameters, - get_slurm_partition, - get_slurm_qos, - get_user_checkpoint_path, -) -from dinov3.utils.custom_callable import load_custom_callable - -logger = logging.getLogger("dinov3") - - -def get_submitit_parser(): - slurm_partition = get_slurm_partition() - slurm_account = get_slurm_account() - slurm_qos = get_slurm_qos() - parser = argparse.ArgumentParser("Submitit arguments", add_help=False) - parser.add_argument( - "--ngpus", - default=8, - type=int, - help="Number of gpus to request on each node, default: %(default)s", - ) - parser.add_argument( - "--nodes", - default=1, - type=int, - help="Number of nodes to request, default: %(default)s", - ) - parser.add_argument( - "--timeout", - default=2800, - type=int, - help="Duration of the job, default: %(default)s", - ) - parser.add_argument( - "--slurm-partition", - default=slurm_partition, - type=str, - help="Partition where to submit, default: %(default)s", - ) - parser.add_argument( - "--slurm-qos", - default=slurm_qos, - metavar="SLURM_QOS", - type=str, - dest="slurm_qos", - help="slurm QoS to use for jobs in cluster environment, default: %(default)s", - ) - parser.add_argument( - "--slurm-array-parallelism", - default=256, - type=int, - help="Maximum number of jobs that will be executed in parallel, default: %(default)s", - ) - parser.add_argument( - "--slurm-nice", - default=0, - type=int, - help="Adjusted scheduling priority within Slurm, default: %(default)s", - ) - parser.add_argument( - "--slurm-account", - default=slurm_account, - type=str, - help="Slurm account name, default: %(default)s", - ) - parser.add_argument( - "--comment", - default="", - type=str, - help="Comment to pass to scheduler, e.g. priority message, default: '%(default)s'", - ) - parser.add_argument( - "--exclude", - default="", - type=str, - help="Nodes to exclude, default: '%(default)s'", - ) - parser.add_argument( - "--output-dir", - type=str, - help="output dir", - ) - return parser - - -def get_run_parser(): - parser = argparse.ArgumentParser("Launcher arguments", parents=[get_submitit_parser()]) - parser.add_argument( - "module_path", - type=str, - help="Full path to the program/script to be launched in parallel, " - "followed by all the arguments for the training script.", - ) - parser.add_argument( - "--callable-name", - type=str, - default="main", - help="Name of the callable to execute in the script", - ) - return parser - - -def get_shared_folder() -> Path: - user_checkpoint_path = get_user_checkpoint_path() - if user_checkpoint_path is None: - raise RuntimeError("Path to user checkpoint cannot be determined") - path = user_checkpoint_path / "experiments" - path.mkdir(exist_ok=True) - return path - - -class CheckpointableSubmitter: - def __init__(self, module_path, callable_name, args, output_dir): - self.args = args - self.callable_name = callable_name - self.module_path = os.path.realpath(module_path) - self.output_dir = os.path.realpath(output_dir) - - def __call__(self): - self._setup_args() - callable_ = load_custom_callable(self.module_path, self.callable_name) - callable_(self.args) - - def checkpoint(self): - import submitit - - logger.info(f"Requeuing {self.callable_name} from {self.module_path} with {self.args}") - empty_class = type(self)(self.module_path, self.callable_name, self.args, self.output_dir) - return submitit.helpers.DelayedSubmission(empty_class) - - def _setup_args(self): - import submitit - - job_env = submitit.JobEnvironment() - self.output_dir = str(self.output_dir).replace("%j", str(job_env.job_id)) - if "--output-dir" not in self.args: - self.args.insert(0, f"--output-dir={self.output_dir}") - - # Setup logging with exact same arguments as in fairvit/run/init.py - # to use lru_cache memoization and avoid setting up the logger twice - setup_logging(output=self.output_dir, level=logging.INFO) - logger.info(f"Process group: {job_env.num_tasks} tasks, rank: {job_env.global_rank}") - logger.info(f"Module Path: {self.module_path}") - logger.info(f"Callable Name: {self.callable_name}") - logger.info(f'Args: {" ".join(self.args)}') - - -def submit_jobs(class_to_submit, output_dir, submitit_args, name="fairvit"): - import submitit - - Path(output_dir).mkdir(parents=True, exist_ok=True) - executor = submitit.AutoExecutor(folder=output_dir, slurm_max_num_timeout=30) - - kwargs = {} - if submitit_args.comment: - kwargs["slurm_comment"] = submitit_args.comment - if submitit_args.exclude: - kwargs["slurm_exclude"] = submitit_args.exclude - - executor_params = get_slurm_executor_parameters( - nodes=submitit_args.nodes, - num_gpus_per_node=submitit_args.ngpus, - timeout_min=submitit_args.timeout, # max is 60 * 72 - slurm_signal_delay_s=120, - slurm_partition=submitit_args.slurm_partition, - slurm_qos=submitit_args.slurm_qos, - # slurm_account=submitit_args.slurm_account, - slurm_additional_parameters=dict(nice=submitit_args.slurm_nice), - **kwargs, - ) - executor.update_parameters(name=name, **executor_params) - job = executor.submit(class_to_submit) - - logger.info(f"Submitted job_id: {job.job_id}") - str_output_dir = os.path.abspath(output_dir).replace("%j", str(job.job_id)) - logger.info(f"Logs and checkpoints will be saved at: {str_output_dir}") - - -def main(): - setup_logging(level=logging.INFO) - args, script_args = get_run_parser().parse_known_args() - assert os.path.exists(args.module_path), "The module path does not exist" - - file_name = os.path.splitext(os.path.split(args.module_path)[1])[0] - name = f"{file_name}:{args.callable_name}" - - if args.output_dir is None: - args.output_dir = get_shared_folder() / "%j" - - class_to_submit = CheckpointableSubmitter(args.module_path, args.callable_name, script_args, args.output_dir) - submit_jobs(class_to_submit, args.output_dir, args, name=name) - - -if __name__ == "__main__": - main() diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/thirdparty/CLIP/clip/simple_tokenizer.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/thirdparty/CLIP/clip/simple_tokenizer.py deleted file mode 100644 index e424249d57012833247c058e9e9f581e2b8097c9..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/thirdparty/CLIP/clip/simple_tokenizer.py +++ /dev/null @@ -1,143 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -# References: -# https://github.com/openai/CLIP/blob/main/clip/simple_tokenizer.py - -import gzip -import html -import os -from functools import lru_cache - -import ftfy -import regex as re - - -@lru_cache() -def default_bpe(): - return os.path.join(os.path.dirname(os.path.abspath(__file__)), "bpe_simple_vocab_16e6.txt.gz") - - -@lru_cache() -def bytes_to_unicode(): - """ - Returns list of utf-8 byte and a corresponding list of unicode strings. - The reversible bpe codes work on unicode strings. - This means you need a large # of unicode characters in your vocab if you want to avoid UNKs. - When you're at something like a 10B token dataset you end up needing around 5K for decent coverage. - This is a signficant percentage of your normal, say, 32K bpe vocab. - To avoid that, we want lookup tables between utf-8 bytes and unicode strings. - And avoids mapping to whitespace/control characters the bpe code barfs on. - """ - bs = list(range(ord("!"), ord("~") + 1)) + list(range(ord("¡"), ord("¬") + 1)) + list(range(ord("®"), ord("ÿ") + 1)) - cs = bs[:] - n = 0 - for b in range(2**8): - if b not in bs: - bs.append(b) - cs.append(2**8 + n) - n += 1 - cs = [chr(n) for n in cs] - return dict(zip(bs, cs)) - - -def get_pairs(word): - """Return set of symbol pairs in a word. - Word is represented as tuple of symbols (symbols being variable-length strings). - """ - pairs = set() - prev_char = word[0] - for char in word[1:]: - pairs.add((prev_char, char)) - prev_char = char - return pairs - - -def basic_clean(text): - text = ftfy.fix_text(text) - text = html.unescape(html.unescape(text)) - return text.strip() - - -def whitespace_clean(text): - text = re.sub(r"\s+", " ", text) - text = text.strip() - return text - - -class SimpleTokenizer(object): - def __init__(self, bpe_path: str = default_bpe()): - self.byte_encoder = bytes_to_unicode() - self.byte_decoder = {v: k for k, v in self.byte_encoder.items()} - merges = gzip.open(bpe_path).read().decode("utf-8").split("\n") - merges = merges[1 : 49152 - 256 - 2 + 1] - merges = [tuple(merge.split()) for merge in merges] - vocab = list(bytes_to_unicode().values()) - vocab = vocab + [v + "" for v in vocab] - for merge in merges: - vocab.append("".join(merge)) - vocab.extend(["<|startoftext|>", "<|endoftext|>"]) - self.encoder = dict(zip(vocab, range(len(vocab)))) - self.decoder = {v: k for k, v in self.encoder.items()} - self.bpe_ranks = dict(zip(merges, range(len(merges)))) - self.cache = {"<|startoftext|>": "<|startoftext|>", "<|endoftext|>": "<|endoftext|>"} - self.pat = re.compile( - r"""<\|startoftext\|>|<\|endoftext\|>|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""", - re.IGNORECASE, - ) - - def bpe(self, token): - if token in self.cache: - return self.cache[token] - word = tuple(token[:-1]) + (token[-1] + "",) - pairs = get_pairs(word) - - if not pairs: - return token + "" - - while True: - bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf"))) - if bigram not in self.bpe_ranks: - break - first, second = bigram - new_word = [] - i = 0 - while i < len(word): - try: - j = word.index(first, i) - new_word.extend(word[i:j]) - i = j - except Exception: - new_word.extend(word[i:]) - break - - if word[i] == first and i < len(word) - 1 and word[i + 1] == second: - new_word.append(first + second) - i += 2 - else: - new_word.append(word[i]) - i += 1 - new_word = tuple(new_word) - word = new_word - if len(word) == 1: - break - else: - pairs = get_pairs(word) - word = " ".join(word) - self.cache[token] = word - return word - - def encode(self, text): - bpe_tokens = [] - text = whitespace_clean(basic_clean(text)).lower() - for token in re.findall(self.pat, text): - token = "".join(self.byte_encoder[b] for b in token.encode("utf-8")) - bpe_tokens.extend(self.encoder[bpe_token] for bpe_token in self.bpe(token).split(" ")) - return bpe_tokens - - def decode(self, tokens): - text = "".join([self.decoder[token] for token in tokens]) - text = bytearray([self.byte_decoder[c] for c in text]).decode("utf-8", errors="replace").replace("", " ") - return text diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/train/__init__.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/train/__init__.py deleted file mode 100644 index 39e2108eb60628bb32958c7b373e76f68a10e891..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/train/__init__.py +++ /dev/null @@ -1,8 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -from .multidist_meta_arch import MultiDistillationMetaArch -from .ssl_meta_arch import SSLMetaArch -from .train import get_args_parser, main diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/train/cosine_lr_scheduler.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/train/cosine_lr_scheduler.py deleted file mode 100644 index 2c61d7d01f573bca1e1111586244ef00587dd1f3..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/train/cosine_lr_scheduler.py +++ /dev/null @@ -1,85 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import logging - -import numpy as np - -logger = logging.getLogger("dinov3") - - -class CosineScheduler(object): - def __init__( - self, - base_value, - final_value, - total_iters, - warmup_iters=0, - start_warmup_value=0, - freeze_iters=0, - trunc_extra=0.0, - ): - super().__init__() - self.final_value = np.float64(final_value) - self.total_iters = total_iters - - freeze_schedule = np.zeros((freeze_iters)) - - warmup_schedule = np.linspace(start_warmup_value, base_value, warmup_iters) - - if trunc_extra == 0.0: - iters = np.arange(total_iters - warmup_iters - freeze_iters) - schedule = final_value + 0.5 * (base_value - final_value) * (1 + np.cos(np.pi * iters / len(iters))) - else: - cosine_steps = total_iters - warmup_iters - freeze_iters - iters = np.linspace(0, np.pi, int((1 + trunc_extra) * cosine_steps))[:cosine_steps] - schedule = np.cos(iters) - schedule = (schedule + 1) / 2 - schedule = (schedule - schedule[-1]) / (1 - schedule[-1]) - schedule = schedule * (base_value - final_value) + final_value - - self.schedule = np.concatenate((freeze_schedule, warmup_schedule, schedule), dtype=np.float64) - - assert len(self.schedule) == self.total_iters - - def __getitem__(self, it): - if it >= self.total_iters: - return self.final_value - else: - return self.schedule[it] - - -def linear_warmup_cosine_decay( - start: float, - peak: float, - end: float, - warmup_iterations: int, - total_iterations: int, - cosine_iterations: int | None = None, -) -> np.ndarray: - """ - Create a learning rate schedule with linear warmup, a cosine, and an optional constant part in the end. - - Args: - start (float): Initial learning rate. - peak (float): Learning rate after linear warmup. - end (float): Final learning rate after cosine. - warmup_iterations (int): Number of iterations for linear warmup. - total_iterations (int): Total number of iterations for the schedule. - cosine_iterations (int | None): Number of iterations for cosine. - If None, cosine part will be over remaining iterations after warmup. - Returns: - np.ndarray: Learning rate schedule as a numpy array. - """ - linear = np.linspace(start, peak, warmup_iterations, endpoint=False) - if cosine_iterations is None: - cosine_iterations = total_iterations - warmup_iterations - cosine = np.cos(np.linspace(0, np.pi, cosine_iterations)) - cosine = (cosine + 1) / 2 - cosine = (peak - end) * cosine + end - remaining_iterations = total_iterations - cosine_iterations - warmup_iterations - assert remaining_iterations >= 0 - constant = np.full((remaining_iterations,), fill_value=end) - return np.concatenate([linear, cosine, constant]) diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/train/multidist_meta_arch.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/train/multidist_meta_arch.py deleted file mode 100644 index d2238c4a9f974134dd2bf98e6df41de73fdea49a..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/train/multidist_meta_arch.py +++ /dev/null @@ -1,155 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import logging - -import torch -from torch import Tensor - -from .ssl_meta_arch import SSLMetaArch - -logger = logging.getLogger("dinov3") - - -class MultiDistillationMetaArch(SSLMetaArch): - """ - Multidistillation version of SSLMetaArchCompilableGram: - - baked-in scales for DINO, KOLEO, and IBOT losses - - always global and local crops - - always separate heads for DINO and IBOT - - always sinkhorn-knopp centering for DINO and IBOT - - always per-GPU computation of KOLEO loss (non-distributed) - - DINO, IBOT, and KOLEO are always computed even if their weight is 0.0 - """ - - def forward_backward( - self, data, *, teacher_temp, iteration: int = 0, **ignored_kwargs - ) -> tuple[Tensor, dict[str, float | Tensor]]: - del ignored_kwargs - metrics_dict = {} - - # Shapes - n_global_crops = 2 - n_local_crops = self.n_local_crops # self.cfg.crops.local_crops_number - B_teacher = B = data["collated_local_crops"].shape[0] // n_local_crops - assert data["collated_global_crops"].shape[0] == n_global_crops * B - metrics_dict["batch_size"] = B - - global_crops = data["collated_global_crops"].cuda(non_blocking=True) - local_crops = data["collated_local_crops"].cuda(non_blocking=True) - masks = data["collated_masks"].cuda(non_blocking=True) - mask_indices_list = data["mask_indices_list"].cuda(non_blocking=True) - masks_weight = data["masks_weight"].cuda(non_blocking=True) - n_masked_patches_tensor = data["n_masked_patches"].cuda(non_blocking=True) - global_batch_size = data["global_batch_size"] - - # Multidistillation codepath: - global_crops_subgroup = self.broadcast_to_subgroups( - global_crops.view(n_global_crops, -1, *global_crops.shape[1:]), - 1, - global_batch_size=global_batch_size, - ).view(-1, *global_crops.shape[1:]) - local_crops_subgroup = self.broadcast_to_subgroups( - local_crops.view(n_local_crops, -1, *local_crops.shape[1:]), - 1, - global_batch_size=global_batch_size, - ).view(-1, *local_crops.shape[1:]) - B = local_crops_subgroup.shape[0] // n_local_crops - - # Teacher output (will trigger an all-gather to unshard) - teacher_global = self.get_teacher_output( - global_crops.unflatten(0, (n_global_crops, B_teacher)), - teacher_temp=teacher_temp, - n_masked_patches_tensor=n_masked_patches_tensor, - mask_indices_list=mask_indices_list, - upperbound=data["upperbound"], - global_batch_size=global_batch_size, - ) - - # Student output (will trigger an all-gather to unshard) - student_global, student_local = self.get_student_output( - global_crops=global_crops_subgroup.unflatten(0, (n_global_crops, B)), - local_crops=local_crops_subgroup.unflatten(0, (n_local_crops, B)), - upperbound=data["upperbound"], - masks=masks, - mask_indices_list=mask_indices_list, - ) - # End of multidistillation codepath - - # Compute losses and backprop - loss_accumulator, loss_dict = self.compute_losses( - teacher_global=teacher_global, - student_global=student_global, - student_local=student_local, - masks=masks, - mask_indices_list=mask_indices_list, - masks_weight=masks_weight, - gram_global=None, - iteration=iteration, - ) - - self.backprop_loss(loss_accumulator) - - # Return total weighted loss and a dict of metrics to log - return loss_accumulator, metrics_dict | loss_dict - - @torch.no_grad() - def get_teacher_output( - self, - images, - *, - upperbound, - mask_indices_list, - teacher_temp, - n_masked_patches_tensor, - global_batch_size, - ): - n_crops, B_teacher, rgb, H, W = images.shape - - backbone_out = self.teacher.backbone(images.flatten(0, 1), is_training=True) - cls = backbone_out["x_norm_clstoken"] # [n_crops * B, D] - reg = backbone_out["x_storage_tokens"] # [n_crops * B, R, D] - ibot_patch = backbone_out["x_norm_patchtokens"] # [n_crops * B, P, D] - - R, D = reg.shape[-2:] - - # Multidistillation codepath: - # IBOT head only on patches that are masked for the student - n_tokens = ibot_patch.shape[1] - masked_patch_after_head = self.teacher.ibot_head(ibot_patch.flatten(0, 1), no_last_layer=True) - masked_patch_after_head = masked_patch_after_head.view(n_crops, -1, *masked_patch_after_head.shape[1:]) - masked_patch_after_head = self.broadcast_to_subgroups( - masked_patch_after_head, - over_dim=1, - global_batch_size=global_batch_size * n_tokens, - ) - buffer = torch.index_select(masked_patch_after_head.flatten(0, 1), dim=0, index=mask_indices_list) - masked_patch_after_head = self.teacher.ibot_head(buffer, only_last_layer=True) - - # DINO head on CLS tokens - cls_after_head = self.teacher.dino_head(cls, no_last_layer=True) # [n_crops * B, K] - cls_after_head = cls_after_head.view(n_crops, -1, *cls_after_head.shape[1:]) - cls_after_head = self.broadcast_to_subgroups(cls_after_head, over_dim=1, global_batch_size=global_batch_size) - B = cls_after_head.shape[1] - cls_after_head = cls_after_head.flatten(0, 1) - cls_after_head = self.teacher.dino_head(cls_after_head, only_last_layer=True) # [n_crops * B, K] - # End of multidistillation codepath - - # Center with sinkhorn-knopp - cls_centered = self.dino_loss.sinkhorn_knopp_teacher( - cls_after_head, teacher_temp=teacher_temp - ) # [n_crops * B, K] - cls_centered = cls_centered.unflatten(0, (n_crops, B)) # [n_crops, B, K] - masked_patch_centered = self.ibot_patch_loss.sinkhorn_knopp_teacher( - masked_patch_after_head, - teacher_temp=teacher_temp, - n_masked_patches_tensor=n_masked_patches_tensor, - ) # [n_masked_patches, K] - - return { - "cls_after_head": cls_after_head.unflatten(0, [n_crops, B]), # [n_crops, B, K] - "cls_centered": cls_centered, # [n_crops, B, K] - "masked_patch_centered": masked_patch_centered, # [n_masked_patches, K] - } diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/train/param_groups.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/train/param_groups.py deleted file mode 100644 index d8712a82042db21ae1e21e24f4ed1968205a9d32..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/train/param_groups.py +++ /dev/null @@ -1,180 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import logging -from collections import defaultdict - -logger = logging.getLogger("dinov3") - - -def get_vit_lr_decay_rate( - name, - lr_decay_rate=1.0, - num_layers=12, - force_is_backbone=False, - chunked_blocks=False, -): - """ - Calculate lr decay rate for different ViT blocks. - Args: - name (string): parameter name. - lr_decay_rate (float): base lr decay rate. - num_layers (int): number of ViT blocks. - Returns: - lr decay rate for the given parameter. - """ - layer_id = num_layers + 1 - if name.startswith("backbone") or force_is_backbone: - if ( - ".pos_embed" in name - or ".patch_embed" in name - or ".mask_token" in name - or ".cls_token" in name - or ".storage_tokens" in name - ): - layer_id = 0 - elif force_is_backbone and ( - "pos_embed" in name - or "patch_embed" in name - or "mask_token" in name - or "cls_token" in name - or "storage_tokens" in name - ): - layer_id = 0 - elif ".blocks." in name and ".residual." not in name: - layer_id = int(name[name.find(".blocks.") :].split(".")[2]) + 1 - elif chunked_blocks and "blocks." in name and "residual." not in name: - layer_id = int(name[name.find("blocks.") :].split(".")[2]) + 1 - elif "blocks." in name and "residual." not in name: - layer_id = int(name[name.find("blocks.") :].split(".")[1]) + 1 - - return lr_decay_rate ** (num_layers + 1 - layer_id) - - -def get_params_groups_with_decay(model, lr_decay_rate=1.0, patch_embed_lr_mult=1.0, dino_head_wd_multiplier=1.0): - chunked_blocks = False - if hasattr(model, "n_blocks"): - logger.info("chunked fsdp") - n_blocks = model.n_blocks - chunked_blocks = model.chunked_blocks - elif hasattr(model, "blocks"): - logger.info("first code branch") - n_blocks = len(model.blocks) - elif hasattr(model, "backbone"): - logger.info("second code branch") - n_blocks = len(model.backbone.blocks) - else: - logger.info("else code branch") - n_blocks = 0 - all_param_groups = [] - - for name, param in model.named_parameters(): - name = remove_fsdp_compile_names(name) - if not param.requires_grad: - continue - decay_rate = get_vit_lr_decay_rate( - name, - lr_decay_rate, - num_layers=n_blocks, - force_is_backbone=n_blocks > 0, - chunked_blocks=chunked_blocks, - ) - d = { - "name": name, - "params": param, - "is_last_layer": False, - "lr_multiplier": decay_rate, - "wd_multiplier": 1.0, - } - - if "dino_head" in name: - d["wd_multiplier"] = dino_head_wd_multiplier - - if "last_layer" in name: - d["is_last_layer"] = True - - # No weight-decay on biases, norm parameters, layer scale gamma, learned tokens and embeddings - if name.endswith("bias") or "norm" in name or "gamma" in name or "fourier_w" in name: - d["wd_multiplier"] = 0.0 - - if "patch_embed" in name: - d["lr_multiplier"] *= patch_embed_lr_mult - - all_param_groups.append(d) - logger.info(f"{name}: lr_multiplier: {d['lr_multiplier']}, wd_multiplier: {d['wd_multiplier']}") - - return all_param_groups - - -def fuse_params_groups(all_params_groups, keys=("lr_multiplier", "wd_multiplier", "is_last_layer")): - fused_params_groups = defaultdict(lambda: {"params": []}) - for d in all_params_groups: - identifier = "" - for k in keys: - identifier += k + str(d[k]) + "_" - - for k in keys: - fused_params_groups[identifier][k] = d[k] - fused_params_groups[identifier]["params"].append(d["params"]) - - return fused_params_groups.values() - - -def get_params_groups_with_decay_fsdp(model, lr_decay_rate=1.0, patch_embed_lr_mult=1.0, dino_head_wd_multiplier=1.0): - if hasattr(model, "module"): # SimpleFSDP - is_backbone = hasattr(model.module, "blocks") - n_blocks = len(model.module.blocks) if is_backbone else 0 - else: # FSDP2 - is_backbone = hasattr(model, "blocks") - n_blocks = len(model.blocks) if is_backbone else 0 - - all_param_groups = [] - - for name, param in model.named_parameters(): - name = remove_fsdp_compile_names(name) - if not param.requires_grad: - continue - decay_rate = get_vit_lr_decay_rate( - name, - lr_decay_rate, - num_layers=n_blocks, - force_is_backbone=n_blocks > 0, - chunked_blocks=False, - ) - d = { - "name": name, - "params": param, - "is_last_layer": False, - "lr_multiplier": decay_rate, - "wd_multiplier": 1.0, - } - - if "dino_head" in name: - d["wd_multiplier"] = dino_head_wd_multiplier - - if "last_layer" in name: - d["is_last_layer"] = True - - # No weight-decay on biases, norm parameters, layer scale gamma, learned tokens and embeddings - if name.endswith("bias") or "norm" in name or "gamma" in name or "fourier_w" in name: - d["wd_multiplier"] = 0.0 - - if "patch_embed" in name: - d["lr_multiplier"] *= patch_embed_lr_mult - - all_param_groups.append(d) - logger.info(f"{name}: lr_multiplier: {d['lr_multiplier']}, wd_multiplier: {d['wd_multiplier']}") - - return all_param_groups - - -def remove_fsdp_compile_names(name: str): - name = name.replace("_fsdp_wrapped_module.", "") # Added by FSDP - name = name.replace("_checkpoint_wrapped_module.", "") # Added by activation checkpointing for xFSDP - name = name.replace("parametrizations.", "") # Added by xFSDP - name = name.removesuffix(".original") # Added by xFSDP - name = name.replace("module.", "") # Added by xFSDP - name = name.replace("_orig_mod.", "") # Added by torch.compile - return name diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/train/ssl_meta_arch.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/train/ssl_meta_arch.py deleted file mode 100644 index 172d80b9ef2d87e534785b15904d0d9e472dcc27..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/train/ssl_meta_arch.py +++ /dev/null @@ -1,815 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import gc -import logging -from functools import partial - -import torch -from omegaconf import OmegaConf -from torch import Tensor, nn - -import dinov3.distributed as distributed -from dinov3.checkpointer import init_fsdp_model_from_checkpoint -from dinov3.configs import get_default_config -from dinov3.data import DataAugmentationDINO -from dinov3.fsdp.ac_compile_parallelize import ac_compile_parallelize -from dinov3.layers.dino_head import DINOHead -from dinov3.loss import DINOLoss, GramLoss, KoLeoLoss, KoLeoLossDistributed, iBOTPatchLoss -from dinov3.models import build_model_from_cfg -from dinov3.train.cosine_lr_scheduler import linear_warmup_cosine_decay -from dinov3.train.param_groups import fuse_params_groups, get_params_groups_with_decay_fsdp -from dinov3.utils import count_parameters - -logger = logging.getLogger("dinov3") - - -class SSLMetaArch(nn.Module): - """ - Modified version of SSLMetaArchCompilable including gram loss: - - Gram loss is used only if gram.use_loss is set to true - """ - - def __init__(self, cfg): - super().__init__() - - # assert cfg.multidistillation.enabled is False - assert cfg.crops.local_crops_number > 0 - assert cfg.ibot.separate_head is True - assert cfg.train.centering == "sinkhorn_knopp" - - # For some reason FULL_SHARD doesn't work - assert cfg.compute_precision.sharding_strategy == "SHARD_GRAD_OP" - - self.cfg = cfg - - student_model_dict = dict() - teacher_model_dict = dict() - gram_model_dict = dict() - - student_backbone, teacher_backbone, embed_dim = build_model_from_cfg(cfg) - torch.cuda.empty_cache() - gc.collect() - gram_backbone, _ = build_model_from_cfg(cfg, only_teacher=True) - logger.info(f"Number of parameters: {count_parameters(student_backbone)}") - student_model_dict["backbone"] = student_backbone - teacher_model_dict["backbone"] = teacher_backbone - gram_model_dict["backbone"] = gram_backbone - logger.info(f"OPTIONS -- architecture : embed_dim: {embed_dim}") - - self.embed_dim = embed_dim # D - self.dino_out_dim = cfg.dino.head_n_prototypes # K - - logger.info("OPTIONS -- DINO") - logger.info(f"OPTIONS -- DINO -- loss_weight: {cfg.dino.loss_weight}") - logger.info(f"OPTIONS -- DINO -- global_ignore_diagonal: {cfg.dino.global_ignore_diagonal}") - logger.info(f"OPTIONS -- DINO -- head_n_prototypes: {cfg.dino.head_n_prototypes}") - logger.info(f"OPTIONS -- DINO -- head_bottleneck_dim: {cfg.dino.head_bottleneck_dim}") - logger.info(f"OPTIONS -- DINO -- head_hidden_dim: {cfg.dino.head_hidden_dim}") - logger.info(f"OPTIONS -- DINO -- head_norm_last_layer: {cfg.dino.head_norm_last_layer}") - dino_head_class = partial( - DINOHead, - in_dim=embed_dim, - out_dim=cfg.dino.head_n_prototypes, - hidden_dim=cfg.dino.head_hidden_dim, - bottleneck_dim=cfg.dino.head_bottleneck_dim, - nlayers=cfg.dino.head_nlayers, - ) - student_model_dict["dino_head"] = dino_head_class() - teacher_model_dict["dino_head"] = dino_head_class() - self.dino_loss = DINOLoss(self.dino_out_dim) - - logger.info("OPTIONS -- KOLEO") - logger.info(f"OPTIONS -- KOLEO -- loss_weight: {cfg.dino.koleo_loss_weight}") - logger.info(f"OPTIONS -- KOLEO -- distributed: {cfg.dino.koleo_loss_distributed}") - if cfg.dino.koleo_loss_distributed: - logger.info(f"OPTIONS -- KOLEO -- topk: {cfg.dino.koleo_topk}") - logger.info( - f"OPTIONS -- KOLEO -- distributed_loss_group_size: {cfg.dino.koleo_distributed_loss_group_size}" - ) - assert cfg.dino.koleo_distributed_replicas == 0, ( - "Option `dino.koleo_distributed_replicas` is no longer supported" - ) - self.koleo_loss = KoLeoLossDistributed( - topk=cfg.dino.koleo_topk, - loss_group_size=cfg.dino.koleo_distributed_loss_group_size, - ) - else: - assert cfg.dino.koleo_topk == 1, "Non-distributed KoLeo loss only supports `dino.koleo_topk=1`" - self.koleo_loss = KoLeoLoss() - - logger.info("OPTIONS -- IBOT") - logger.info(f"OPTIONS -- IBOT -- loss_weight: {cfg.ibot.loss_weight}") - logger.info(f"OPTIONS -- IBOT masking -- ibot_mask_ratio_tuple: {cfg.ibot.mask_ratio_min_max}") - logger.info(f"OPTIONS -- IBOT masking -- ibot_mask_sample_probability: {cfg.ibot.mask_sample_probability}") - - assert 0 <= cfg.ibot.mask_ratio_min_max[0] < cfg.ibot.mask_ratio_min_max[1] <= 1, ( - "provide a valid cfg.ibot.mask_ratio_min_max" - ) - assert 0 <= cfg.ibot.mask_sample_probability <= 1, "provide a positive mask probability for ibot" - logger.info(f"OPTIONS -- IBOT -- head_n_prototypes: {cfg.ibot.head_n_prototypes}") - logger.info(f"OPTIONS -- IBOT -- head_bottleneck_dim: {cfg.ibot.head_bottleneck_dim}") - logger.info(f"OPTIONS -- IBOT -- head_hidden_dim: {cfg.ibot.head_hidden_dim}") - logger.info(f"OPTIONS -- IBOT -- head_norm_last_layer: {cfg.ibot.head_norm_last_layer}") - ibot_head_class = partial( - DINOHead, - in_dim=embed_dim, - out_dim=cfg.ibot.head_n_prototypes, - hidden_dim=cfg.ibot.head_hidden_dim, - bottleneck_dim=cfg.ibot.head_bottleneck_dim, - nlayers=cfg.ibot.head_nlayers, - ) - student_model_dict["ibot_head"] = ibot_head_class() - teacher_model_dict["ibot_head"] = ibot_head_class() - self.ibot_patch_loss = iBOTPatchLoss(cfg.ibot.head_n_prototypes) - - # Build student and teacher models - self.student = nn.ModuleDict(student_model_dict) - self.teacher = nn.ModuleDict(teacher_model_dict) - self.model_ema = self.teacher # this may be overwritten for distillation - logger.info(f"Student and Teacher are built: they are both {cfg.student.arch} network.") - - if cfg.distillation.enabled: - self._setup_distillation() - # No grad is needed for these two - self.teacher.requires_grad_(False) - self.model_ema.requires_grad_(False) - self.ema_params_lists = None - - # getting config params fixed: - self.n_local_crops = self.cfg.crops.local_crops_number - self.is_distillation_enabled = self.cfg.distillation.enabled - self.dino_global_ignore_diagonal = self.cfg.dino.global_ignore_diagonal - self.dino_loss_weight = self.cfg.dino.loss_weight - self.dino_koleo_loss_weight = self.cfg.dino.koleo_loss_weight - self.ibot_loss_weight = self.cfg.ibot.loss_weight - - # Local loss reweighting - if self.cfg.dino.reweight_dino_local_loss: - iter_per_epoch = cfg.train.OFFICIAL_EPOCH_LENGTH - total_iterations = iter_per_epoch * cfg.optim.epochs - schedule_cfg = cfg.dino.local_loss_weight_schedule - self.dino_local_loss_schedule = linear_warmup_cosine_decay( - start=schedule_cfg.start, - peak=schedule_cfg.peak, - end=schedule_cfg.end, - warmup_iterations=iter_per_epoch * schedule_cfg.warmup_epochs, - total_iterations=total_iterations, - cosine_iterations=( - iter_per_epoch * schedule_cfg.cosine_epochs if "cosine_epochs" in schedule_cfg else None - ), - ) - - # Gram - self.gram_use_loss = self.cfg.gram.use_loss - self.gram_ema_teacher = False - self.has_gram_teacher = False - self.gram_teacher_initialized = False - if self.gram_use_loss: - # Gram regularization - self.gram_loss = GramLoss( - apply_norm=self.cfg.gram.normalized, - remove_only_teacher_neg=self.cfg.gram.remove_only_teacher_neg, - remove_neg=self.cfg.gram.remove_neg, - ) - # Construct gram teacher - self.has_gram_teacher = True if not cfg.gram.ema_teacher else False - if self.has_gram_teacher: - self.gram_teacher = nn.ModuleDict(gram_model_dict) - self.gram_teacher.requires_grad_(False) - logger.info(f"Gram teacher parameter at init: {next(self.gram_teacher.named_parameters())}") - else: - self.gram_teacher = None - - self.gram_loss_weight = self.cfg.gram.loss_weight - if self.cfg.gram.get("loss_weight_schedule"): - iter_per_epoch = cfg.train.OFFICIAL_EPOCH_LENGTH - total_iterations = iter_per_epoch * cfg.optim.epochs - schedule_cfg = self.cfg.gram.loss_weight_schedule - self.gram_loss_schedule = linear_warmup_cosine_decay( - start=schedule_cfg.start, - peak=schedule_cfg.peak, - end=schedule_cfg.end, - warmup_iterations=iter_per_epoch * schedule_cfg.warmup_epochs, - total_iterations=total_iterations, - cosine_iterations=( - iter_per_epoch * schedule_cfg.cosine_epochs if "cosine_epochs" in schedule_cfg else None - ), - ) - logger.info(f"Applying gram loss weight schedule instead of `cfg.gram.loss_weight`: {schedule_cfg}") - else: - self.gram_loss_schedule = None - self.gram_ema_teacher = self.cfg.gram.ema_teacher # If true use the EMA_teacher as gram_teacher - self.gram_ckpt = self.cfg.gram.ckpt # Checkpoint to the first gram teacher model - self.gram_img_level = self.cfg.gram.img_level # Apply the loss on the image, if false on the batch - self.gram_tokens_used = self.cfg.gram.tokens_used # Any value in ["all", "masked", "unmasked"] - # Update the teacher frequently - self.gram_rep_update = self.cfg.gram.rep_update # bool, if yes the gram teacher will be updated at the freq - self.gram_update_frequency = self.cfg.gram.update_frequency # defined by this var update_frequency - self.gram_it_first_update = self.cfg.gram.it_first_update # after iteration it_first_update is passed. - self.gram_it_load_ema_teacher = ( - self.cfg.gram.it_load_ema_teacher - ) # after iteration it_load_ema the ema teacher is loaded into the gram teacher - self.gram_compute_stats = self.cfg.gram.compute_stats # whether to compute auxiliary stats - self.gram_params_lists = None - - if self.gram_ema_teacher and self.gram_ckpt is not None: - raise ValueError( - "Cannot use both `gram.ema_teacher` and `gram.ckpt` at the same time. Please set one of them to False." - ) - if self.gram_ckpt is None and self.gram_it_load_ema_teacher < 0: - raise ValueError( - "If no gram checkpoint is provided, `gram.it_load_ema_teacher` must be set to a non-negative value." - ) - - assert not (self.gram_ema_teacher and self.gram_rep_update) - assert self.gram_tokens_used in ["all", "masked", "unmasked"] - # Currently using masked/unmasked not handle at the image-level - if self.gram_tokens_used in ["masked", "unmasked"]: - assert self.gram_img_level is False - - logger.info("OPTIONS -- GRAM") - logger.info(f"OPTIONS -- GRAM -- loss_weight: {cfg.gram.loss_weight}") - logger.info(f"OPTIONS -- GRAM -- ema teacher: {cfg.gram.ema_teacher}") - logger.info(f"OPTIONS -- GRAM -- ckpt: {cfg.gram.ckpt}") - if self.cfg.gram.rep_update: - logger.info(f"OPTIONS -- GRAM -- repeated update: {cfg.gram.rep_update}") - logger.info(f"OPTIONS -- GRAM -- update freq: {cfg.gram.update_frequency}") - logger.info(f"OPTIONS -- GRAM -- iteration first update: {cfg.gram.it_first_update}") - - logger.info(f"OPTIONS -- GRAM -- tokens_used: {cfg.gram.tokens_used}") - logger.info(f"OPTIONS -- GRAM -- apply normalization: {cfg.gram.normalized}") - logger.info(f"OPTIONS -- GRAM -- img_level: {cfg.gram.img_level}") - logger.info(f"OPTIONS -- GRAM -- remove_neg: {cfg.gram.remove_neg}") - logger.info(f"OPTIONS -- GRAM -- remove_only_teacher_neg: {cfg.gram.remove_only_teacher_neg}") - - if cfg.crops.gram_teacher_crops_size is None and self.has_gram_teacher: - raise ValueError("cfg.crops.gram_teacher_crops_size must be set to use gram loss") - if cfg.crops.gram_teacher_crops_size is not None and self.gram_ema_teacher: - raise ValueError("cfg.crops.gram_teacher_crops_size shoud be None when gram.ema_teacher=True") - - self.student_crop_size = cfg.crops.global_crops_size - self.gram_global_teacher_resize_method = cfg.gram.global_teacher_resize_method - self.gram_global_teacher_resize_antialias = cfg.gram.global_teacher_resize_antialias - logger.info(f"OPTIONS -- global crops student/teacher size: {self.student_crop_size}") - logger.info(f"OPTIONS -- global crops GRAM teacher size: {cfg.crops.gram_teacher_crops_size}") - logger.info(f"OPTIONS -- global crops GRAM teacher resize method: {cfg.gram.global_teacher_resize_method}") - logger.info( - f"OPTIONS -- global crops GRAM teacher resize antialias: {cfg.gram.global_teacher_resize_antialias}" - ) - - def _setup_distillation(self): - logger.info(f"Performing distillation from {self.cfg.distillation.full_cfg_path}") - - default_cfg = get_default_config() - distillation_cfg = OmegaConf.load(self.cfg.distillation.full_cfg_path) - distillation_cfg = OmegaConf.merge(default_cfg, distillation_cfg) - - assert distillation_cfg.ibot.separate_head is True - assert distillation_cfg.ibot.head_n_prototypes == self.cfg.ibot.head_n_prototypes - assert distillation_cfg.dino.head_n_prototypes == self.cfg.dino.head_n_prototypes - assert distillation_cfg.student.patch_size == self.cfg.student.patch_size - - teacher_model_dict = dict() - - backbone, embed_dim = build_model_from_cfg(distillation_cfg, only_teacher=True) - teacher_model_dict["backbone"] = backbone - - teacher_model_dict["dino_head"] = DINOHead( - in_dim=embed_dim, - out_dim=distillation_cfg.dino.head_n_prototypes, - hidden_dim=distillation_cfg.dino.head_hidden_dim, - bottleneck_dim=distillation_cfg.dino.head_bottleneck_dim, - nlayers=distillation_cfg.dino.head_nlayers, - ) - teacher_model_dict["ibot_head"] = DINOHead( - in_dim=embed_dim, - out_dim=distillation_cfg.ibot.head_n_prototypes, - hidden_dim=distillation_cfg.ibot.head_hidden_dim, - bottleneck_dim=distillation_cfg.ibot.head_bottleneck_dim, - nlayers=distillation_cfg.ibot.head_nlayers, - ) - self.teacher = nn.ModuleDict(teacher_model_dict) - - def init_weights(self) -> None: - # All weights are set to `nan` to ensure we initialize everything explicitly - self.student.backbone.init_weights() - self.student.dino_head.init_weights() - self.student.ibot_head.init_weights() - self.dino_loss.init_weights() - self.ibot_patch_loss.init_weights() - self.model_ema.load_state_dict(self.student.state_dict()) - if self.has_gram_teacher: - if self.gram_ckpt is not None: - logger.info(f"Loading pretrained weights from {self.gram_ckpt}") - init_fsdp_model_from_checkpoint( - self.gram_teacher, - self.gram_ckpt, - skip_load_keys=[ - "dino_head", - "ibot_head", - "dino_loss.center", - "ibot_patch_loss.center", - ], - keys_not_sharded=["backbone.rope_embed.periods", "qkv.bias_mask"], - process_group=distributed.get_default_process_group(), - ) - self.gram_teacher_initialized = True - else: - raise ValueError(f"Provide a correct path to {self.gram_ckpt}") - self.gram_teacher.requires_grad_(False) - self.gram_teacher.eval() - if self.cfg.student.resume_from_teacher_chkpt: - logger.info(f"Loading pretrained weights from {self.cfg.student.resume_from_teacher_chkpt}") - init_fsdp_model_from_checkpoint( - self.student, - self.cfg.student.resume_from_teacher_chkpt, - skip_load_keys=["dino_loss.center", "ibot_patch_loss.center"], - keys_not_sharded=["backbone.rope_embed.periods", "qkv.bias_mask"], - process_group=distributed.get_process_subgroup(), - ) - self.model_ema.load_state_dict(self.student.state_dict()) - if self.cfg.distillation.enabled: - if self.cfg.distillation.checkpoint_path != "ignore": - logger.info(f"Loading teacher to distil from : {self.cfg.distillation.checkpoint_path}") - init_fsdp_model_from_checkpoint( - self.teacher, - self.cfg.distillation.checkpoint_path, - skip_load_keys=["dino_loss.center", "ibot_patch_loss.center"], - keys_not_sharded=["backbone.rope_embed.periods", "qkv.bias_mask"], - ) - else: - logger.info("Init teacher to distil from, used for testing purpose only") - self.teacher.backbone.init_weights() - self.teacher.dino_head.init_weights() - self.teacher.ibot_head.init_weights() - logger.info(f"Performing distillation from: {self.teacher}") - - def forward_backward( - self, data, *, teacher_temp, iteration=0, **ignored_kwargs - ) -> tuple[Tensor, dict[str, float | Tensor]]: - del ignored_kwargs - metrics_dict = {} - - # Shapes - n_global_crops = 2 - n_local_crops = self.n_local_crops # self.cfg.crops.local_crops_number - B = data["collated_local_crops"].shape[0] // n_local_crops - assert data["collated_global_crops"].shape[0] == n_global_crops * B - metrics_dict["local_batch_size"] = B - metrics_dict["global_batch_size"] = data["global_batch_size"] - - global_crops = data["collated_global_crops"].cuda(non_blocking=True) - local_crops = data["collated_local_crops"].cuda(non_blocking=True) - masks = data["collated_masks"].cuda(non_blocking=True) - mask_indices_list = data["mask_indices_list"].cuda(non_blocking=True) - masks_weight = data["masks_weight"].cuda(non_blocking=True) - n_masked_patches_tensor = data["n_masked_patches"].cuda(non_blocking=True) - - if self.has_gram_teacher: - assert "collated_gram_teacher_crops" in data, ( - "no gram teacher crops in the data, have you set cfg.crops.gram_teacher_crops_size?" - ) - gram_teacher_crops = data["collated_gram_teacher_crops"].cuda(non_blocking=True) - else: - gram_teacher_crops = None - - # Teacher output (will trigger an all-gather to unshard) - teacher_global = self.get_teacher_output( - global_crops.unflatten(0, (n_global_crops, B)), - teacher_temp=teacher_temp, - n_masked_patches_tensor=n_masked_patches_tensor, - mask_indices_list=mask_indices_list, - upperbound=data["upperbound"], - ) - - # Student output (will trigger an all-gather to unshard) - student_global, student_local = self.get_student_output( - global_crops=global_crops.unflatten(0, (n_global_crops, B)), - local_crops=local_crops.unflatten(0, (n_local_crops, B)), - upperbound=data["upperbound"], - masks=masks, - mask_indices_list=mask_indices_list, - ) - - # Gram output - if self.gram_use_loss: - gram_global = self.get_gram_teacher_output( - gram_teacher_crops.unflatten(0, (n_global_crops, B)) if gram_teacher_crops is not None else None, - masks=masks, - teacher_global=teacher_global, - student_global=student_global, - student_global_crops_size=global_crops.shape[-1], - ) - else: - gram_global = {} - - # Compute losses and backprop - loss_accumulator, loss_dict = self.compute_losses( - teacher_global=teacher_global, - student_global=student_global, - student_local=student_local, - gram_global=gram_global, - masks=masks, - mask_indices_list=mask_indices_list, - masks_weight=masks_weight, - iteration=iteration, - ) - - self.backprop_loss(loss_accumulator) - - # Return total weighted loss and a dict of metrics to log - return loss_accumulator, metrics_dict | loss_dict - - @torch.no_grad() - def get_teacher_output( - self, - images, - *, - upperbound, - mask_indices_list, - teacher_temp, - n_masked_patches_tensor, - ): - n_crops, B, rgb, H, W = images.shape - images = images.flatten(0, 1) - - backbone_out = self.teacher.backbone(images, is_training=True) - cls = backbone_out["x_norm_clstoken"] # [n_crops * B, D] - reg = backbone_out["x_storage_tokens"] # [n_crops * B, R, D] - ibot_patch = backbone_out["x_norm_patchtokens"] # [n_crops * B, P, D] - - # IBOT head only on patches that are masked for the student - buffer = torch.index_select(ibot_patch.flatten(0, 1), dim=0, index=mask_indices_list) - masked_patch_after_head = self.teacher.ibot_head(buffer) - - # DINO head on CLS tokens - cls_after_head = self.teacher.dino_head(cls) # [n_crops * B, K] - - # Center with sinkhorn-knopp - cls_centered = self.dino_loss.sinkhorn_knopp_teacher( - cls_after_head, teacher_temp=teacher_temp - ) # [n_crops * B, K] - cls_centered = cls_centered.unflatten(0, (n_crops, B)) # [n_crops, B, K] - masked_patch_centered = self.ibot_patch_loss.sinkhorn_knopp_teacher( - masked_patch_after_head, - teacher_temp=teacher_temp, - n_masked_patches_tensor=n_masked_patches_tensor, - ) # [n_masked_patches, K] - - return { - "cls_pre_head": cls.unflatten(0, [n_crops, B]), # [n_crops, B, D] - "reg_pre_head": reg.unflatten(0, [n_crops, B]), # [n_crops, B, R, D] - "patch_pre_head": ibot_patch.unflatten(0, [n_crops, B]), # [n_crops, B, P, D] - "cls_after_head": cls_after_head.unflatten(0, [n_crops, B]), # [n_crops, B, K] - "cls_centered": cls_centered, # [n_crops, B, K] - "masked_patch_centered": masked_patch_centered, # [n_masked_patches, K] - } - - def get_gram_teacher_output(self, images, *, masks, teacher_global, student_global, student_global_crops_size): - # Get student patch features - student_patches = student_global["patch_pre_head"].flatten(0, 1) # [n_crops * B, P, D] - - # Get gram targets - if self.gram_ema_teacher: - teacher_patches = teacher_global["patch_pre_head"].flatten(0, 1) # [n_crops * B, P, D] - else: - if not self.gram_teacher_initialized: - raise ValueError("Gram teacher has not been initialized. Load a checkpoint or from the EMA teacher.") - n_crops, B, rgb, H, W = images.shape - images = images.flatten(0, 1) # [n_crops * B, rgb, H, W] - - with torch.no_grad(): - backbone_out = self.gram_teacher.backbone(images, is_training=True) - teacher_patches = backbone_out["x_norm_patchtokens"] # [n_crops * B, P_T, D] - - # Downsample Gram teacher features if needed - if teacher_patches.shape[1] != student_patches.shape[1]: - N = H // self.cfg.student.patch_size - assert teacher_patches.shape[1] == N**2 - N_student = student_global_crops_size // self.cfg.student.patch_size - assert student_patches.shape[1] == N_student**2 - patches_hw = teacher_patches.transpose(-2, -1).unflatten(-1, (N, N)) # [n_crops * B, D, N, N] - patches_hw = torch.nn.functional.interpolate( - patches_hw, - size=(N_student, N_student), - mode=self.gram_global_teacher_resize_method, - align_corners=False, - antialias=self.gram_global_teacher_resize_antialias, - ) - teacher_patches = patches_hw.flatten(-2, -1).transpose( - -2, -1 - ) # [n_crops * B, N_student * N_student, D] - assert teacher_patches.shape == student_patches.shape - - # Select the patches to be considered in the loss - orig_student_patches = student_patches - orig_teacher_patches = teacher_patches - if self.gram_tokens_used == "masked": - student_patches = student_patches[masks] - teacher_patches = teacher_patches[masks] - elif self.gram_tokens_used == "unmasked": - student_patches = student_patches[~masks] - teacher_patches = teacher_patches[~masks] - - return { - "student_patches": student_patches, # [n_crops * B, P, D] or [n_selected_patches, D] - "teacher_patches": teacher_patches, # [n_crops * B, P, D] or [n_selected_patches, D] - # Unmasked patches, for computing statistics - "orig_student_patches": orig_student_patches, # [n_crops * B, P, D] - "orig_teacher_patches": orig_teacher_patches, # [n_crops * B, P, D] - } - - def get_student_output(self, *, global_crops, local_crops, upperbound, masks, mask_indices_list): - n_global_crops, B, rgb, H, W = global_crops.shape - n_local_crops, B, rgb, H, W = local_crops.shape - - global_crops = global_crops.flatten(0, 1) - - # Forward global and local crops through the student backbone jointly - global_out, local_out = self.student.backbone( - [global_crops, local_crops.flatten(0, 1)], - masks=[masks if not self.is_distillation_enabled else None, None], - is_training=True, - ) - g_cls, g_reg, g_patch = ( - global_out["x_norm_clstoken"], - global_out["x_storage_tokens"], - global_out["x_norm_patchtokens"], - ) - l_cls, l_reg, l_patch = ( - local_out["x_norm_clstoken"], - local_out["x_storage_tokens"], - local_out["x_norm_patchtokens"], - ) - - # IBOT head only on masked patches - masked_patches_pre_head = torch.index_select(g_patch.flatten(0, 1), dim=0, index=mask_indices_list) - global_masked_patch_after_head = self.student.ibot_head(masked_patches_pre_head) - - # DINO head on CLS tokens (all in one pass) - buffer = [ - g_cls, # [n_global_crops * B, D] - l_cls, # [n_local_crops * B, D] - ] - sizes = [x.shape[0] for x in buffer] - buffer = torch.cat(buffer, dim=0) # [n_global_crops * B + n_local_crops * B, D] - buffer = self.student.dino_head(buffer) # [n_global_crops * B + n_local_crops * B, K] - buffer = torch.split_with_sizes(buffer, sizes, dim=0) - - global_out = { - "cls_pre_head": g_cls.unflatten(0, [n_global_crops, B]), # [n_global_crops, B, D] - "reg_pre_head": g_reg.unflatten(0, [n_global_crops, B]), # [n_global_crops, B, R, D] - "patch_pre_head": g_patch.unflatten(0, [n_global_crops, B]), # [n_global_crops, B, P, D] - "cls_after_head": buffer[0].unflatten(0, [n_global_crops, B]), # [n_global_crops, B, K], - "masked_patch_after_head": global_masked_patch_after_head, # [n_masked_patches, K] - "masked_patch_pre_head": masked_patches_pre_head, # [n_masked_patches, D] - } - local_out = { - "cls_pre_head": l_cls.unflatten(0, [n_local_crops, B]), # [n_local_crops, B, D] - "reg_pre_head": l_reg.unflatten(0, [n_local_crops, B]), # [n_local_crops, B, R, D] - "patch_pre_head": l_patch.unflatten(0, [n_local_crops, B]), # [n_local_crops, B, P, D] - "cls_after_head": buffer[1].unflatten(0, [n_local_crops, B]), # [n_local_crops, B, K], - } - - return global_out, local_out - - def compute_losses( - self, - *, - teacher_global, - student_global, - student_local, - gram_global, - masks, - mask_indices_list, - masks_weight, - iteration, - ): - n_global_crops = student_global["cls_after_head"].shape[0] - n_local_crops = student_local["cls_after_head"].shape[0] - loss_dict = {} - loss_accumulator = 0.0 - - # Loss scales like in DINOv2, these are multiplied with the loss weights from the config - dino_global_terms = ( - n_global_crops * (n_global_crops - 1) if self.dino_global_ignore_diagonal else n_global_crops**2 - ) - dino_local_terms = n_global_crops * n_local_crops - dino_global_scale = dino_global_terms / (dino_global_terms + dino_local_terms) - dino_local_scale = dino_local_terms / (dino_global_terms + dino_local_terms) - koleo_scale = n_global_crops - - # DINO local loss: compare post-head CLS tokens: student(local crops) vs. teacher(global crops) - dino_local_crops_loss = self.dino_loss( - student_logits=student_local["cls_after_head"], - teacher_probs=teacher_global["cls_centered"], - ) - loss_dict["dino_local_crops_loss"] = dino_local_crops_loss - - # Reweighting of DINO loss - if self.cfg.dino.reweight_dino_local_loss: - local_weight = self.dino_local_loss_schedule[iteration] - else: - local_weight = 1.0 - - loss_dict["dino_local_loss_weight"] = local_weight - loss_accumulator += self.dino_loss_weight * dino_local_scale * local_weight * dino_local_crops_loss - - # DINO global loss: compare post-head CLS tokens: student(global crops) vs. teacher(global crops) - dino_global_crops_loss = self.dino_loss( - student_logits=student_global["cls_after_head"], - teacher_probs=teacher_global["cls_centered"], - ignore_diagonal=self.dino_global_ignore_diagonal, - ) - loss_dict["dino_global_crops_loss"] = dino_global_crops_loss - loss_accumulator += self.dino_loss_weight * dino_global_scale * dino_global_crops_loss - - # Koleo: regularize pre-head CLS tokens of student(global crops) - koleo_loss = sum(self.koleo_loss(x) for x in student_global["cls_pre_head"]) / n_global_crops - loss_dict["koleo_loss"] = koleo_loss - loss_accumulator += self.dino_koleo_loss_weight * koleo_scale * koleo_loss - - # IBOT loss - ibot_patch_loss = self.ibot_patch_loss.forward_masked( - student_global["masked_patch_after_head"], - teacher_global["masked_patch_centered"], - student_masks_flat=masks, - n_masked_patches=mask_indices_list.shape[0], - masks_weight=masks_weight, - ) - loss_dict["ibot_loss"] = ibot_patch_loss - loss_accumulator += self.ibot_loss_weight * ibot_patch_loss - - # Gram loss - if self.gram_use_loss: - gram_loss = self.gram_loss( - gram_global["student_patches"], - gram_global["teacher_patches"], - img_level=self.gram_img_level, - ) - - if self.gram_loss_schedule is not None: - gram_loss_weight = self.gram_loss_schedule[iteration] - else: - gram_loss_weight = self.gram_loss_weight - - loss_dict["gram_loss_weight"] = gram_loss_weight - loss_accumulator += gram_loss * gram_loss_weight - loss_dict["gram_loss"] = gram_loss - - if self.gram_compute_stats: - with torch.no_grad(): - # Save stats over masked / unmasked tokens - gram_loss_masked = self.gram_loss( - gram_global["orig_student_patches"][masks].detach(), - gram_global["orig_teacher_patches"][masks], - img_level=False, - ) - loss_dict["stats_only/masked_gram_loss"] = gram_loss_masked - gram_loss_unmasked = self.gram_loss( - gram_global["orig_student_patches"][~masks].detach(), - gram_global["orig_teacher_patches"][~masks], - img_level=False, - ) - loss_dict["stats_only/unmasked_gram_loss"] = gram_loss_unmasked - - return loss_accumulator, loss_dict - - @torch.no_grad() - def gram_load_ema_teacher(self): - if self.has_gram_teacher: - skip_load_prefixes = ["dino_head.", "ibot_head."] - self.gram_teacher.load_state_dict( - { - k: v - for k, v in self.model_ema.state_dict().items() - if not any(k.startswith(prefix) for prefix in skip_load_prefixes) - } - ) - self.gram_teacher.requires_grad_(False) - self.gram_teacher.eval() - self.gram_teacher_initialized = True - - def train(self): - super().train() - self.teacher.eval() - if self.has_gram_teacher: - self.gram_teacher.eval() - - def forward(self, inputs): - raise NotImplementedError - - def backprop_loss(self, loss): - loss.backward() - - def update_ema(self, m): - if self.ema_params_lists is None: - student_param_list = [] - teacher_param_list = [] - for k in self.student.keys(): - for ms, mt in zip(self.student[k].parameters(), self.model_ema[k].parameters()): - student_param_list += [ms] - teacher_param_list += [mt] - self.ema_params_lists = (student_param_list, teacher_param_list) - else: - student_param_list, teacher_param_list = self.ema_params_lists - with torch.no_grad(): - torch._foreach_mul_(teacher_param_list, m) - torch._foreach_add_(teacher_param_list, student_param_list, alpha=1 - m) - - def update_gram(self, m=0): - if not self.has_gram_teacher: - return - logger.info("Updating gram teacher with teacher weights.") - if self.gram_params_lists is None: - teacher_param_list = [] - gramteacher_param_list = [] - for k in self.gram_teacher.keys(): - for mgt, mt in zip(self.gram_teacher[k].parameters(), self.teacher[k].parameters()): - gramteacher_param_list += [mgt] - teacher_param_list += [mt] - self.gram_params_lists = (gramteacher_param_list, teacher_param_list) - else: - gramteacher_param_list, teacher_param_list = self.gram_params_lists - - with torch.no_grad(): - torch._foreach_mul_(gramteacher_param_list, m) - torch._foreach_add_(gramteacher_param_list, teacher_param_list, alpha=1 - m) - - def build_data_augmentation_dino(self, cfg): - return DataAugmentationDINO( - cfg.crops.global_crops_scale, - cfg.crops.local_crops_scale, - cfg.crops.local_crops_number, - global_crops_size=cfg.crops.global_crops_size, - local_crops_size=cfg.crops.local_crops_size, - gram_teacher_crops_size=cfg.crops.gram_teacher_crops_size, - gram_teacher_no_distortions=cfg.crops.gram_teacher_no_distortions, - local_crops_subset_of_global_crops=cfg.crops.localcrops_subset_of_globalcrops, - share_color_jitter=cfg.crops.share_color_jitter, - horizontal_flips=cfg.crops.horizontal_flips, - mean=cfg.crops.rgb_mean, - std=cfg.crops.rgb_std, - ) - - def get_maybe_fused_params_for_submodel(self, m: nn.Module): - params_groups = get_params_groups_with_decay_fsdp( - model=m, - lr_decay_rate=self.cfg.optim.layerwise_decay, - patch_embed_lr_mult=self.cfg.optim.patch_embed_lr_mult, - dino_head_wd_multiplier=self.cfg.optim.dino_head_wd_multiplier, - ) - if self.cfg.optim.multi_tensor_optim: - fused_params_groups = fuse_params_groups(params_groups) - logger.info("fusing param groups") - - for g in fused_params_groups: - g["foreach"] = True - g["fused"] = True - return fused_params_groups - else: - return params_groups - - def get_params_groups(self): - all_params_groups = [] - for name, m in self.student.items(): - logger.info(f"Getting paramer groups for {name}") - all_params_groups += self.get_maybe_fused_params_for_submodel(m) - return all_params_groups - - def prepare_for_distributed_training(self) -> None: - process_subgroup = distributed.get_process_subgroup() - default_process_group = distributed.get_default_process_group() - inference_only_models = [self.model_ema] - inference_only_models_process_groups = [process_subgroup] - if self.has_gram_teacher: - inference_only_models.append(self.gram_teacher) - inference_only_models_process_groups.append(default_process_group) - if self.cfg.distillation.enabled: - inference_only_models.append(self.teacher) - inference_only_models_process_groups.append(default_process_group) - ac_compile_parallelize( - trained_model=self.student, - inference_only_models=inference_only_models, - cfg=self.cfg, - trained_model_process_group=process_subgroup, - inference_only_models_process_groups=inference_only_models_process_groups, - ) - - def broadcast_to_subgroups(self, tensor, over_dim, global_batch_size=None): - """ - This is an operation that takes a tensor from the default process group, gathers it, stacks it, then scatters it within a smaller process subgroup - """ - world_size = distributed.get_world_size() - subgroup_size = distributed.get_subgroup_size() - gathered = [torch.zeros_like(tensor) for _ in range(world_size)] - - torch.distributed.all_gather(gathered, tensor) - catted = torch.cat(gathered, dim=over_dim) - if global_batch_size is not None: - catted = catted.narrow(dim=over_dim, start=0, length=global_batch_size) - - return catted.chunk(subgroup_size, dim=over_dim)[distributed.get_subgroup_rank()].clone() diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/train/train.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/train/train.py deleted file mode 100644 index fe78876571b7c0b09b889422fb50e6e5683759f4..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/train/train.py +++ /dev/null @@ -1,637 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import argparse -import copy -import gc -import logging -import math -import os -import sys -from functools import partial -from pathlib import Path - -import torch -import torch.distributed -from torch.distributed._tensor import DTensor - -import dinov3.distributed as distributed -from dinov3.checkpointer import ( - find_latest_checkpoint, - keep_checkpoint_copy, - keep_last_n_checkpoints, - load_checkpoint, - register_dont_save_hooks, - save_checkpoint, -) -from dinov3.configs import setup_config, setup_job, setup_multidistillation -from dinov3.data import ( - MaskingGenerator, - SamplerType, - collate_data_and_cast, - make_data_loader, - make_dataset, - CombinedDataLoader, -) -from dinov3.logging import MetricLogger, setup_logging -from dinov3.train.cosine_lr_scheduler import CosineScheduler, linear_warmup_cosine_decay -from dinov3.train.multidist_meta_arch import MultiDistillationMetaArch -from dinov3.train.ssl_meta_arch import SSLMetaArch - -assert torch.__version__ >= (2, 1) -torch.backends.cuda.matmul.allow_tf32 = True # pytorch 1.12 sets this to false by default -torch.backends.cudnn.benchmark = False # True - -logger = logging.getLogger("dinov3") - - -def get_args_parser(add_help: bool = True): - parser = argparse.ArgumentParser("DINOv3 training", add_help=add_help) - parser.add_argument("--config-file", default="", metavar="FILE", help="path to config file") - parser.add_argument( - "--no-resume", - action="store_true", - help="Whether to not attempt to resume from the checkpoint directory. ", - ) - parser.add_argument("--eval-only", action="store_true", help="perform evaluation only") - parser.add_argument("--eval", type=str, default="", help="Eval type to perform") - parser.add_argument( - "--eval_pretrained_weights", - type=str, - default="", - help="Path to pretrained weights", - ) - parser.add_argument( - "opts", - help=""" -Modify config options at the end of the command. For Yacs configs, use -space-separated "PATH.KEY VALUE" pairs. -For python-based LazyConfig, use "path.key=value". - """.strip(), - default=None, - nargs=argparse.REMAINDER, - ) - parser.add_argument( - "--output-dir", - default="./local_dino", - type=str, - help="Path to save logs and checkpoints.", - ) - parser.add_argument("--seed", default=0, type=int, help="RNG seed") - parser.add_argument( - "--benchmark-codebase", - action="store_true", - help="test the codebase for a few iters", - ) - parser.add_argument("--test-ibot", action="store_true", help="test ibot") - parser.add_argument("--profiling", action="store_true", help="do profiling") - parser.add_argument("--dump-fsdp-weights", action="store_true", help="dump fsdp weights") - parser.add_argument("--record_ref_losses", action="store_true", help="record reference losses") - parser.add_argument("--ref_losses_path", default="", type=str) - parser.add_argument("--multi-distillation", action="store_true", help="run multi-distillation") - - return parser - - -def build_optimizer(cfg, params_groups): - return torch.optim.AdamW(params_groups, betas=(cfg.optim.adamw_beta1, cfg.optim.adamw_beta2)) - - -def build_schedulers(cfg): - if "schedules" in cfg: - logger.info("Using schedules v2") - return build_schedulers_v2(cfg) - - OFFICIAL_EPOCH_LENGTH = cfg.train.OFFICIAL_EPOCH_LENGTH - lr = dict( - base_value=cfg.optim["lr"], - final_value=cfg.optim["min_lr"], - total_iters=cfg.optim["epochs"] * OFFICIAL_EPOCH_LENGTH, - warmup_iters=cfg.optim["warmup_epochs"] * OFFICIAL_EPOCH_LENGTH, - start_warmup_value=0, - trunc_extra=cfg.optim["schedule_trunc_extra"], - ) - wd = dict( - base_value=cfg.optim["weight_decay"], - final_value=cfg.optim["weight_decay_end"], - total_iters=cfg.optim["epochs"] * OFFICIAL_EPOCH_LENGTH, - trunc_extra=cfg.optim["schedule_trunc_extra"], - ) - momentum = dict( - base_value=cfg.teacher["momentum_teacher"], - final_value=cfg.teacher["final_momentum_teacher"], - total_iters=cfg.optim["epochs"] * OFFICIAL_EPOCH_LENGTH, - trunc_extra=cfg.optim["schedule_trunc_extra"], - ) - teacher_temp = dict( - base_value=cfg.teacher["teacher_temp"], - final_value=cfg.teacher["teacher_temp"], - total_iters=cfg.teacher["warmup_teacher_temp_epochs"] * OFFICIAL_EPOCH_LENGTH, - warmup_iters=cfg.teacher["warmup_teacher_temp_epochs"] * OFFICIAL_EPOCH_LENGTH, - start_warmup_value=cfg.teacher["warmup_teacher_temp"], - ) - - lr_schedule = CosineScheduler(**lr) - wd_schedule = CosineScheduler(**wd) - momentum_schedule = CosineScheduler(**momentum) - teacher_temp_schedule = CosineScheduler(**teacher_temp) - last_layer_lr_schedule = CosineScheduler(**lr) - - last_layer_lr_schedule.schedule[: cfg.optim["freeze_last_layer_epochs"] * OFFICIAL_EPOCH_LENGTH] = ( - 0 # mimicking the original schedules - ) - logger.info("Schedulers ready.") - return ( - lr_schedule, - wd_schedule, - momentum_schedule, - teacher_temp_schedule, - last_layer_lr_schedule, - ) - - -def build_schedulers_v2(cfg): - iter_per_epoch = cfg.train.OFFICIAL_EPOCH_LENGTH - total_iterations = cfg.train.OFFICIAL_EPOCH_LENGTH * cfg.optim.epochs - logger.info(f"Total training iterations {total_iterations}") - - # LR scaling rules - lr_peak = cfg.schedules.lr.peak - lr_end = cfg.schedules.lr.end - if cfg.optim.scaling_rule == "linear_wrt_256": - lr_peak *= cfg.train.batch_size_per_gpu * distributed.get_world_size() / 256.0 - lr_end *= cfg.train.batch_size_per_gpu * distributed.get_world_size() / 256.0 - logger.info( - f"Scaling rule {cfg.optim.scaling_rule}, LR peak {cfg.schedules.lr.peak} -> {lr_peak}, LR end {cfg.schedules.lr.end} -> {lr_end}" - ) - elif cfg.optim.scaling_rule == "sqrt_wrt_1024": - lr_peak *= 4 * math.sqrt(cfg.train.batch_size_per_gpu * distributed.get_world_size() / 1024.0) - lr_end *= 4 * math.sqrt(cfg.train.batch_size_per_gpu * distributed.get_world_size() / 1024.0) - logger.info( - f"Scaling rule {cfg.optim.scaling_rule}, LR peak {cfg.schedules.lr.peak} -> {lr_peak}, LR end {cfg.schedules.lr.end} -> {lr_end}" - ) - else: - logger.info(f"No scaling rule for {cfg.optim.scaling_rule=}") - - lr = linear_warmup_cosine_decay( - start=cfg.schedules.lr.start, - peak=lr_peak, - end=lr_end, - warmup_iterations=iter_per_epoch * cfg.schedules.lr.warmup_epochs, - total_iterations=total_iterations, - cosine_iterations=( - iter_per_epoch * cfg.schedules.lr.cosine_epochs if "cosine_epochs" in cfg.schedules.lr else None - ), - ) - last_layer_lr = lr.copy() - last_layer_lr[: iter_per_epoch * cfg.schedules.lr.freeze_last_layer_epochs] = 0 - weight_decay = linear_warmup_cosine_decay( - start=cfg.schedules.weight_decay.start, - peak=cfg.schedules.weight_decay.peak, - end=cfg.schedules.weight_decay.end, - warmup_iterations=iter_per_epoch * cfg.schedules.weight_decay.warmup_epochs, - total_iterations=total_iterations, - cosine_iterations=( - iter_per_epoch * cfg.schedules.weight_decay.cosine_epochs - if "cosine_epochs" in cfg.schedules.weight_decay - else None - ), - ) - momentum = linear_warmup_cosine_decay( - start=cfg.schedules.momentum.start, - peak=cfg.schedules.momentum.peak, - end=cfg.schedules.momentum.end, - warmup_iterations=iter_per_epoch * cfg.schedules.momentum.warmup_epochs, - total_iterations=total_iterations, - cosine_iterations=( - iter_per_epoch * cfg.schedules.momentum.cosine_epochs if "cosine_epochs" in cfg.schedules.momentum else None - ), - ) - teacher_temp = linear_warmup_cosine_decay( - start=cfg.schedules.teacher_temp.start, - peak=cfg.schedules.teacher_temp.peak, - end=cfg.schedules.teacher_temp.end, - warmup_iterations=iter_per_epoch * cfg.schedules.teacher_temp.warmup_epochs, - total_iterations=total_iterations, - cosine_iterations=( - iter_per_epoch * cfg.schedules.teacher_temp.cosine_epochs - if "cosine_epochs" in cfg.schedules.teacher_temp - else None - ), - ) - return lr, weight_decay, momentum, teacher_temp, last_layer_lr - - -def apply_optim_scheduler(optimizer, lr, wd, last_layer_lr): - for param_group in optimizer.param_groups: - is_last_layer = param_group["is_last_layer"] - lr_multiplier = param_group["lr_multiplier"] - wd_multiplier = param_group["wd_multiplier"] - param_group["weight_decay"] = wd * wd_multiplier - if is_last_layer: - param_group["lr"] = last_layer_lr * lr_multiplier - else: - param_group["lr"] = lr * lr_multiplier - - -def do_test(cfg, model, iteration, process_group, do_low_freq=False): - # dump a sharded checkpoint - eval_dir = Path(cfg.train.output_dir) / "eval" / str(iteration) - if distributed.is_subgroup_main_process(): - eval_dir.mkdir(parents=True, exist_ok=True) - if cfg.train.sharded_eval_checkpoint: - ckpt_path = eval_dir / "sharded_teacher_checkpoint" - if distributed.is_subgroup_main_process(): - ckpt_path.mkdir(parents=True, exist_ok=True) - torch.distributed.barrier() - teacher_backbone = model.model_ema - save_checkpoint( - ckpt_dir=ckpt_path, iteration=iteration, model=teacher_backbone, overwrite=True, process_group=process_group - ) - if not distributed.is_subgroup_main_process(): - return - else: - new_state_dict = model.model_ema.state_dict() - for k, tensor in list(new_state_dict.items()): - if isinstance(tensor, DTensor): - new_state_dict[k] = tensor.full_tensor() - if not distributed.is_subgroup_main_process(): - return - # save teacher checkpoint - ckpt_path = eval_dir / "teacher_checkpoint.pth" - torch.save({"teacher": new_state_dict}, ckpt_path) - logger.info("Saved eval checkpoint: %s", ckpt_path) - - -def build_data_loader_from_cfg( - cfg, - model, - start_iter, -): - # Collate function - img_size = cfg.crops.global_crops_size - patch_size = cfg.student.patch_size - n_tokens = (img_size // patch_size) ** 2 - mask_generator = MaskingGenerator( - input_size=(img_size // patch_size, img_size // patch_size), - max_num_patches=0.5 * img_size // patch_size * img_size // patch_size, - ) - - if cfg.multidistillation.enabled: - assert cfg.multidistillation.global_batch_size % distributed.get_subgroup_size() == 0 - local_batch_size = cfg.multidistillation.global_batch_size // distributed.get_subgroup_size() - dataloader_batch_size_per_gpu = ( - cfg.multidistillation.global_batch_size + (distributed.get_world_size() - 1) - ) // distributed.get_world_size() - else: - local_batch_size = None # will default to the standard local batch size matching the data batch size - dataloader_batch_size_per_gpu = cfg.train.batch_size_per_gpu - - collate_fn = partial( - collate_data_and_cast, - mask_ratio_tuple=cfg.ibot.mask_ratio_min_max, - mask_probability=cfg.ibot.mask_sample_probability, - dtype={ - "fp32": torch.float32, - "fp16": torch.float16, - "bf16": torch.bfloat16, - }[cfg.compute_precision.param_dtype], - n_tokens=n_tokens, - mask_generator=mask_generator, - random_circular_shift=cfg.ibot.mask_random_circular_shift, - local_batch_size=local_batch_size, - ) - batch_size = dataloader_batch_size_per_gpu - num_workers = cfg.train.num_workers - dataset_path = cfg.train.dataset_path - dataset = make_dataset( - dataset_str=dataset_path, - transform=model.build_data_augmentation_dino(cfg), - target_transform=lambda _: (), - ) - - if isinstance(dataset, torch.utils.data.IterableDataset): - sampler_type = SamplerType.INFINITE - else: - sampler_type = SamplerType.SHARDED_INFINITE if cfg.train.cache_dataset else SamplerType.INFINITE - - data_loader = make_data_loader( - dataset=dataset, - batch_size=batch_size, - num_workers=num_workers, - shuffle=True, - seed=cfg.train.seed + start_iter + 1, - sampler_type=sampler_type, - sampler_advance=start_iter * dataloader_batch_size_per_gpu, - drop_last=True, - collate_fn=collate_fn, - ) - return data_loader - - -def build_multi_resolution_data_loader_from_cfg( - cfg, - model, - start_iter, - seed=65537, -): - global_crops_sizes = ( - [cfg.crops.global_crops_size] if isinstance(cfg.crops.global_crops_size, int) else cfg.crops.global_crops_size - ) - local_crops_sizes = ( - [cfg.crops.local_crops_size] if isinstance(cfg.crops.local_crops_size, int) else cfg.crops.local_crops_size - ) - gram_teacher_crops_sizes = ( - [cfg.crops.gram_teacher_crops_size] - if cfg.crops.gram_teacher_crops_size is None or isinstance(cfg.crops.gram_teacher_crops_size, int) - else cfg.crops.gram_teacher_crops_size - ) - loader_ratios = ( - [cfg.crops.global_local_crop_pairs_ratios] - if type(cfg.crops.global_local_crop_pairs_ratios) in [int, float] - else cfg.crops.global_local_crop_pairs_ratios - ) - assert len(global_crops_sizes) == len(local_crops_sizes) == len(gram_teacher_crops_sizes) == len(loader_ratios) - - loaders = [] - for increment, (global_crops_size_i, local_crops_size_i, gram_teacher_crops_size_i) in enumerate( - zip(global_crops_sizes, local_crops_sizes, gram_teacher_crops_sizes) - ): - cfg_i = copy.deepcopy(cfg) - cfg_i.crops.global_crops_size = global_crops_size_i - cfg_i.crops.local_crops_size = local_crops_size_i - cfg_i.crops.gram_teacher_crops_size = gram_teacher_crops_size_i - cfg_i.train.seed = cfg.train.seed + increment + 1 - loaders.append(build_data_loader_from_cfg(cfg=cfg_i, model=model, start_iter=start_iter)) - - if len(loaders) == 1: - data_loader = loaders[0] - else: - data_loader = CombinedDataLoader( - loaders_with_ratios=zip(loaders, loader_ratios), - batch_size=cfg.train.batch_size_per_gpu, - combining_mode=0, - seed=seed, - name="MultiResDL", - ) - return data_loader - - -def do_train(cfg, model, resume=False): - process_subgroup = distributed.get_process_subgroup() - ckpt_dir = Path(cfg.train.output_dir, "ckpt").expanduser() - ckpt_dir.mkdir(parents=True, exist_ok=True) - - model.train() - # Optimizer - optimizer = build_optimizer(cfg, model.get_params_groups()) - ( - lr_schedule, - wd_schedule, - momentum_schedule, - teacher_temp_schedule, - last_layer_lr_schedule, - ) = build_schedulers(cfg) - if cfg.multidistillation.enabled: - register_dont_save_hooks( - model, - dont_save=[k for k, _ in model.state_dict().items() if k.startswith("teacher")], - ) - model.init_weights() - start_iter = 0 - if resume and (last_checkpoint_dir := find_latest_checkpoint(ckpt_dir)): - logger.info(f"Checkpoint found {last_checkpoint_dir}") - start_iter = ( - load_checkpoint( - last_checkpoint_dir, - model=model, - optimizer=optimizer, - strict_loading=False, - process_group=process_subgroup, - ) - + 1 - ) - OFFICIAL_EPOCH_LENGTH = cfg.train.OFFICIAL_EPOCH_LENGTH - max_iter = cfg.optim.epochs * OFFICIAL_EPOCH_LENGTH - if cfg.multidistillation.enabled: - global_batch_size = cfg.multidistillation.global_batch_size - else: - global_batch_size = cfg.train.batch_size_per_gpu * distributed.get_world_size() - - # Build data loader - data_loader = build_multi_resolution_data_loader_from_cfg( - cfg=cfg, - model=model, - start_iter=start_iter, - ) - - # Metric logging - logger.info("Starting training from iteration %d", start_iter) - metrics_file = os.path.join(cfg.train.output_dir, "training_metrics.json") - metric_logger = MetricLogger(delimiter=" ", output_file=metrics_file) - # Manual garbage collection - gc.disable() - gc.collect() - - # Training loop - student = model.student - iteration = start_iter - num_gram_updates = 0 - if ( - cfg.gram.use_loss - and model.has_gram_teacher - and cfg.gram.rep_update - and start_iter > 0 - and start_iter >= cfg.gram.it_first_update - ): - # If `start_iter == it_first_update`, we have performed one gram teacher update after - # iteration `start_iter - 1`, except if we are starting training from scratch and `start_iter == 0`. - num_gram_updates = math.ceil((start_iter + 1 - cfg.gram.it_first_update) / cfg.gram.update_frequency) - logger.info(f"Gram was updated {num_gram_updates} times before iteration {start_iter}") - consecutive_nan_count = 0 - for data in metric_logger.log_every( - data_loader, - print_freq=10, - header="Training", - n_iterations=max_iter, - start_iteration=start_iter, - ): - it = iteration - data["global_batch_size"] = global_batch_size - if iteration > max_iter: - return - - # Garbage collection (trigger manually so it happens on all ranks at the same time) - if (iteration + 1) % 150 == 0: - logger.info("Garbage collection") - gc.collect() - - if cfg.gram.use_loss and model.gram_it_load_ema_teacher == it: - logger.info(f"Loading EMA teacher into Gram teacher before iteration {it}") - model.gram_load_ema_teacher() - - # Learning rates and other schedules - lr = lr_schedule[it] - wd = wd_schedule[it] - mom = momentum_schedule[it] - teacher_temp = teacher_temp_schedule[it] - last_layer_lr = last_layer_lr_schedule[it] - apply_optim_scheduler(optimizer, lr, wd, last_layer_lr) - - # Forward backward - optimizer.zero_grad(set_to_none=True) - total_loss, metrics_dict = model.forward_backward(data, teacher_temp=teacher_temp, iteration=it) - - # Gradient clipping - if cfg.optim.clip_grad: - for k, v in student.items(): - grad_norm = torch.nn.utils.clip_grad_norm_( - v.parameters(), - max_norm=cfg.optim.clip_grad, - ) - metrics_dict[f"{k}_grad_norm"] = ( - grad_norm.full_tensor().item() - if isinstance(grad_norm, torch.distributed.tensor.DTensor) - else grad_norm.item() - ) - - # Reduce total_loss to check for NaNs, reduce metrics for logging - total_loss_all_ranks = total_loss.new_empty(distributed.get_subgroup_size()) - torch.distributed.all_gather_into_tensor( - total_loss_all_ranks, - total_loss.detach(), - group=distributed.get_process_subgroup(), - ) - total_loss = total_loss_all_ranks.mean() - metrics_values = torch.stack( - [torch.as_tensor(v, dtype=torch.float32, device=total_loss.device).detach() for v in metrics_dict.values()] - ) - torch.distributed.all_reduce( - metrics_values, - op=torch.distributed.ReduceOp.AVG, - group=distributed.get_process_subgroup(), - ) - metrics_dict = dict(zip(metrics_dict.keys(), metrics_values)) - if total_loss_all_ranks.isnan().any(): - consecutive_nan_count += 1 - which_ranks = total_loss_all_ranks.isnan().nonzero().flatten().tolist() - logger.warning("NaN loss detected on ranks: %s", which_ranks) - logger.warning("Consecutive NaNs: %d", consecutive_nan_count) - metrics_dict_str = "\n".join([f"{k}: {v}" for k, v in metrics_dict.items()]) - logger.warning("All-reduced metrics:\n%s", metrics_dict_str) - if consecutive_nan_count > 2 and not cfg.multidistillation.enabled: - msg = "Too many consecutive nans detected in loss, aborting..." - logger.error(msg) - raise RuntimeError(msg) - else: - consecutive_nan_count = 0 - # Step optimizer - optimizer.step() - model.update_ema(mom) - - # [GRAM] Update gram teacher when using gram teacher and frequent updates - if ( - cfg.gram.use_loss - and model.gram_rep_update - and (it + 1) >= model.gram_it_first_update - and (it + 1) % model.gram_update_frequency == 0 - and (cfg.gram.max_updates is None or num_gram_updates < cfg.gram.max_updates) - ): - logger.info(f"Updating Gram teacher from EMA teacher after iteration {it}") - model.update_gram() - num_gram_updates += 1 - - # Log metrics - metric_logger.update(lr=lr) - metric_logger.update(wd=wd) - metric_logger.update(mom=mom) - metric_logger.update(last_layer_lr=last_layer_lr) - metric_logger.update(total_loss=total_loss, **metrics_dict) - - # Submit evaluation jobs - if ( - cfg.evaluation.eval_period_iterations > 0 and (iteration + 1) % cfg.evaluation.eval_period_iterations == 0 - # and iteration != max_iter - 1 - ): - do_test(cfg, model, f"training_{iteration}", process_group=process_subgroup) - torch.cuda.synchronize() - - # Checkpointing - if (iteration + 1) % cfg.checkpointing.period == 0: - torch.cuda.synchronize() - save_checkpoint( - ckpt_dir / str(iteration), - iteration=iteration, - model=model, - optimizer=optimizer, - overwrite=True, - process_group=process_subgroup, - ) - if distributed.is_subgroup_main_process(): - keep_last_n_checkpoints(ckpt_dir, cfg.checkpointing.max_to_keep) - if "keep_every" in cfg.checkpointing and (iteration + 1) % cfg.checkpointing.keep_every == 0: - keep_checkpoint_copy(ckpt_dir / str(iteration)) - - iteration = iteration + 1 - metric_logger.synchronize_between_processes() - - return {k: meter.global_avg for k, meter in metric_logger.meters.items()} - - -def main(argv=None): - if argv is None: - args = get_args_parser().parse_args() - else: - args = get_args_parser().parse_args(argv[1:]) - args.output_dir = sys.argv[1] - if args.multi_distillation: - print("performing multidistillation run") - cfg = setup_multidistillation(args) - torch.distributed.barrier() - logger.info("setup_multidistillation done") - assert cfg.MODEL.META_ARCHITECTURE == "MultiDistillationMetaArch" - else: - setup_job(output_dir=args.output_dir, seed=args.seed) - cfg = setup_config(args, strict_cfg=False) - logger.info(cfg) - setup_logging( - output=os.path.join(os.path.abspath(args.output_dir), "nan_logs"), - name="nan_logger", - ) - meta_arch = { - "SSLMetaArch": SSLMetaArch, - "MultiDistillationMetaArch": MultiDistillationMetaArch, - }.get(cfg.MODEL.META_ARCHITECTURE, None) - if meta_arch is None: - raise ValueError(f"Unknown MODEL.META_ARCHITECTURE {cfg.MODEL.META_ARCHITECTURE}") - logger.info(f"Making meta arch {meta_arch.__name__}") - with torch.device("meta"): - model = meta_arch(cfg) - model.prepare_for_distributed_training() - # Fill all values with `nans` so that we identify - # non-initialized values - model._apply( - lambda t: torch.full_like( - t, - fill_value=math.nan if t.dtype.is_floating_point else (2 ** (t.dtype.itemsize * 8 - 1)), - device="cuda", - ), - recurse=True, - ) - logger.info(f"Model after distributed:\n{model}") - if args.eval_only: - model.init_weights() - iteration = ( - model.get_checkpointer_class()(model, save_dir=cfg.train.output_dir) - .resume_or_load(cfg.MODEL.WEIGHTS, resume=not args.no_resume) - .get("iteration", -1) - + 1 - ) - return do_test(cfg, model, f"manual_{iteration}") - do_train(cfg, model, resume=not args.no_resume) - - -if __name__ == "__main__": - main() diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/utils/__init__.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/utils/__init__.py deleted file mode 100644 index 2794ac284e9bba24c6cee6a3eb5ecf7722f8734c..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/utils/__init__.py +++ /dev/null @@ -1,16 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -from .dtype import as_torch_dtype -from .utils import ( - cat_keep_shapes, - count_parameters, - fix_random_seeds, - get_conda_env, - get_sha, - named_apply, - named_replace, - uncat_with_shapes, -) diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/utils/__pycache__/__init__.cpython-310.pyc b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/utils/__pycache__/__init__.cpython-310.pyc deleted file mode 100644 index d6d3d9e9f2863c1d1022f88df17d8f5c763fc616..0000000000000000000000000000000000000000 Binary files a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/utils/__pycache__/__init__.cpython-310.pyc and /dev/null differ diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/utils/__pycache__/dtype.cpython-310.pyc b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/utils/__pycache__/dtype.cpython-310.pyc deleted file mode 100644 index 0e2cb502438ecde55c506acdc24158bc264b1c30..0000000000000000000000000000000000000000 Binary files a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/utils/__pycache__/dtype.cpython-310.pyc and /dev/null differ diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/utils/__pycache__/utils.cpython-310.pyc b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/utils/__pycache__/utils.cpython-310.pyc deleted file mode 100644 index 0fe9b87f39c06b068129eaaa1a694e7a1a09edd0..0000000000000000000000000000000000000000 Binary files a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/utils/__pycache__/utils.cpython-310.pyc and /dev/null differ diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/utils/cluster.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/utils/cluster.py deleted file mode 100644 index 25df3bc556241b1efc22908c7c832d5f3751682f..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/utils/cluster.py +++ /dev/null @@ -1,103 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import os -from enum import Enum -from pathlib import Path -from typing import Any, Dict, Optional - - -class ClusterType(Enum): - CW = "cw" - - -def _guess_cluster_type() -> ClusterType: - return ClusterType.CW - - -def get_cluster_type( - cluster_type: Optional[ClusterType] = None, -) -> Optional[ClusterType]: - if cluster_type is None: - return _guess_cluster_type() - - return cluster_type - - -def get_slurm_account(cluster_type: Optional[ClusterType] = None) -> Optional[str]: - cluster_type = get_cluster_type(cluster_type) - if cluster_type is None: - return None - return { - ClusterType.CW: "fair_amaia_cw_explore", - }[cluster_type] - - -def get_checkpoint_path(cluster_type: Optional[ClusterType] = None) -> Optional[Path]: - cluster_type = get_cluster_type(cluster_type) - if cluster_type is None: - return None - - CHECKPOINT_DIRNAMES = { - ClusterType.CW: "", - } - return Path("/") / CHECKPOINT_DIRNAMES[cluster_type] - - -def get_user_checkpoint_path( - cluster_type: Optional[ClusterType] = None, -) -> Optional[Path]: - checkpoint_path = get_checkpoint_path(cluster_type) - if checkpoint_path is None: - return None - - username = os.environ.get("USER") - assert username is not None - return checkpoint_path / username - - -def get_slurm_qos(cluster_type: Optional[ClusterType] = None) -> Optional[str]: - cluster_type = get_cluster_type(cluster_type) - if cluster_type is None: - return None - - return { - ClusterType.CW: "explore", - }.get(cluster_type) - - -def get_slurm_partition(cluster_type: Optional[ClusterType] = None) -> Optional[str]: - cluster_type = get_cluster_type(cluster_type) - if cluster_type is None: - return None - - SLURM_PARTITIONS = { - ClusterType.CW: "learn", - } - return SLURM_PARTITIONS[cluster_type] - - -def get_slurm_executor_parameters( - nodes: int, - num_gpus_per_node: int, - cluster_type: Optional[ClusterType] = None, - **kwargs, -) -> Dict[str, Any]: - # create default parameters - params = { - "mem_gb": 0, # Requests all memory on a node, see https://slurm.schedmd.com/sbatch.html - "gpus_per_node": num_gpus_per_node, - "tasks_per_node": num_gpus_per_node, # one task per GPU - "cpus_per_task": 10, - "nodes": nodes, - "slurm_partition": get_slurm_partition(cluster_type), - } - # apply cluster-specific adjustments - cluster_type = get_cluster_type(cluster_type) - if cluster_type == ClusterType.CW: - params["cpus_per_task"] = 16 - # set additional parameters / apply overrides - params.update(kwargs) - return params diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/utils/custom_callable.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/utils/custom_callable.py deleted file mode 100644 index cb7c2f762835a6027f94006fad3360cf19ca4be3..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/utils/custom_callable.py +++ /dev/null @@ -1,47 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import contextlib -import importlib -import inspect -import os -import sys -from pathlib import Path - - -@contextlib.contextmanager -def _load_modules_from_dir(dir_: str): - sys.path.insert(0, dir_) - yield - sys.path.pop(0) - - -def load_custom_callable(module_path: str | Path, callable_name: str): - module_full_path = os.path.realpath(module_path) - assert os.path.exists(module_full_path), f"module {module_full_path} does not exist" - module_dir, module_filename = os.path.split(module_full_path) - module_name, _ = os.path.splitext(module_filename) - - with _load_modules_from_dir(module_dir): - module = importlib.import_module(module_name) - if inspect.getfile(module) != module_full_path: - importlib.reload(module) - callable_ = getattr(module, callable_name) - - return callable_ - - -@contextlib.contextmanager -def change_working_dir_and_pythonpath(new_dir): - old_dir = Path.cwd() - new_dir = Path(new_dir).expanduser().resolve().as_posix() - old_pythonpath = sys.path.copy() - sys.path.insert(0, new_dir) - os.chdir(new_dir) - try: - yield - finally: - os.chdir(old_dir) - sys.path = old_pythonpath diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/utils/dtype.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/utils/dtype.py deleted file mode 100644 index b2795ed42b512ce51890c8592db5c364b18a5f4c..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/utils/dtype.py +++ /dev/null @@ -1,35 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -from typing import Dict, Union - -import numpy as np -import torch - -TypeSpec = Union[str, np.dtype, torch.dtype] - - -_NUMPY_TO_TORCH_DTYPE: Dict[np.dtype, torch.dtype] = { - np.dtype("bool"): torch.bool, - np.dtype("uint8"): torch.uint8, - np.dtype("int8"): torch.int8, - np.dtype("int16"): torch.int16, - np.dtype("int32"): torch.int32, - np.dtype("int64"): torch.int64, - np.dtype("float16"): torch.float16, - np.dtype("float32"): torch.float32, - np.dtype("float64"): torch.float64, - np.dtype("complex64"): torch.complex64, - np.dtype("complex128"): torch.complex128, -} - - -def as_torch_dtype(dtype: TypeSpec) -> torch.dtype: - if isinstance(dtype, torch.dtype): - return dtype - if isinstance(dtype, str): - dtype = np.dtype(dtype) - assert isinstance(dtype, np.dtype), f"Expected an instance of nunpy dtype, got {type(dtype)}" - return _NUMPY_TO_TORCH_DTYPE[dtype] diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/utils/utils.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/utils/utils.py deleted file mode 100644 index 7b267033f49dfa0819406f48152f490b7c17ac94..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/dinov3/utils/utils.py +++ /dev/null @@ -1,130 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -import logging -import os -import random -import subprocess -from typing import Callable, List, Optional, Tuple - -import numpy as np -import torch -from torch import Tensor, nn - -logger = logging.getLogger("dinov3") - - -def cat_keep_shapes(x_list: List[Tensor]) -> Tuple[Tensor, List[Tuple[int]], List[int]]: - shapes = [x.shape for x in x_list] - num_tokens = [x.select(dim=-1, index=0).numel() for x in x_list] - flattened = torch.cat([x.flatten(0, -2) for x in x_list]) - return flattened, shapes, num_tokens - - -def uncat_with_shapes(flattened: Tensor, shapes: List[Tuple[int]], num_tokens: List[int]) -> List[Tensor]: - outputs_splitted = torch.split_with_sizes(flattened, num_tokens, dim=0) - shapes_adjusted = [shape[:-1] + torch.Size([flattened.shape[-1]]) for shape in shapes] - outputs_reshaped = [o.reshape(shape) for o, shape in zip(outputs_splitted, shapes_adjusted)] - return outputs_reshaped - - -def named_replace( - fn: Callable, - module: nn.Module, - name: str = "", - depth_first: bool = True, - include_root: bool = False, -) -> nn.Module: - if not depth_first and include_root: - module = fn(module=module, name=name) - for child_name_o, child_module in list(module.named_children()): - child_name = ".".join((name, child_name_o)) if name else child_name_o - new_child = named_replace( - fn=fn, - module=child_module, - name=child_name, - depth_first=depth_first, - include_root=True, - ) - setattr(module, child_name_o, new_child) - - if depth_first and include_root: - module = fn(module=module, name=name) - return module - - -def named_apply( - fn: Callable, - module: nn.Module, - name: str = "", - depth_first: bool = True, - include_root: bool = False, -) -> nn.Module: - if not depth_first and include_root: - fn(module=module, name=name) - for child_name, child_module in module.named_children(): - child_name = ".".join((name, child_name)) if name else child_name - named_apply( - fn=fn, - module=child_module, - name=child_name, - depth_first=depth_first, - include_root=True, - ) - if depth_first and include_root: - fn(module=module, name=name) - return module - - -def fix_random_seeds(seed: int = 31): - """ - Fix random seeds. - """ - torch.manual_seed(seed) - torch.cuda.manual_seed_all(seed) - np.random.seed(seed) - random.seed(seed) - - -def get_sha() -> str: - cwd = os.path.dirname(os.path.abspath(__file__)) - - def _run(command): - return subprocess.check_output(command, cwd=cwd).decode("ascii").strip() - - sha = "N/A" - diff = "clean" - branch = "N/A" - try: - sha = _run(["git", "rev-parse", "HEAD"]) - subprocess.check_output(["git", "diff"], cwd=cwd) - diff = _run(["git", "diff-index", "HEAD"]) - diff = "has uncommited changes" if diff else "clean" - branch = _run(["git", "rev-parse", "--abbrev-ref", "HEAD"]) - except Exception: - pass - message = f"sha: {sha}, status: {diff}, branch: {branch}" - return message - - -def get_conda_env() -> Tuple[Optional[str], Optional[str]]: - conda_env_name = os.environ.get("CONDA_DEFAULT_ENV") - conda_env_path = os.environ.get("CONDA_PREFIX") - return conda_env_name, conda_env_path - - -def count_parameters(module: nn.Module) -> int: - c = 0 - for m in module.parameters(): - c += m.nelement() - return c - - -def has_batchnorms(model: nn.Module) -> bool: - bn_types = (nn.BatchNorm1d, nn.BatchNorm2d, nn.BatchNorm3d, nn.SyncBatchNorm) - for _, module in model.named_modules(): - if isinstance(module, bn_types): - return True - return False diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/hubconf.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/hubconf.py deleted file mode 100644 index ac08688eb0aada70c9bd1284cff1079f5f0f57e5..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/hubconf.py +++ /dev/null @@ -1,26 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -from dinov3.hub.backbones import ( - dinov3_convnext_base, - dinov3_convnext_large, - dinov3_convnext_small, - dinov3_convnext_tiny, - dinov3_vit7b16, - dinov3_vitb16, - dinov3_vith16plus, - dinov3_vitl16, - dinov3_vitl16plus, - dinov3_vits16, - dinov3_vits16plus, -) -from dinov3.hub.classifiers import dinov3_vit7b16_lc -from dinov3.hub.detectors import dinov3_vit7b16_de -from dinov3.hub.dinotxt import dinov3_vitl16_dinotxt_tet1280d20h24l -from dinov3.hub.segmentors import dinov3_vit7b16_ms - -from dinov3.hub.depthers import dinov3_vit7b16_dd - -dependencies = ["torch", "numpy"] diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/notebooks/dense_sparse_matching.ipynb b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/notebooks/dense_sparse_matching.ipynb deleted file mode 100644 index 45defe76764a5c7c03c94eecc5bdf581779e7264..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/notebooks/dense_sparse_matching.ipynb +++ /dev/null @@ -1,612 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "e7bdfb61-0e65-48c9-ab90-74cb19436615", - "metadata": {}, - "source": [ - "# Dense And Sparse Correspondence\n", - "In this tutorial we show how DINOv3 features can be used to establish correspondences between two objects." - ] - }, - { - "cell_type": "markdown", - "id": "22ee78ba-5796-4cf2-8dd8-0babec77c297", - "metadata": {}, - "source": [ - "### Setup\n", - "\n", - "Let's start by loading some pre-requisites and checking the DINOv3 repository location:\n", - "- `local` if `DINOV3_LOCATION` environment variable was set to work with a local version of DINOv3 repository;\n", - "- `github` if the code should be loaded via torch hub." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "ccfbed2e-c938-4ae1-9b11-e3c12a4086dc", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "DINOv3 location set to /private/home/vkhalidov/fairvit_dinov3_release/app/dinov3_release/\n" - ] - } - ], - "source": [ - "import pickle\n", - "import os\n", - "import urllib\n", - "\n", - "import numpy as np\n", - "from matplotlib.patches import ConnectionPatch\n", - "import matplotlib.pyplot as plt\n", - "from PIL import Image\n", - "from sklearn.decomposition import PCA\n", - "\n", - "import torch\n", - "import torch.nn.functional as F\n", - "import torchvision.transforms.functional as TF\n", - "from tqdm import tqdm\n", - "\n", - "DINOV3_GITHUB_LOCATION = \"facebookresearch/dinov3\"\n", - "\n", - "if os.getenv(\"DINOV3_LOCATION\") is not None:\n", - " DINOV3_LOCATION = os.getenv(\"DINOV3_LOCATION\")\n", - "else:\n", - " DINOV3_LOCATION = DINOV3_GITHUB_LOCATION\n", - "\n", - "print(f\"DINOv3 location set to {DINOV3_LOCATION}\")" - ] - }, - { - "cell_type": "markdown", - "id": "7ff9859a-71d6-4808-8187-eea8e625e4d5", - "metadata": {}, - "source": [ - "### Model Loading\n", - "We load the DINOv3 ViT-L model. Feel free to try other DINOv3 models as well!" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "2db67419-3446-41c9-968b-c4eaa991e3f6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DinoVisionTransformer(\n", - " (patch_embed): PatchEmbed(\n", - " (proj): Conv2d(3, 1024, kernel_size=(16, 16), stride=(16, 16))\n", - " (norm): Identity()\n", - " )\n", - " (rope_embed): RopePositionEmbedding()\n", - " (blocks): ModuleList(\n", - " (0-23): 24 x SelfAttentionBlock(\n", - " (norm1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n", - " (attn): SelfAttention(\n", - " (qkv): LinearKMaskedBias(in_features=1024, out_features=3072, bias=True)\n", - " (attn_drop): Dropout(p=0.0, inplace=False)\n", - " (proj): Linear(in_features=1024, out_features=1024, bias=True)\n", - " (proj_drop): Dropout(p=0.0, inplace=False)\n", - " )\n", - " (ls1): LayerScale()\n", - " (norm2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n", - " (mlp): Mlp(\n", - " (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n", - " (act): GELU(approximate='none')\n", - " (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n", - " (drop): Dropout(p=0.0, inplace=False)\n", - " )\n", - " (ls2): LayerScale()\n", - " )\n", - " )\n", - " (norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n", - " (head): Identity()\n", - ")" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# examples of available DINOv3 models:\n", - "MODEL_DINOV3_VITS = \"dinov3_vits16\"\n", - "MODEL_DINOV3_VITSP = \"dinov3_vits16plus\"\n", - "MODEL_DINOV3_VITB = \"dinov3_vitb16\"\n", - "MODEL_DINOV3_VITL = \"dinov3_vitl16\"\n", - "MODEL_DINOV3_VITHP = \"dinov3_vith16plus\"\n", - "MODEL_DINOV3_VIT7B = \"dinov3_vit7b16\"\n", - "\n", - "# we take DINOv3 ViT-L\n", - "MODEL_NAME = MODEL_DINOV3_VITL\n", - "\n", - "model = torch.hub.load(\n", - " repo_or_dir=DINOV3_LOCATION,\n", - " model=MODEL_NAME,\n", - " source=\"local\" if DINOV3_LOCATION != DINOV3_GITHUB_LOCATION else \"github\",\n", - ")\n", - "model.cuda()" - ] - }, - { - "cell_type": "markdown", - "id": "9c8170fa-224b-4df4-9863-aca11baae1c1", - "metadata": {}, - "source": [ - "### Data Loading\n", - "Now that we have the model set up, let's load the data. It consists of two image / mask pairs:\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "fc300746-94a7-4248-8c4b-f5bce7a7e55f", - "metadata": {}, - "outputs": [], - "source": [ - "image_left_uri = \"https://dl.fbaipublicfiles.com/dinov3/notebooks/dense_sparse_matching/image_left.jpg\"\n", - "mask_left_uri = \"https://dl.fbaipublicfiles.com/dinov3/notebooks/dense_sparse_matching/image_left_fg.png\"\n", - "image_right_uri = \"https://dl.fbaipublicfiles.com/dinov3/notebooks/dense_sparse_matching/image_right.jpg\"\n", - "mask_right_uri = \"https://dl.fbaipublicfiles.com/dinov3/notebooks/dense_sparse_matching/image_right_fg.png\"\n", - "\n", - "def load_image_from_url(url: str) -> Image:\n", - " with urllib.request.urlopen(url) as f:\n", - " return Image.open(f)\n", - "\n", - "\n", - "image_left = load_image_from_url(image_left_uri)\n", - "mask_left = load_image_from_url(mask_left_uri)\n", - "\n", - "image_right = load_image_from_url(image_right_uri)\n", - "mask_right = load_image_from_url(mask_right_uri)" - ] - }, - { - "cell_type": "markdown", - "id": "79245c4e-5227-4c91-b625-189910d64671", - "metadata": {}, - "source": [ - "Let's visualize the two images and the corresponding masks:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "c55c6bdb-78bf-4771-a495-7aa27d995b86", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(16, 8), dpi=300)\n", - "\n", - "for j, (image, mask) in enumerate([(image_left, mask_left), (image_right, mask_right)]):\n", - " foreground = Image.composite(image, mask, mask)\n", - " mask_bg_np = np.copy(np.array(mask))\n", - " mask_bg_np[:, :, 3] = 255 - mask_bg_np[:, :, 3]\n", - " mask_bg = Image.fromarray(mask_bg_np)\n", - " background = Image.composite(image, mask_bg, mask_bg)\n", - "\n", - " data_to_show = [image, mask, foreground, background]\n", - " data_labels = [\"Image\", \"Mask\", \"Foreground\", \"Background\"]\n", - "\n", - "\n", - " for i in range(len(data_to_show)):\n", - " plt.subplot(2, len(data_to_show), 4 * j + i + 1)\n", - " plt.imshow(data_to_show[i])\n", - " plt.axis('off')\n", - " plt.title(data_labels[i], fontsize=12)\n", - " \n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "33ae54fb-05bc-40d0-af41-6d60feb32a45", - "metadata": {}, - "source": [ - "### Data Transforms\n", - "\n", - "Since our models run with a patch size of 16, we have to quantize the masks to a 16x16 pixels grid. To achieve this, we define:\n", - "- the resize transform to resize an image such that it aligns well with the 16x16 grid;\n", - "- a uniform 16x16 conv layer as a [box blur filter](https://en.wikipedia.org/wiki/Box_blur) with stride equal to the patch size." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "29a5affc-e665-4cdb-93e7-da5e681b4a06", - "metadata": {}, - "outputs": [], - "source": [ - "PATCH_SIZE = 16\n", - "IMAGE_SIZE = 768\n", - "\n", - "# quantization filter for the given patch size\n", - "patch_quant_filter = torch.nn.Conv2d(1, 1, PATCH_SIZE, stride=PATCH_SIZE, bias=False)\n", - "patch_quant_filter.weight.data.fill_(1.0 / (PATCH_SIZE * PATCH_SIZE))\n", - "\n", - "# image resize transform to dimensions divisible by patch size\n", - "def resize_transform(\n", - " mask_image: Image,\n", - " image_size: int = IMAGE_SIZE,\n", - " patch_size: int = PATCH_SIZE,\n", - ") -> torch.Tensor:\n", - " w, h = mask_image.size\n", - " h_patches = int(image_size / patch_size)\n", - " w_patches = int((w * image_size) / (h * patch_size))\n", - " return TF.to_tensor(TF.resize(mask_image, (h_patches * patch_size, w_patches * patch_size)))" - ] - }, - { - "cell_type": "markdown", - "id": "728ebe9b-38ea-4a2d-8ccc-d232a207b14c", - "metadata": {}, - "source": [ - "### Extracting Features\n", - "Now we will extract for each of the two images mask values, as well as the patch features. That involves running the dense feature extraction with our model using the following code snippet :\n", - "\n", - "```\n", - "with torch.inference_mode(): \n", - " feats = model.get_intermediate_layers(img, n=range(n_layers), reshape=True, norm=True)\n", - " patch_features.append(feats[-1].squeeze().detach().cpu())\n", - "```\n", - "Each tensor of patch features thus has shape `[D, H, W]`, where `D` is feature dimensionality, and `H` and `W` are image dimensions in the number of patches." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "56357bef-4c28-492f-b531-e46b8bbb0284", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Processing images: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:01<00:00, 1.41it/s]\n" - ] - } - ], - "source": [ - "IMAGENET_MEAN = (0.485, 0.456, 0.406)\n", - "IMAGENET_STD = (0.229, 0.224, 0.225)\n", - "\n", - "MODEL_TO_NUM_LAYERS = {\n", - " MODEL_DINOV3_VITS: 12,\n", - " MODEL_DINOV3_VITSP: 12,\n", - " MODEL_DINOV3_VITB: 12,\n", - " MODEL_DINOV3_VITL: 24,\n", - " MODEL_DINOV3_VITHP: 32,\n", - " MODEL_DINOV3_VIT7B: 40,\n", - "}\n", - "\n", - "n_layers = MODEL_TO_NUM_LAYERS[MODEL_NAME]\n", - "\n", - "patch_mask_values = []\n", - "patch_features = []\n", - "\n", - "with torch.inference_mode():\n", - " with torch.autocast(device_type='cuda', dtype=torch.float32):\n", - " for image, mask in tqdm([(image_left, mask_left), (image_right, mask_right)], desc=\"Processing images\"):\n", - " # processing mask\n", - " mask = mask.split()[-1]\n", - " mask_resized = resize_transform(mask)\n", - " #mask_quantized = patch_quant_filter(mask_resized).squeeze().view(-1).detach().cpu()\n", - " mask_quantized = patch_quant_filter(mask_resized).squeeze().detach().cpu()\n", - " patch_mask_values.append(mask_quantized)\n", - " # processing image\n", - " image = image.convert('RGB')\n", - " image_resized = resize_transform(image)\n", - " image_resized = TF.normalize(image_resized, mean=IMAGENET_MEAN, std=IMAGENET_STD)\n", - " image_resized = image_resized.unsqueeze(0).cuda()\n", - "\n", - " feats = model.get_intermediate_layers(image_resized, n=range(n_layers), reshape=True, norm=True)\n", - " dim = feats[-1].shape[1]\n", - " #patch_features.append(feats[-1].squeeze().view(dim, -1).permute(1,0).detach().cpu())\n", - " patch_features.append(feats[-1].squeeze().detach().cpu())" - ] - }, - { - "cell_type": "markdown", - "id": "b3623cd8-fb75-4ac2-857e-7aa37cc7674c", - "metadata": {}, - "source": [ - "### Matching Patches\n", - "\n", - "For each patch within the foreground object in the left image, we search for the closest patch in the right image using [cosine similarity](https://en.wikipedia.org/wiki/Cosine_similarity) between patch features. We select correspondences only for foreground objects." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "25abb4a0-f8a2-44f9-aa4d-42ead737a589", - "metadata": {}, - "outputs": [], - "source": [ - "MASK_FG_THRESHOLD = 0.5\n", - "\n", - "patch_features[0] = F.normalize(patch_features[0], p=2, dim=0)\n", - "patch_features[1] = F.normalize(patch_features[1], p=2, dim=0)\n", - "\n", - "heatmaps = torch.einsum(\n", - " \"k f, f h w -> k h w\",\n", - " patch_features[0].view(dim, -1).permute(1, 0),\n", - " patch_features[1],\n", - ")\n", - "\n", - "# compute 2D patch locations in the left image\n", - "n_patches_left = patch_features[0].shape[1] * patch_features[0].shape[2]\n", - "patch_indices_left = torch.arange(n_patches_left)\n", - "locs_2d_left = (\n", - " torch.stack(\n", - " (\n", - " patch_indices_left // patch_features[0].shape[2], # row\n", - " patch_indices_left % patch_features[0].shape[2] # column\n", - " ),\n", - " dim=-1\n", - " ) + 0.5\n", - ") * PATCH_SIZE\n", - "\n", - "# compute the corresponding 2D patch locations in the right image\n", - "patch_indices_right = torch.flatten(heatmaps, start_dim=-2).argmax(dim=-1)\n", - "locs_2d_right = (\n", - " torch.stack(\n", - " (\n", - " patch_indices_right // patch_features[1].shape[2], # row\n", - " patch_indices_right % patch_features[1].shape[2] # column\n", - " ),\n", - " dim=-1\n", - " ) + 0.5\n", - ") * PATCH_SIZE\n", - "\n", - "# foreground patches mask in the left image\n", - "patches_left_fg_selection = (patch_mask_values[0].view(-1) > MASK_FG_THRESHOLD)\n", - "# left image patches mask for patches that map to a foreground patch in the right image\n", - "patches_right_fg_selection = (patch_mask_values[1].view(-1)[patch_indices_right] > MASK_FG_THRESHOLD)\n", - "# select foreground left image patches that map to foreground right image patches\n", - "patches_fg_selection = (patches_left_fg_selection * patches_right_fg_selection)\n", - "\n", - "# select (row, col) coordinates for the matched patches\n", - "locs_2d_left_fg = locs_2d_left[patches_fg_selection, :]\n", - "locs_2d_right_fg = locs_2d_right[patches_fg_selection, :]" - ] - }, - { - "cell_type": "markdown", - "id": "7575711f-54af-4903-9e4b-dbdc2fe829f9", - "metadata": {}, - "source": [ - "### Dense Correspondence\n", - "We now show dense correspondences between patches using the color space estimated through PCA over foreground patches in the left image. Closer colors correspond to more similar patches." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "1ae7f70c-1fce-40fc-bc64-4dac93c6eccf", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "PCA(n_components=3, whiten=True)" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pca = PCA(n_components=3, whiten=True)\n", - "fg_patches_left = patch_features[0].view(dim, -1).permute(1, 0)[patches_fg_selection]\n", - "pca.fit(fg_patches_left)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "5e0ea726-4c97-4077-a2d7-b7565cd5f22d", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# get colors for the left image\n", - "h_patches_left = patch_features[0].shape[1]\n", - "w_patches_left = patch_features[0].shape[2]\n", - "x_left = patch_features[0].view(dim, -1).permute(1, 0)\n", - "projected_image_left = torch.from_numpy(\n", - " pca.transform(x_left.numpy())\n", - ").view(h_patches_left, w_patches_left, 3)\n", - "# multiply by 2.0 and pass through a sigmoid to get vibrant colors \n", - "projected_image_left = torch.nn.functional.sigmoid(projected_image_left.mul(2.0)).permute(2, 0, 1)\n", - "\n", - "# get colors for the right image\n", - "h_patches_right = patch_features[1].shape[1]\n", - "w_patches_right = patch_features[1].shape[2]\n", - "x_right = patch_features[1].view(dim, -1).permute(1, 0)\n", - "projected_image_right = torch.from_numpy(\n", - " pca.transform(x_right.numpy())\n", - ").view(h_patches_right, w_patches_right, 3)\n", - "projected_image_right = torch.nn.functional.sigmoid(projected_image_right.mul(2.0)).permute(2, 0, 1)\n", - "\n", - "# apply foreground masks to both visualizations\n", - "projected_image_left *= (patch_mask_values[0] > MASK_FG_THRESHOLD)[None, :, :]\n", - "projected_image_right *= (patch_mask_values[1] > MASK_FG_THRESHOLD)[None, :, :]\n", - "\n", - "plt.figure(figsize=(4, 2), dpi=300)\n", - "plt.subplot(1, 2, 1)\n", - "plt.imshow(projected_image_left.permute(1, 2, 0))\n", - "plt.title(\"Left Image, Dense Correspondences\", fontsize=5)\n", - "plt.axis('off')\n", - "plt.subplot(1, 2, 2)\n", - "plt.imshow(projected_image_right.permute(1, 2, 0))\n", - "plt.title(\"Right Image, Dense Correspondences\", fontsize=5)\n", - "plt.axis('off')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "6e241abb-1c6a-41e9-ad60-9a94dded0f5b", - "metadata": {}, - "source": [ - "### Sparse Correspondence\n", - "\n", - "Finally, we show the established correspondences via the sparse set of matched points." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "05c805cb-5887-45f7-8683-6bde5b2f0f7a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Non-stratified points: (474, 2)\n", - "Stratified points: (55, 2)\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# image scale to go from patches to the original image coordinates\n", - "scale_left = image_left.height / IMAGE_SIZE\n", - "scale_right = image_right.height / IMAGE_SIZE\n", - "\n", - "\n", - "STRATIFY_DISTANCE_THRESHOLD = 100.0\n", - "\n", - "def compute_distances_l2(X, Y, X_squared_norm, Y_squared_norm):\n", - " distances = -2 * X @ Y.T\n", - " distances.add_(X_squared_norm[:, None]).add_(Y_squared_norm[None, :])\n", - " return distances\n", - "\n", - "\n", - "def stratify_points(pts_2d: torch.Tensor, threshold: float = 100.0) -> torch.Tensor:\n", - " # pts_2d: [N, 2]\n", - " n = len(pts_2d)\n", - " max_value = threshold + 1\n", - " pts_2d_sq_norms = torch.linalg.vector_norm(pts_2d, dim=1)\n", - " pts_2d_sq_norms.square_()\n", - " distances = compute_distances_l2(pts_2d, pts_2d, pts_2d_sq_norms, pts_2d_sq_norms)\n", - " distances.fill_diagonal_(max_value)\n", - " distances_mask = torch.empty((n, n), dtype=pts_2d.dtype, device=pts_2d.device)\n", - " torch.le(distances, threshold, out=distances_mask)\n", - " ones_vec = torch.ones(n, device=pts_2d.device, dtype=pts_2d.dtype)\n", - " counts_vec = torch.mv(distances_mask, ones_vec)\n", - " indices_mask = np.ones(n)\n", - " while torch.any(counts_vec).item():\n", - " index_max = torch.argmax(counts_vec).item()\n", - " indices_mask[index_max] = 0\n", - " distances[index_max, :] = max_value\n", - " distances[:, index_max] = max_value\n", - " torch.le(distances, threshold, out=distances_mask)\n", - " torch.mv(distances_mask, ones_vec, out=counts_vec)\n", - " indices_to_exclude = np.nonzero(indices_mask == 0)[0]\n", - " indices_to_keep = np.nonzero(indices_mask > 0)[0]\n", - " return indices_to_exclude, indices_to_keep\n", - "\n", - "print(f\"Non-stratified points: {tuple(locs_2d_left_fg.shape)}\")\n", - "\n", - "indices_to_exclude, indices_to_keep = stratify_points(locs_2d_left_fg * scale_left, STRATIFY_DISTANCE_THRESHOLD**2)\n", - "\n", - "sparse_points_left_yx = locs_2d_left_fg[indices_to_keep, :].cpu().numpy()\n", - "sparse_points_right_yx = locs_2d_right_fg[indices_to_keep, :].cpu().numpy()\n", - "\n", - "print(f\"Stratified points: {sparse_points_left_yx.shape}\")\n", - "\n", - "# show original left and right images\n", - "fig = plt.figure(figsize=(20,10))\n", - "ax1 = fig.add_subplot(121)\n", - "ax1.imshow(image_left)\n", - "ax1.set_axis_off()\n", - "ax2 = fig.add_subplot(122)\n", - "ax2.imshow(image_right)\n", - "ax2.set_axis_off()\n", - "\n", - "\n", - "for i, (row_left, col_left), (row_right, col_right) in zip(\n", - " indices_to_keep, sparse_points_left_yx, sparse_points_right_yx\n", - "):\n", - " row_left_orig, col_left_orig = locs_2d_left_fg[i]\n", - " # use the color used for PCA visualization\n", - " color = projected_image_left[\n", - " :,\n", - " int(row_left_orig / PATCH_SIZE),\n", - " int(col_left_orig / PATCH_SIZE)\n", - " ].cpu().numpy()\n", - " con = ConnectionPatch(\n", - " xyA=(col_left * scale_left, row_left * scale_left),\n", - " xyB=(col_right * scale_right, row_right * scale_right),\n", - " coordsA=\"data\",\n", - " coordsB=\"data\",\n", - " axesA=ax1,\n", - " axesB=ax2,\n", - " color=color,\n", - " )\n", - " ax2.add_artist(con)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/notebooks/dinotxt_inference.ipynb b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/notebooks/dinotxt_inference.ipynb deleted file mode 100644 index fb654e3e8a1d2aa18cdc250cd640065f278fc092..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/notebooks/dinotxt_inference.ipynb +++ /dev/null @@ -1,357 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "id": "231db286", - "metadata": {}, - "outputs": [], - "source": [ - "import sys\n", - "sys.path.append(\"../\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "09db1762", - "metadata": {}, - "outputs": [], - "source": [ - "from dinov3.hub.dinotxt import dinov3_vitl16_dinotxt_tet1280d20h24l\n", - "model, tokenizer = dinov3_vitl16_dinotxt_tet1280d20h24l()" - ] - }, - { - "cell_type": "markdown", - "id": "9e6af07f", - "metadata": {}, - "source": [ - "# Load sample image" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2175f54e", - "metadata": {}, - "outputs": [], - "source": [ - "import urllib\n", - "from PIL import Image\n", - "\n", - "def load_image_from_url(url: str) -> Image:\n", - " with urllib.request.urlopen(url) as f:\n", - " return Image.open(f).convert(\"RGB\")\n", - "\n", - "\n", - "EXAMPLE_IMAGE_URL = \"https://dl.fbaipublicfiles.com/dinov2/images/example.jpg\"\n", - "img_pil = load_image_from_url(EXAMPLE_IMAGE_URL)\n", - "display(img_pil)" - ] - }, - { - "cell_type": "markdown", - "id": "4c7fb8b3", - "metadata": {}, - "source": [ - "# Get Zero-shot classification scores on sample image" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "02560c91", - "metadata": {}, - "outputs": [], - "source": [ - "import torch\n", - "from dinov3.data.transforms import make_classification_eval_transform\n", - "\n", - "image_preprocess = make_classification_eval_transform()\n", - "image_tensor = torch.stack([image_preprocess(img_pil)], dim=0).cuda()\n", - "texts = [\"photo of dogs\", \"photo of a chair\", \"photo of a bowl\", \"photo of a tupperware\"]\n", - "class_names = [\"dog\", \"chair\", \"bowl\", \"tupperware\"]\n", - "tokenized_texts_tensor = tokenizer.tokenize(texts).cuda()\n", - "model = model.cuda()\n", - "with torch.autocast('cuda', dtype=torch.float):\n", - " with torch.no_grad():\n", - " image_features = model.encode_image(image_tensor)\n", - " text_features = model.encode_text(tokenized_texts_tensor)\n", - "image_features /= image_features.norm(dim=-1, keepdim=True)\n", - "text_features /= text_features.norm(dim=-1, keepdim=True)\n", - "similarity = (\n", - " text_features.cpu().float().numpy() @ image_features.cpu().float().numpy().T\n", - ")\n", - "print(similarity) " - ] - }, - { - "cell_type": "markdown", - "id": "1774c99b", - "metadata": {}, - "source": [ - "# Get patch embeddings" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e4debe30", - "metadata": {}, - "outputs": [], - "source": [ - "with torch.autocast('cuda', dtype=torch.float):\n", - " with torch.no_grad():\n", - " image_class_tokens, image_patch_tokens, backbone_patch_tokens = model.encode_image_with_patch_tokens(image_tensor)\n", - " text_features_aligned_to_patch = model.encode_text(tokenized_texts_tensor)[:, 1024:] # Part of text features that is aligned to patch features" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c9929096", - "metadata": {}, - "outputs": [], - "source": [ - "import torch.nn.functional as F\n", - "\n", - "B, P, D = image_patch_tokens.shape\n", - "H = W = int(P**0.5) \n", - "x = image_patch_tokens.movedim(2, 1).unflatten(2, (H, W)).float() # [B, D, H, W]\n", - "x = F.interpolate(x, size=(480, 640), mode=\"bicubic\", align_corners=False)\n", - "x = F.normalize(x, p=2, dim=1)\n", - "y = F.normalize(text_features_aligned_to_patch.float(), p=2, dim=1)\n", - "per_patch_similarity_to_text = torch.einsum(\"bdhw,cd->bchw\", x, y)\n", - "pred_idx = per_patch_similarity_to_text.argmax(1).squeeze(0)" - ] - }, - { - "cell_type": "markdown", - "id": "f5d099b9", - "metadata": {}, - "source": [ - "# Zero-shot classification on ImageNet1k" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f23470a8", - "metadata": {}, - "outputs": [], - "source": [ - "# https://raw.githubusercontent.com/kantharajucn/CLIP-imagenet-evaluation/refs/heads/main/classes.py\n", - "imagenet_clip_class_names= [\"tench\", \"goldfish\", \"great white shark\", \"tiger shark\", \"hammerhead shark\", \"electric ray\", \"stingray\", \"rooster\", \"hen\", \"ostrich\", \"brambling\", \"goldfinch\", \"house finch\", \"junco\", \"indigo bunting\", \"American robin\", \"bulbul\", \"jay\", \"magpie\", \"chickadee\", \"American dipper\", \"kite (bird of prey)\", \"bald eagle\", \"vulture\", \"great grey owl\", \"fire salamander\", \"smooth newt\", \"newt\", \"spotted salamander\", \"axolotl\", \"American bullfrog\", \"tree frog\", \"tailed frog\", \"loggerhead sea turtle\", \"leatherback sea turtle\", \"mud turtle\", \"terrapin\", \"box turtle\", \"banded gecko\", \"green iguana\", \"Carolina anole\", \"desert grassland whiptail lizard\", \"agama\", \"frilled-necked lizard\", \"alligator lizard\", \"Gila monster\", \"European green lizard\", \"chameleon\", \"Komodo dragon\", \"Nile crocodile\", \"American alligator\", \"triceratops\", \"worm snake\", \"ring-necked snake\", \"eastern hog-nosed snake\", \"smooth green snake\", \"kingsnake\", \"garter snake\", \"water snake\", \"vine snake\", \"night snake\", \"boa constrictor\", \"African rock python\", \"Indian cobra\", \"green mamba\", \"sea snake\", \"Saharan horned viper\", \"eastern diamondback rattlesnake\", \"sidewinder rattlesnake\", \"trilobite\", \"harvestman\", \"scorpion\", \"yellow garden spider\", \"barn spider\", \"European garden spider\", \"southern black widow\", \"tarantula\", \"wolf spider\", \"tick\", \"centipede\", \"black grouse\", \"ptarmigan\", \"ruffed grouse\", \"prairie grouse\", \"peafowl\", \"quail\", \"partridge\", \"african grey parrot\", \"macaw\", \"sulphur-crested cockatoo\", \"lorikeet\", \"coucal\", \"bee eater\", \"hornbill\", \"hummingbird\", \"jacamar\", \"toucan\", \"duck\", \"red-breasted merganser\", \"goose\", \"black swan\", \"tusker\", \"echidna\", \"platypus\", \"wallaby\", \"koala\", \"wombat\", \"jellyfish\", \"sea anemone\", \"brain coral\", \"flatworm\", \"nematode\", \"conch\", \"snail\", \"slug\", \"sea slug\", \"chiton\", \"chambered nautilus\", \"Dungeness crab\", \"rock crab\", \"fiddler crab\", \"red king crab\", \"American lobster\", \"spiny lobster\", \"crayfish\", \"hermit crab\", \"isopod\", \"white stork\", \"black stork\", \"spoonbill\", \"flamingo\", \"little blue heron\", \"great egret\", \"bittern bird\", \"crane bird\", \"limpkin\", \"common gallinule\", \"American coot\", \"bustard\", \"ruddy turnstone\", \"dunlin\", \"common redshank\", \"dowitcher\", \"oystercatcher\", \"pelican\", \"king penguin\", \"albatross\", \"grey whale\", \"killer whale\", \"dugong\", \"sea lion\", \"Chihuahua\", \"Japanese Chin\", \"Maltese\", \"Pekingese\", \"Shih Tzu\", \"King Charles Spaniel\", \"Papillon\", \"toy terrier\", \"Rhodesian Ridgeback\", \"Afghan Hound\", \"Basset Hound\", \"Beagle\", \"Bloodhound\", \"Bluetick Coonhound\", \"Black and Tan Coonhound\", \"Treeing Walker Coonhound\", \"English foxhound\", \"Redbone Coonhound\", \"borzoi\", \"Irish Wolfhound\", \"Italian Greyhound\", \"Whippet\", \"Ibizan Hound\", \"Norwegian Elkhound\", \"Otterhound\", \"Saluki\", \"Scottish Deerhound\", \"Weimaraner\", \"Staffordshire Bull Terrier\", \"American Staffordshire Terrier\", \"Bedlington Terrier\", \"Border Terrier\", \"Kerry Blue Terrier\", \"Irish Terrier\", \"Norfolk Terrier\", \"Norwich Terrier\", \"Yorkshire Terrier\", \"Wire Fox Terrier\", \"Lakeland Terrier\", \"Sealyham Terrier\", \"Airedale Terrier\", \"Cairn Terrier\", \"Australian Terrier\", \"Dandie Dinmont Terrier\", \"Boston Terrier\", \"Miniature Schnauzer\", \"Giant Schnauzer\", \"Standard Schnauzer\", \"Scottish Terrier\", \"Tibetan Terrier\", \"Australian Silky Terrier\", \"Soft-coated Wheaten Terrier\", \"West Highland White Terrier\", \"Lhasa Apso\", \"Flat-Coated Retriever\", \"Curly-coated Retriever\", \"Golden Retriever\", \"Labrador Retriever\", \"Chesapeake Bay Retriever\", \"German Shorthaired Pointer\", \"Vizsla\", \"English Setter\", \"Irish Setter\", \"Gordon Setter\", \"Brittany dog\", \"Clumber Spaniel\", \"English Springer Spaniel\", \"Welsh Springer Spaniel\", \"Cocker Spaniel\", \"Sussex Spaniel\", \"Irish Water Spaniel\", \"Kuvasz\", \"Schipperke\", \"Groenendael dog\", \"Malinois\", \"Briard\", \"Australian Kelpie\", \"Komondor\", \"Old English Sheepdog\", \"Shetland Sheepdog\", \"collie\", \"Border Collie\", \"Bouvier des Flandres dog\", \"Rottweiler\", \"German Shepherd Dog\", \"Dobermann\", \"Miniature Pinscher\", \"Greater Swiss Mountain Dog\", \"Bernese Mountain Dog\", \"Appenzeller Sennenhund\", \"Entlebucher Sennenhund\", \"Boxer\", \"Bullmastiff\", \"Tibetan Mastiff\", \"French Bulldog\", \"Great Dane\", \"St. Bernard\", \"husky\", \"Alaskan Malamute\", \"Siberian Husky\", \"Dalmatian\", \"Affenpinscher\", \"Basenji\", \"pug\", \"Leonberger\", \"Newfoundland dog\", \"Great Pyrenees dog\", \"Samoyed\", \"Pomeranian\", \"Chow Chow\", \"Keeshond\", \"brussels griffon\", \"Pembroke Welsh Corgi\", \"Cardigan Welsh Corgi\", \"Toy Poodle\", \"Miniature Poodle\", \"Standard Poodle\", \"Mexican hairless dog (xoloitzcuintli)\", \"grey wolf\", \"Alaskan tundra wolf\", \"red wolf or maned wolf\", \"coyote\", \"dingo\", \"dhole\", \"African wild dog\", \"hyena\", \"red fox\", \"kit fox\", \"Arctic fox\", \"grey fox\", \"tabby cat\", \"tiger cat\", \"Persian cat\", \"Siamese cat\", \"Egyptian Mau\", \"cougar\", \"lynx\", \"leopard\", \"snow leopard\", \"jaguar\", \"lion\", \"tiger\", \"cheetah\", \"brown bear\", \"American black bear\", \"polar bear\", \"sloth bear\", \"mongoose\", \"meerkat\", \"tiger beetle\", \"ladybug\", \"ground beetle\", \"longhorn beetle\", \"leaf beetle\", \"dung beetle\", \"rhinoceros beetle\", \"weevil\", \"fly\", \"bee\", \"ant\", \"grasshopper\", \"cricket insect\", \"stick insect\", \"cockroach\", \"praying mantis\", \"cicada\", \"leafhopper\", \"lacewing\", \"dragonfly\", \"damselfly\", \"red admiral butterfly\", \"ringlet butterfly\", \"monarch butterfly\", \"small white butterfly\", \"sulphur butterfly\", \"gossamer-winged butterfly\", \"starfish\", \"sea urchin\", \"sea cucumber\", \"cottontail rabbit\", \"hare\", \"Angora rabbit\", \"hamster\", \"porcupine\", \"fox squirrel\", \"marmot\", \"beaver\", \"guinea pig\", \"common sorrel horse\", \"zebra\", \"pig\", \"wild boar\", \"warthog\", \"hippopotamus\", \"ox\", \"water buffalo\", \"bison\", \"ram (adult male sheep)\", \"bighorn sheep\", \"Alpine ibex\", \"hartebeest\", \"impala (antelope)\", \"gazelle\", \"arabian camel\", \"llama\", \"weasel\", \"mink\", \"European polecat\", \"black-footed ferret\", \"otter\", \"skunk\", \"badger\", \"armadillo\", \"three-toed sloth\", \"orangutan\", \"gorilla\", \"chimpanzee\", \"gibbon\", \"siamang\", \"guenon\", \"patas monkey\", \"baboon\", \"macaque\", \"langur\", \"black-and-white colobus\", \"proboscis monkey\", \"marmoset\", \"white-headed capuchin\", \"howler monkey\", \"titi monkey\", \"Geoffroy's spider monkey\", \"common squirrel monkey\", \"ring-tailed lemur\", \"indri\", \"Asian elephant\", \"African bush elephant\", \"red panda\", \"giant panda\", \"snoek fish\", \"eel\", \"silver salmon\", \"rock beauty fish\", \"clownfish\", \"sturgeon\", \"gar fish\", \"lionfish\", \"pufferfish\", \"abacus\", \"abaya\", \"academic gown\", \"accordion\", \"acoustic guitar\", \"aircraft carrier\", \"airliner\", \"airship\", \"altar\", \"ambulance\", \"amphibious vehicle\", \"analog clock\", \"apiary\", \"apron\", \"trash can\", \"assault rifle\", \"backpack\", \"bakery\", \"balance beam\", \"balloon\", \"ballpoint pen\", \"Band-Aid\", \"banjo\", \"baluster / handrail\", \"barbell\", \"barber chair\", \"barbershop\", \"barn\", \"barometer\", \"barrel\", \"wheelbarrow\", \"baseball\", \"basketball\", \"bassinet\", \"bassoon\", \"swimming cap\", \"bath towel\", \"bathtub\", \"station wagon\", \"lighthouse\", \"beaker\", \"military hat (bearskin or shako)\", \"beer bottle\", \"beer glass\", \"bell tower\", \"baby bib\", \"tandem bicycle\", \"bikini\", \"ring binder\", \"binoculars\", \"birdhouse\", \"boathouse\", \"bobsleigh\", \"bolo tie\", \"poke bonnet\", \"bookcase\", \"bookstore\", \"bottle cap\", \"hunting bow\", \"bow tie\", \"brass memorial plaque\", \"bra\", \"breakwater\", \"breastplate\", \"broom\", \"bucket\", \"buckle\", \"bulletproof vest\", \"high-speed train\", \"butcher shop\", \"taxicab\", \"cauldron\", \"candle\", \"cannon\", \"canoe\", \"can opener\", \"cardigan\", \"car mirror\", \"carousel\", \"tool kit\", \"cardboard box / carton\", \"car wheel\", \"automated teller machine\", \"cassette\", \"cassette player\", \"castle\", \"catamaran\", \"CD player\", \"cello\", \"mobile phone\", \"chain\", \"chain-link fence\", \"chain mail\", \"chainsaw\", \"storage chest\", \"chiffonier\", \"bell or wind chime\", \"china cabinet\", \"Christmas stocking\", \"church\", \"movie theater\", \"cleaver\", \"cliff dwelling\", \"cloak\", \"clogs\", \"cocktail shaker\", \"coffee mug\", \"coffeemaker\", \"spiral or coil\", \"combination lock\", \"computer keyboard\", \"candy store\", \"container ship\", \"convertible\", \"corkscrew\", \"cornet\", \"cowboy boot\", \"cowboy hat\", \"cradle\", \"construction crane\", \"crash helmet\", \"crate\", \"infant bed\", \"Crock Pot\", \"croquet ball\", \"crutch\", \"cuirass\", \"dam\", \"desk\", \"desktop computer\", \"rotary dial telephone\", \"diaper\", \"digital clock\", \"digital watch\", \"dining table\", \"dishcloth\", \"dishwasher\", \"disc brake\", \"dock\", \"dog sled\", \"dome\", \"doormat\", \"drilling rig\", \"drum\", \"drumstick\", \"dumbbell\", \"Dutch oven\", \"electric fan\", \"electric guitar\", \"electric locomotive\", \"entertainment center\", \"envelope\", \"espresso machine\", \"face powder\", \"feather boa\", \"filing cabinet\", \"fireboat\", \"fire truck\", \"fire screen\", \"flagpole\", \"flute\", \"folding chair\", \"football helmet\", \"forklift\", \"fountain\", \"fountain pen\", \"four-poster bed\", \"freight car\", \"French horn\", \"frying pan\", \"fur coat\", \"garbage truck\", \"gas mask or respirator\", \"gas pump\", \"goblet\", \"go-kart\", \"golf ball\", \"golf cart\", \"gondola\", \"gong\", \"gown\", \"grand piano\", \"greenhouse\", \"radiator grille\", \"grocery store\", \"guillotine\", \"hair clip\", \"hair spray\", \"half-track\", \"hammer\", \"hamper\", \"hair dryer\", \"hand-held computer\", \"handkerchief\", \"hard disk drive\", \"harmonica\", \"harp\", \"combine harvester\", \"hatchet\", \"holster\", \"home theater\", \"honeycomb\", \"hook\", \"hoop skirt\", \"gymnastic horizontal bar\", \"horse-drawn vehicle\", \"hourglass\", \"iPod\", \"clothes iron\", \"carved pumpkin\", \"jeans\", \"jeep\", \"T-shirt\", \"jigsaw puzzle\", \"rickshaw\", \"joystick\", \"kimono\", \"knee pad\", \"knot\", \"lab coat\", \"ladle\", \"lampshade\", \"laptop computer\", \"lawn mower\", \"lens cap\", \"letter opener\", \"library\", \"lifeboat\", \"lighter\", \"limousine\", \"ocean liner\", \"lipstick\", \"slip-on shoe\", \"lotion\", \"music speaker\", \"loupe magnifying glass\", \"sawmill\", \"magnetic compass\", \"messenger bag\", \"mailbox\", \"tights\", \"one-piece bathing suit\", \"manhole cover\", \"maraca\", \"marimba\", \"mask\", \"matchstick\", \"maypole\", \"maze\", \"measuring cup\", \"medicine cabinet\", \"megalith\", \"microphone\", \"microwave oven\", \"military uniform\", \"milk can\", \"minibus\", \"miniskirt\", \"minivan\", \"missile\", \"mitten\", \"mixing bowl\", \"mobile home\", \"ford model t\", \"modem\", \"monastery\", \"monitor\", \"moped\", \"mortar and pestle\", \"graduation cap\", \"mosque\", \"mosquito net\", \"vespa\", \"mountain bike\", \"tent\", \"computer mouse\", \"mousetrap\", \"moving van\", \"muzzle\", \"metal nail\", \"neck brace\", \"necklace\", \"baby pacifier\", \"notebook computer\", \"obelisk\", \"oboe\", \"ocarina\", \"odometer\", \"oil filter\", \"pipe organ\", \"oscilloscope\", \"overskirt\", \"bullock cart\", \"oxygen mask\", \"product packet / packaging\", \"paddle\", \"paddle wheel\", \"padlock\", \"paintbrush\", \"pajamas\", \"palace\", \"pan flute\", \"paper towel\", \"parachute\", \"parallel bars\", \"park bench\", \"parking meter\", \"railroad car\", \"patio\", \"payphone\", \"pedestal\", \"pencil case\", \"pencil sharpener\", \"perfume\", \"Petri dish\", \"photocopier\", \"plectrum\", \"Pickelhaube\", \"picket fence\", \"pickup truck\", \"pier\", \"piggy bank\", \"pill bottle\", \"pillow\", \"ping-pong ball\", \"pinwheel\", \"pirate ship\", \"drink pitcher\", \"block plane\", \"planetarium\", \"plastic bag\", \"plate rack\", \"farm plow\", \"plunger\", \"Polaroid camera\", \"pole\", \"police van\", \"poncho\", \"pool table\", \"soda bottle\", \"plant pot\", \"potter's wheel\", \"power drill\", \"prayer rug\", \"printer\", \"prison\", \"missile\", \"projector\", \"hockey puck\", \"punching bag\", \"purse\", \"quill\", \"quilt\", \"race car\", \"racket\", \"radiator\", \"radio\", \"radio telescope\", \"rain barrel\", \"recreational vehicle\", \"fishing casting reel\", \"reflex camera\", \"refrigerator\", \"remote control\", \"restaurant\", \"revolver\", \"rifle\", \"rocking chair\", \"rotisserie\", \"eraser\", \"rugby ball\", \"ruler measuring stick\", \"sneaker\", \"safe\", \"safety pin\", \"salt shaker\", \"sandal\", \"sarong\", \"saxophone\", \"scabbard\", \"weighing scale\", \"school bus\", \"schooner\", \"scoreboard\", \"CRT monitor\", \"screw\", \"screwdriver\", \"seat belt\", \"sewing machine\", \"shield\", \"shoe store\", \"shoji screen / room divider\", \"shopping basket\", \"shopping cart\", \"shovel\", \"shower cap\", \"shower curtain\", \"ski\", \"balaclava ski mask\", \"sleeping bag\", \"slide rule\", \"sliding door\", \"slot machine\", \"snorkel\", \"snowmobile\", \"snowplow\", \"soap dispenser\", \"soccer ball\", \"sock\", \"solar thermal collector\", \"sombrero\", \"soup bowl\", \"keyboard space bar\", \"space heater\", \"space shuttle\", \"spatula\", \"motorboat\", \"spider web\", \"spindle\", \"sports car\", \"spotlight\", \"stage\", \"steam locomotive\", \"through arch bridge\", \"steel drum\", \"stethoscope\", \"scarf\", \"stone wall\", \"stopwatch\", \"stove\", \"strainer\", \"tram\", \"stretcher\", \"couch\", \"stupa\", \"submarine\", \"suit\", \"sundial\", \"sunglasses\", \"sunglasses\", \"sunscreen\", \"suspension bridge\", \"mop\", \"sweatshirt\", \"swim trunks / shorts\", \"swing\", \"electrical switch\", \"syringe\", \"table lamp\", \"tank\", \"tape player\", \"teapot\", \"teddy bear\", \"television\", \"tennis ball\", \"thatched roof\", \"front curtain\", \"thimble\", \"threshing machine\", \"throne\", \"tile roof\", \"toaster\", \"tobacco shop\", \"toilet seat\", \"torch\", \"totem pole\", \"tow truck\", \"toy store\", \"tractor\", \"semi-trailer truck\", \"tray\", \"trench coat\", \"tricycle\", \"trimaran\", \"tripod\", \"triumphal arch\", \"trolleybus\", \"trombone\", \"hot tub\", \"turnstile\", \"typewriter keyboard\", \"umbrella\", \"unicycle\", \"upright piano\", \"vacuum cleaner\", \"vase\", \"vaulted or arched ceiling\", \"velvet fabric\", \"vending machine\", \"vestment\", \"viaduct\", \"violin\", \"volleyball\", \"waffle iron\", \"wall clock\", \"wallet\", \"wardrobe\", \"military aircraft\", \"sink\", \"washing machine\", \"water bottle\", \"water jug\", \"water tower\", \"whiskey jug\", \"whistle\", \"hair wig\", \"window screen\", \"window shade\", \"Windsor tie\", \"wine bottle\", \"airplane wing\", \"wok\", \"wooden spoon\", \"wool\", \"split-rail fence\", \"shipwreck\", \"sailboat\", \"yurt\", \"website\", \"comic book\", \"crossword\", \"traffic or street sign\", \"traffic light\", \"dust jacket\", \"menu\", \"plate\", \"guacamole\", \"consomme\", \"hot pot\", \"trifle\", \"ice cream\", \"popsicle\", \"baguette\", \"bagel\", \"pretzel\", \"cheeseburger\", \"hot dog\", \"mashed potatoes\", \"cabbage\", \"broccoli\", \"cauliflower\", \"zucchini\", \"spaghetti squash\", \"acorn squash\", \"butternut squash\", \"cucumber\", \"artichoke\", \"bell pepper\", \"cardoon\", \"mushroom\", \"Granny Smith apple\", \"strawberry\", \"orange\", \"lemon\", \"fig\", \"pineapple\", \"banana\", \"jackfruit\", \"cherimoya (custard apple)\", \"pomegranate\", \"hay\", \"carbonara\", \"chocolate syrup\", \"dough\", \"meatloaf\", \"pizza\", \"pot pie\", \"burrito\", \"red wine\", \"espresso\", \"tea cup\", \"eggnog\", \"mountain\", \"bubble\", \"cliff\", \"coral reef\", \"geyser\", \"lakeshore\", \"promontory\", \"sandbar\", \"beach\", \"valley\", \"volcano\", \"baseball player\", \"bridegroom\", \"scuba diver\", \"rapeseed\", \"daisy\", \"yellow lady's slipper\", \"corn\", \"acorn\", \"rose hip\", \"horse chestnut seed\", \"coral fungus\", \"agaric\", \"gyromitra\", \"stinkhorn mushroom\", \"earth star fungus\", \"hen of the woods mushroom\", \"bolete\", \"corn cob\", \"toilet paper\"]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "77388348", - "metadata": {}, - "outputs": [], - "source": [ - "# The following comes from here: https://github.com/mlfoundations/open_clip/blob/main/src/open_clip/zero_shot_metadata.py\n", - "# Original reference: https://github.com/openai/CLIP/blob/main/notebooks/Prompt_Engineering_for_ImageNet.ipynb\n", - "openai_imagenet_templates = (\n", - " lambda c: f\"a bad photo of a {c}.\",\n", - " lambda c: f\"a photo of many {c}.\",\n", - " lambda c: f\"a sculpture of a {c}.\",\n", - " lambda c: f\"a photo of the hard to see {c}.\",\n", - " lambda c: f\"a low resolution photo of the {c}.\",\n", - " lambda c: f\"a rendering of a {c}.\",\n", - " lambda c: f\"graffiti of a {c}.\",\n", - " lambda c: f\"a bad photo of the {c}.\",\n", - " lambda c: f\"a cropped photo of the {c}.\",\n", - " lambda c: f\"a tattoo of a {c}.\",\n", - " lambda c: f\"the embroidered {c}.\",\n", - " lambda c: f\"a photo of a hard to see {c}.\",\n", - " lambda c: f\"a bright photo of a {c}.\",\n", - " lambda c: f\"a photo of a clean {c}.\",\n", - " lambda c: f\"a photo of a dirty {c}.\",\n", - " lambda c: f\"a dark photo of the {c}.\",\n", - " lambda c: f\"a drawing of a {c}.\",\n", - " lambda c: f\"a photo of my {c}.\",\n", - " lambda c: f\"the plastic {c}.\",\n", - " lambda c: f\"a photo of the cool {c}.\",\n", - " lambda c: f\"a close-up photo of a {c}.\",\n", - " lambda c: f\"a black and white photo of the {c}.\",\n", - " lambda c: f\"a painting of the {c}.\",\n", - " lambda c: f\"a painting of a {c}.\",\n", - " lambda c: f\"a pixelated photo of the {c}.\",\n", - " lambda c: f\"a sculpture of the {c}.\",\n", - " lambda c: f\"a bright photo of the {c}.\",\n", - " lambda c: f\"a cropped photo of a {c}.\",\n", - " lambda c: f\"a plastic {c}.\",\n", - " lambda c: f\"a photo of the dirty {c}.\",\n", - " lambda c: f\"a jpeg corrupted photo of a {c}.\",\n", - " lambda c: f\"a blurry photo of the {c}.\",\n", - " lambda c: f\"a photo of the {c}.\",\n", - " lambda c: f\"a good photo of the {c}.\",\n", - " lambda c: f\"a rendering of the {c}.\",\n", - " lambda c: f\"a {c} in a video game.\",\n", - " lambda c: f\"a photo of one {c}.\",\n", - " lambda c: f\"a doodle of a {c}.\",\n", - " lambda c: f\"a close-up photo of the {c}.\",\n", - " lambda c: f\"a photo of a {c}.\",\n", - " lambda c: f\"the origami {c}.\",\n", - " lambda c: f\"the {c} in a video game.\",\n", - " lambda c: f\"a sketch of a {c}.\",\n", - " lambda c: f\"a doodle of the {c}.\",\n", - " lambda c: f\"a origami {c}.\",\n", - " lambda c: f\"a low resolution photo of a {c}.\",\n", - " lambda c: f\"the toy {c}.\",\n", - " lambda c: f\"a rendition of the {c}.\",\n", - " lambda c: f\"a photo of the clean {c}.\",\n", - " lambda c: f\"a photo of a large {c}.\",\n", - " lambda c: f\"a rendition of a {c}.\",\n", - " lambda c: f\"a photo of a nice {c}.\",\n", - " lambda c: f\"a photo of a weird {c}.\",\n", - " lambda c: f\"a blurry photo of a {c}.\",\n", - " lambda c: f\"a cartoon {c}.\",\n", - " lambda c: f\"art of a {c}.\",\n", - " lambda c: f\"a sketch of the {c}.\",\n", - " lambda c: f\"a embroidered {c}.\",\n", - " lambda c: f\"a pixelated photo of a {c}.\",\n", - " lambda c: f\"itap of the {c}.\",\n", - " lambda c: f\"a jpeg corrupted photo of the {c}.\",\n", - " lambda c: f\"a good photo of a {c}.\",\n", - " lambda c: f\"a plushie {c}.\",\n", - " lambda c: f\"a photo of the nice {c}.\",\n", - " lambda c: f\"a photo of the small {c}.\",\n", - " lambda c: f\"a photo of the weird {c}.\",\n", - " lambda c: f\"the cartoon {c}.\",\n", - " lambda c: f\"art of the {c}.\",\n", - " lambda c: f\"a drawing of the {c}.\",\n", - " lambda c: f\"a photo of the large {c}.\",\n", - " lambda c: f\"a black and white photo of a {c}.\",\n", - " lambda c: f\"the plushie {c}.\",\n", - " lambda c: f\"a dark photo of a {c}.\",\n", - " lambda c: f\"itap of a {c}.\",\n", - " lambda c: f\"graffiti of the {c}.\",\n", - " lambda c: f\"a toy {c}.\",\n", - " lambda c: f\"itap of my {c}.\",\n", - " lambda c: f\"a photo of a cool {c}.\",\n", - " lambda c: f\"a photo of a small {c}.\",\n", - " lambda c: f\"a tattoo of the {c}.\",\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "38e0a1e8", - "metadata": {}, - "outputs": [], - "source": [ - "from torchvision.datasets import ImageFolder\n", - "from dinov3.data.transforms import make_classification_eval_transform\n", - "\n", - "image_preprocess = make_classification_eval_transform(resize_size=512, crop_size=512)\n", - "# Please update the following directory to the root of ImageNet1k val dataset.\n", - "imagenet_val_root_dir = \"\"\n", - "val_dataset = ImageFolder(imagenet_val_root_dir, image_preprocess)\n", - "model = model.eval().cuda()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c80b1674", - "metadata": {}, - "outputs": [], - "source": [ - "def zeroshot_classifier(classnames, templates, tokenizer):\n", - " with torch.no_grad():\n", - " zeroshot_weights = []\n", - " for classname in classnames:\n", - " texts = [template(classname) for template in templates] #format with class\n", - " texts = tokenizer.tokenize(texts).cuda() #tokenize\n", - " class_embeddings = model.encode_text(texts) #embed with text encoder\n", - " class_embeddings /= class_embeddings.norm(dim=-1, keepdim=True)\n", - " class_embedding = class_embeddings.mean(dim=0)\n", - " class_embedding /= class_embedding.norm()\n", - " zeroshot_weights.append(class_embedding)\n", - " zeroshot_weights = torch.stack(zeroshot_weights, dim=1).cuda()\n", - " return zeroshot_weights\n", - "zeroshot_weights = zeroshot_classifier(imagenet_clip_class_names, openai_imagenet_templates, tokenizer)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9544facf", - "metadata": {}, - "outputs": [], - "source": [ - "class_name_to_ids = {class_name:idx for idx, class_name in enumerate(imagenet_clip_class_names)}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b8c2b370", - "metadata": {}, - "outputs": [], - "source": [ - "def accuracy(output, target, topk=(1,)):\n", - " pred = output.topk(max(topk), 1, True, True)[1].t()\n", - " correct = pred.eq(target.view(1, -1).expand_as(pred))\n", - " return [correct[:k].reshape(-1).sum(0, keepdim=True) for k in topk]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "727bed3a", - "metadata": {}, - "outputs": [], - "source": [ - "images = []\n", - "class_ids = []\n", - "batch_size = 64\n", - "num_workers = 8\n", - "top1, top5, n = 0., 0., 0.\n", - "val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=batch_size, shuffle=False, num_workers=num_workers, pin_memory=True)\n", - "for images, targets in val_loader:\n", - " with torch.autocast('cuda', dtype=torch.float):\n", - " with torch.no_grad():\n", - " image_features = model.encode_image(images.cuda())\n", - " image_features /= image_features.norm(dim=-1, keepdim=True)\n", - " logits = 100. * image_features @ zeroshot_weights\n", - " acc1, acc5 = accuracy(logits, targets.cuda(), topk=(1, 5))\n", - " top1 += acc1\n", - " top5 += acc5\n", - " n += len(images)\n", - " images = []\n", - " class_ids = []\n", - "top1 = (top1.item() / n) * 100\n", - "top5 = (top5.item() / n) * 100 \n", - "print(f\"Top-1 accuracy: {top1}\")\n", - "print(f\"Top-5 accuracy: {top5}\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "dinov3_minimal", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/notebooks/foreground_segmentation.ipynb b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/notebooks/foreground_segmentation.ipynb deleted file mode 100644 index 2cc58836aaf10619d986f9f66ebae75939079498..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/notebooks/foreground_segmentation.ipynb +++ /dev/null @@ -1,1065 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "9966b9cd", - "metadata": {}, - "source": [ - "# Training a Foreground Segmentation Tool with DINOv3\n", - "\n", - "In this tutorial, we will train a linear foreground segmentation model using DINOv3 features." - ] - }, - { - "cell_type": "markdown", - "id": "a03c01a4-2fe0-4d61-b05c-9d3274bda2df", - "metadata": {}, - "source": [ - "### Setup\n", - "\n", - "Let's start by loading some pre-requisites and checking the DINOv3 repository location:\n", - "- `local` if `DINOV3_LOCATION` environment variable was set to work with a local version of DINOv3 repository;\n", - "- `github` if the code should be loaded via torch hub." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "7db1d46f", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "DINOv3 location set to /private/home/vkhalidov/fairvit_dinov3_release/app/dinov3_release/\n" - ] - } - ], - "source": [ - "import io\n", - "import os\n", - "import pickle\n", - "import tarfile\n", - "import urllib\n", - "\n", - "from PIL import Image\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from scipy import signal\n", - "from sklearn.metrics import precision_recall_curve\n", - "from sklearn.metrics import average_precision_score\n", - "from sklearn.linear_model import LogisticRegression\n", - "import torch\n", - "import torchvision.transforms.functional as TF\n", - "from tqdm import tqdm\n", - "\n", - "DINOV3_GITHUB_LOCATION = \"facebookresearch/dinov3\"\n", - "\n", - "if os.getenv(\"DINOV3_LOCATION\") is not None:\n", - " DINOV3_LOCATION = os.getenv(\"DINOV3_LOCATION\")\n", - "else:\n", - " DINOV3_LOCATION = DINOV3_GITHUB_LOCATION\n", - "\n", - "print(f\"DINOv3 location set to {DINOV3_LOCATION}\")" - ] - }, - { - "cell_type": "markdown", - "id": "60692f17", - "metadata": {}, - "source": [ - "### Model\n", - "\n", - "Let's load the DINOv3 model. For this notebook, we will be using the ViT-L model, but if you have more or less hardware constraints, you can easily load any other DINOv3 model!" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "e91ecf2c", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "DinoVisionTransformer(\n", - " (patch_embed): PatchEmbed(\n", - " (proj): Conv2d(3, 1024, kernel_size=(16, 16), stride=(16, 16))\n", - " (norm): Identity()\n", - " )\n", - " (rope_embed): RopePositionEmbedding()\n", - " (blocks): ModuleList(\n", - " (0-23): 24 x SelfAttentionBlock(\n", - " (norm1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n", - " (attn): SelfAttention(\n", - " (qkv): LinearKMaskedBias(in_features=1024, out_features=3072, bias=True)\n", - " (attn_drop): Dropout(p=0.0, inplace=False)\n", - " (proj): Linear(in_features=1024, out_features=1024, bias=True)\n", - " (proj_drop): Dropout(p=0.0, inplace=False)\n", - " )\n", - " (ls1): LayerScale()\n", - " (norm2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n", - " (mlp): Mlp(\n", - " (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n", - " (act): GELU(approximate='none')\n", - " (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n", - " (drop): Dropout(p=0.0, inplace=False)\n", - " )\n", - " (ls2): LayerScale()\n", - " )\n", - " )\n", - " (norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n", - " (head): Identity()\n", - ")" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# examples of available DINOv3 models:\n", - "MODEL_DINOV3_VITS = \"dinov3_vits16\"\n", - "MODEL_DINOV3_VITSP = \"dinov3_vits16plus\"\n", - "MODEL_DINOV3_VITB = \"dinov3_vitb16\"\n", - "MODEL_DINOV3_VITL = \"dinov3_vitl16\"\n", - "MODEL_DINOV3_VITHP = \"dinov3_vith16plus\"\n", - "MODEL_DINOV3_VIT7B = \"dinov3_vit7b16\"\n", - "\n", - "MODEL_NAME = MODEL_DINOV3_VITL\n", - "\n", - "model = torch.hub.load(\n", - " repo_or_dir=DINOV3_LOCATION,\n", - " model=MODEL_NAME,\n", - " source=\"local\" if DINOV3_LOCATION != DINOV3_GITHUB_LOCATION else \"github\",\n", - ")\n", - "model.cuda()" - ] - }, - { - "cell_type": "markdown", - "id": "ad3a9aab", - "metadata": {}, - "source": [ - "### Data\n", - "Now that we have the model set up, let's load the training data. It consists of:\n", - "\n", - "- images in `jpg` format:\n", - "```\n", - "https://dl.fbaipublicfiles.com/dinov3/notebooks/foreground_segmentation/foreground_segmentation_images.tar.gz\n", - "```\n", - "\n", - "- and segmentation masks stored as alpha channels in `png` files:\n", - "```\n", - "https://dl.fbaipublicfiles.com/dinov3/notebooks/foreground_segmentation/foreground_segmentation_labels.tar.gz\n", - "```\n", - "\n", - "In total, there are 9 training image / mask pairs.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "dc40dc59", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loaded 9 images and labels\n" - ] - } - ], - "source": [ - "IMAGES_URI = \"https://dl.fbaipublicfiles.com/dinov3/notebooks/foreground_segmentation/foreground_segmentation_images.tar.gz\"\n", - "LABELS_URI = \"https://dl.fbaipublicfiles.com/dinov3/notebooks/foreground_segmentation/foreground_segmentation_labels.tar.gz\"\n", - "\n", - "def load_images_from_remote_tar(tar_uri: str) -> list[Image.Image]:\n", - " images = []\n", - " with urllib.request.urlopen(tar_uri) as f:\n", - " tar = tarfile.open(fileobj=io.BytesIO(f.read()))\n", - " for member in tar.getmembers():\n", - " image_data = tar.extractfile(member)\n", - " image = Image.open(image_data)\n", - " images.append(image)\n", - " return images\n", - " \n", - "images = load_images_from_remote_tar(IMAGES_URI)\n", - "labels = load_images_from_remote_tar(LABELS_URI)\n", - "n_images = len(images)\n", - "assert n_images == len(labels), f\"{len(images)=}, {len(labels)=}\"\n", - "\n", - "print(f\"Loaded {n_images} images and labels\")" - ] - }, - { - "cell_type": "markdown", - "id": "4dc6eb95-bd20-4e54-a967-81304aac9866", - "metadata": {}, - "source": [ - "Let's, for example, visualize the first image / mask pair:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "48333927-9ed4-4f32-beb9-f3d57fc6574d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Showing image / mask at index 0:\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "data_index = 0\n", - "\n", - "print(f\"Showing image / mask at index {data_index}:\")\n", - "\n", - "image = images[data_index]\n", - "mask = labels[data_index]\n", - "foreground = Image.composite(image, mask, mask)\n", - "mask_bg_np = np.copy(np.array(mask))\n", - "mask_bg_np[:, :, 3] = 255 - mask_bg_np[:, :, 3]\n", - "mask_bg = Image.fromarray(mask_bg_np)\n", - "background = Image.composite(image, mask_bg, mask_bg)\n", - "\n", - "data_to_show = [image, mask, foreground, background]\n", - "data_labels = [\"Image\", \"Mask\", \"Foreground\", \"Background\"]\n", - "\n", - "plt.figure(figsize=(16, 4), dpi=300)\n", - "for i in range(len(data_to_show)):\n", - " plt.subplot(1, len(data_to_show), i + 1)\n", - " plt.imshow(data_to_show[i])\n", - " plt.axis('off')\n", - " plt.title(data_labels[i], fontsize=12)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "54002c83", - "metadata": {}, - "source": [ - "### Building Per-Patch Label Map\n", - "\n", - "Since our models run with a patch size of 16, we have to quantize the ground truth to a 16x16 pixels grid. To achieve this, we define:\n", - "- the resize transform to resize an image such that it aligns well with the 16x16 grid;\n", - "- a uniform 16x16 conv layer as a [box blur filter](https://en.wikipedia.org/wiki/Box_blur) with stride equal to the patch size." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "b4317cae", - "metadata": {}, - "outputs": [], - "source": [ - "PATCH_SIZE = 16\n", - "IMAGE_SIZE = 768\n", - "\n", - "# quantization filter for the given patch size\n", - "patch_quant_filter = torch.nn.Conv2d(1, 1, PATCH_SIZE, stride=PATCH_SIZE, bias=False)\n", - "patch_quant_filter.weight.data.fill_(1.0 / (PATCH_SIZE * PATCH_SIZE))\n", - "\n", - "# image resize transform to dimensions divisible by patch size\n", - "def resize_transform(\n", - " mask_image: Image,\n", - " image_size: int = IMAGE_SIZE,\n", - " patch_size: int = PATCH_SIZE,\n", - ") -> torch.Tensor:\n", - " w, h = mask_image.size\n", - " h_patches = int(image_size / patch_size)\n", - " w_patches = int((w * image_size) / (h * patch_size))\n", - " return TF.to_tensor(TF.resize(mask_image, (h_patches * patch_size, w_patches * patch_size)))" - ] - }, - { - "cell_type": "markdown", - "id": "1ab9673f-4a6f-43ca-8ba2-2c6ca39ff460", - "metadata": {}, - "source": [ - "Let's, for example, visualize the first mask before and after quantization:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "32657c30-8f71-46bb-a2f5-c7f47d3e94d1", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "mask_0 = labels[0].split()[-1]\n", - "mask_0_resized = resize_transform(mask_0)\n", - "with torch.no_grad():\n", - " mask_0_quantized = patch_quant_filter(mask_0_resized).squeeze().detach().cpu()\n", - "\n", - "plt.figure(figsize=(4, 2), dpi=300)\n", - "plt.subplot(1, 2, 1)\n", - "plt.imshow(mask_0)\n", - "plt.axis('off')\n", - "plt.title(f\"Original Mask, Size {mask_0.size}\", fontsize=5)\n", - "plt.subplot(1, 2, 2)\n", - "plt.imshow(mask_0_quantized)\n", - "plt.axis('off')\n", - "plt.title(f\"Quantized Mask, Size {tuple(mask_0_quantized.shape)}\", fontsize=5)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "d0763e21", - "metadata": {}, - "source": [ - "### Extracting Features and Labels for All the Images\n", - "Now we will loop over the 9 training images, and extract for each image the patch labels, as well as the patch features. That involves running the dense feature extraction of our model with :\n", - "\n", - "```\n", - "with torch.no_grad(): \n", - " feats = model.get_intermediate_layers(img, n=range(n_layers), reshape=True, norm=True)\n", - " dim = feats[-1].shape[1]\n", - " xs.append(feats[-1].squeeze().view(dim, -1).permute(1,0).detach().cpu())\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "a47d6a1b", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Processing images: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 9/9 [00:08<00:00, 1.12it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Design matrix of size : torch.Size([23604, 1024])\n", - "Label matrix of size : torch.Size([23604])\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "xs = []\n", - "ys = []\n", - "image_index = []\n", - "\n", - "IMAGENET_MEAN = (0.485, 0.456, 0.406)\n", - "IMAGENET_STD = (0.229, 0.224, 0.225)\n", - "\n", - "MODEL_TO_NUM_LAYERS = {\n", - " MODEL_DINOV3_VITS: 12,\n", - " MODEL_DINOV3_VITSP: 12,\n", - " MODEL_DINOV3_VITB: 12,\n", - " MODEL_DINOV3_VITL: 24,\n", - " MODEL_DINOV3_VITHP: 32,\n", - " MODEL_DINOV3_VIT7B: 40,\n", - "}\n", - "\n", - "n_layers = MODEL_TO_NUM_LAYERS[MODEL_NAME]\n", - "\n", - "with torch.inference_mode():\n", - " with torch.autocast(device_type='cuda', dtype=torch.float32):\n", - " for i in tqdm(range(n_images), desc=\"Processing images\"):\n", - " # Loading the ground truth\n", - " mask_i = labels[i].split()[-1]\n", - " mask_i_resized = resize_transform(mask_i)\n", - " mask_i_quantized = patch_quant_filter(mask_i_resized).squeeze().view(-1).detach().cpu()\n", - " ys.append(mask_i_quantized)\n", - " # Loading the image data \n", - " image_i = images[i].convert('RGB')\n", - " image_i_resized = resize_transform(image_i)\n", - " image_i_resized = TF.normalize(image_i_resized, mean=IMAGENET_MEAN, std=IMAGENET_STD)\n", - " image_i_resized = image_i_resized.unsqueeze(0).cuda()\n", - "\n", - " feats = model.get_intermediate_layers(image_i_resized, n=range(n_layers), reshape=True, norm=True)\n", - " dim = feats[-1].shape[1]\n", - " xs.append(feats[-1].squeeze().view(dim, -1).permute(1,0).detach().cpu())\n", - "\n", - " image_index.append(i * torch.ones(ys[-1].shape))\n", - "\n", - "\n", - "# Concatenate all lists into torch tensors \n", - "xs = torch.cat(xs)\n", - "ys = torch.cat(ys)\n", - "image_index = torch.cat(image_index)\n", - "\n", - "# keeping only the patches that have clear positive or negative label\n", - "idx = (ys < 0.01) | (ys > 0.99)\n", - "xs = xs[idx]\n", - "ys = ys[idx]\n", - "image_index = image_index[idx]\n", - "\n", - "print(\"Design matrix of size : \", xs.shape)\n", - "print(\"Label matrix of size : \", ys.shape)" - ] - }, - { - "cell_type": "markdown", - "id": "fade40b7", - "metadata": {}, - "source": [ - "### Training a Classifier and Model Selection\n", - "We computed the features, let's now train a classifier! Our data is very strongly correlated image-by-image. Therefore, to do proper model selection, we can't simply split the data in an IID way. We need to do something a bit smarter. We will do leave-one-out, and consecutively exclude each image as a validation set. \n", - "We'll try 8 values of C ranging from 1e-7 to 1e-0. \n", - "\n", - "For each value of C and each image, we plot the precision-recall curve of the classifier, and report the mAP (area under the PR curve)." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "f92f6714", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "validation using image_01.jpg\n", - "training logisitic regression with C=1.00e-07\n", - "training logisitic regression with C=1.00e-06\n", - "training logisitic regression with C=1.00e-05\n", - "training logisitic regression with C=1.00e-04\n", - "training logisitic regression with C=1.00e-03\n", - "training logisitic regression with C=1.00e-02\n", - "training logisitic regression with C=1.00e-01\n", - "training logisitic regression with C=1.00e+00\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "validation using image_02.jpg\n", - "training logisitic regression with C=1.00e-07\n", - "training logisitic regression with C=1.00e-06\n", - "training logisitic regression with C=1.00e-05\n", - "training logisitic regression with C=1.00e-04\n", - "training logisitic regression with C=1.00e-03\n", - "training logisitic regression with C=1.00e-02\n", - "training logisitic regression with C=1.00e-01\n", - "training logisitic regression with C=1.00e+00\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "validation using image_03.jpg\n", - "training logisitic regression with C=1.00e-07\n", - "training logisitic regression with C=1.00e-06\n", - "training logisitic regression with C=1.00e-05\n", - "training logisitic regression with C=1.00e-04\n", - "training logisitic regression with C=1.00e-03\n", - "training logisitic regression with C=1.00e-02\n", - "training logisitic regression with C=1.00e-01\n", - "training logisitic regression with C=1.00e+00\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "validation using image_04.jpg\n", - "training logisitic regression with C=1.00e-07\n", - "training logisitic regression with C=1.00e-06\n", - "training logisitic regression with C=1.00e-05\n", - "training logisitic regression with C=1.00e-04\n", - "training logisitic regression with C=1.00e-03\n", - "training logisitic regression with C=1.00e-02\n", - "training logisitic regression with C=1.00e-01\n", - "training logisitic regression with C=1.00e+00\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "validation using image_05.jpg\n", - "training logisitic regression with C=1.00e-07\n", - "training logisitic regression with C=1.00e-06\n", - "training logisitic regression with C=1.00e-05\n", - "training logisitic regression with C=1.00e-04\n", - "training logisitic regression with C=1.00e-03\n", - "training logisitic regression with C=1.00e-02\n", - "training logisitic regression with C=1.00e-01\n", - "training logisitic regression with C=1.00e+00\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "validation using image_06.jpg\n", - "training logisitic regression with C=1.00e-07\n", - "training logisitic regression with C=1.00e-06\n", - "training logisitic regression with C=1.00e-05\n", - "training logisitic regression with C=1.00e-04\n", - "training logisitic regression with C=1.00e-03\n", - "training logisitic regression with C=1.00e-02\n", - "training logisitic regression with C=1.00e-01\n", - "training logisitic regression with C=1.00e+00\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "validation using image_07.jpg\n", - "training logisitic regression with C=1.00e-07\n", - "training logisitic regression with C=1.00e-06\n", - "training logisitic regression with C=1.00e-05\n", - "training logisitic regression with C=1.00e-04\n", - "training logisitic regression with C=1.00e-03\n", - "training logisitic regression with C=1.00e-02\n", - "training logisitic regression with C=1.00e-01\n", - "training logisitic regression with C=1.00e+00\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "validation using image_08.jpg\n", - "training logisitic regression with C=1.00e-07\n", - "training logisitic regression with C=1.00e-06\n", - "training logisitic regression with C=1.00e-05\n", - "training logisitic regression with C=1.00e-04\n", - "training logisitic regression with C=1.00e-03\n", - "training logisitic regression with C=1.00e-02\n", - "training logisitic regression with C=1.00e-01\n", - "training logisitic regression with C=1.00e+00\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "validation using image_09.jpg\n", - "training logisitic regression with C=1.00e-07\n", - "training logisitic regression with C=1.00e-06\n", - "training logisitic regression with C=1.00e-05\n", - "training logisitic regression with C=1.00e-04\n", - "training logisitic regression with C=1.00e-03\n", - "training logisitic regression with C=1.00e-02\n", - "training logisitic regression with C=1.00e-01\n", - "training logisitic regression with C=1.00e+00\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "cs = np.logspace(-7, 0, 8)\n", - "scores = np.zeros((n_images, len(cs)))\n", - "\n", - "for i in range(n_images):\n", - " # We use leave-one-out so train will be all but image i, val will be image i\n", - " print('validation using image_{:02d}.jpg'.format(i+1))\n", - " train_selection = image_index != float(i)\n", - " fold_x = xs[train_selection].numpy()\n", - " fold_y = (ys[train_selection] > 0).long().numpy()\n", - " val_x = xs[~train_selection].numpy()\n", - " val_y = (ys[~train_selection] > 0).long().numpy()\n", - "\n", - " plt.figure()\n", - " for j, c in enumerate(cs):\n", - " print(\"training logisitic regression with C={:.2e}\".format(c))\n", - " clf = LogisticRegression(random_state=0, C=c, max_iter=10000).fit(fold_x, fold_y)\n", - " output = clf.predict_proba(val_x)\n", - " precision, recall, thresholds = precision_recall_curve(val_y, output[:, 1])\n", - " s = average_precision_score(val_y, output[:, 1])\n", - " scores[i, j] = s\n", - " plt.plot(recall, precision, label='C={:.1e} AP={:.1f}'.format(c, s*100))\n", - "\n", - " plt.grid()\n", - " plt.xlabel('recall')\n", - " plt.title('image_{:02d}.jpg'.format(i+1))\n", - " plt.ylabel('precision')\n", - " plt.axis([0, 1, 0, 1])\n", - " plt.legend()\n", - " plt.show()\n" - ] - }, - { - "cell_type": "markdown", - "id": "c484eb00", - "metadata": {}, - "source": [ - "### Choosing the Best C\n", - "Now, let's have a look at which value of C works best on average. To this end we will plot the average mAP across all validation images." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "27512758", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(3, 2), dpi=300)\n", - "plt.rcParams.update({\n", - " \"xtick.labelsize\": 5,\n", - " \"ytick.labelsize\": 5,\n", - " \"axes.labelsize\": 5,\n", - "})\n", - "plt.plot(scores.mean(axis=0))\n", - "plt.xticks(np.arange(len(cs)), [\"{:.0e}\".format(c) for c in cs])\n", - "plt.xlabel('data fit C')\n", - "plt.ylabel('average AP')\n", - "plt.grid()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "582a8985", - "metadata": {}, - "source": [ - "### Retraining with the optimal regularization\n", - "Given the above, we seem to have a winner: C=0.1. \n", - "Let's now train a model using this optimal data-fit value. " - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "787d4f93", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n", - " This problem is unconstrained.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RUNNING THE L-BFGS-B CODE\n", - "\n", - " * * *\n", - "\n", - "Machine precision = 2.220D-16\n", - " N = 1025 M = 10\n", - "\n", - "At X0 0 variables are exactly at the bounds\n", - "\n", - "At iterate 0 f= 1.63610D+04 |proj g|= 1.28915D+04\n", - "\n", - "At iterate 1 f= 4.15681D+03 |proj g|= 4.49281D+03\n", - "\n", - "At iterate 2 f= 2.16198D+03 |proj g|= 1.74446D+03\n", - "\n", - "At iterate 3 f= 1.52275D+03 |proj g|= 9.40643D+02\n", - "\n", - "At iterate 4 f= 1.06750D+03 |proj g|= 4.39957D+02\n", - "\n", - "At iterate 5 f= 8.16646D+02 |proj g|= 2.06895D+02\n", - "\n", - "At iterate 6 f= 6.69236D+02 |proj g|= 8.85818D+01\n", - "\n", - "At iterate 7 f= 5.77333D+02 |proj g|= 3.17096D+01\n", - "\n", - "At iterate 8 f= 5.07922D+02 |proj g|= 1.96323D+01\n", - "\n", - "At iterate 9 f= 4.79121D+02 |proj g|= 5.20827D+01\n", - "\n", - "At iterate 10 f= 4.52817D+02 |proj g|= 1.09191D+01\n", - "\n", - "At iterate 11 f= 4.45991D+02 |proj g|= 1.66427D+01\n", - "\n", - "At iterate 12 f= 4.41202D+02 |proj g|= 1.31531D+01\n", - "\n", - "At iterate 13 f= 4.35648D+02 |proj g|= 1.24351D+01\n", - "\n", - "At iterate 14 f= 4.29196D+02 |proj g|= 5.76135D+00\n", - "\n", - "At iterate 15 f= 4.25971D+02 |proj g|= 3.49993D+00\n", - "\n", - "At iterate 16 f= 4.23993D+02 |proj g|= 2.49761D+00\n", - "\n", - "At iterate 17 f= 4.22913D+02 |proj g|= 2.49383D+00\n", - "\n", - "At iterate 18 f= 4.22219D+02 |proj g|= 1.98721D+00\n", - "\n", - "At iterate 19 f= 4.21861D+02 |proj g|= 1.37985D+00\n", - "\n", - "At iterate 20 f= 4.21594D+02 |proj g|= 7.61841D-01\n", - "\n", - "At iterate 21 f= 4.21326D+02 |proj g|= 9.69995D-01\n", - "\n", - "At iterate 22 f= 4.21234D+02 |proj g|= 2.03127D+00\n", - "\n", - "At iterate 23 f= 4.21179D+02 |proj g|= 1.19644D+00\n", - "\n", - "At iterate 24 f= 4.21161D+02 |proj g|= 4.29114D-01\n", - "\n", - "At iterate 25 f= 4.21151D+02 |proj g|= 4.36346D-01\n", - "\n", - "At iterate 26 f= 4.21138D+02 |proj g|= 2.85029D-01\n", - "\n", - "At iterate 27 f= 4.21130D+02 |proj g|= 1.21913D+00\n", - "\n", - "At iterate 28 f= 4.21120D+02 |proj g|= 5.29064D-01\n", - "\n", - "At iterate 29 f= 4.21117D+02 |proj g|= 4.09942D-01\n", - "\n", - "At iterate 30 f= 4.21112D+02 |proj g|= 3.30088D-01\n", - "\n", - "At iterate 31 f= 4.21104D+02 |proj g|= 3.18787D-01\n", - "\n", - "At iterate 32 f= 4.21097D+02 |proj g|= 6.06452D-01\n", - "\n", - "At iterate 33 f= 4.21087D+02 |proj g|= 2.24837D-01\n", - "\n", - "At iterate 34 f= 4.21080D+02 |proj g|= 5.55916D-01\n", - "\n", - "At iterate 35 f= 4.21073D+02 |proj g|= 7.76771D-01\n", - "\n", - "At iterate 36 f= 4.21066D+02 |proj g|= 5.15128D-01\n", - "\n", - "At iterate 37 f= 4.21058D+02 |proj g|= 5.87100D-01\n", - "\n", - "At iterate 38 f= 4.21050D+02 |proj g|= 5.07386D-01\n", - "\n", - "At iterate 39 f= 4.21038D+02 |proj g|= 7.09662D-01\n", - "\n", - "At iterate 40 f= 4.21035D+02 |proj g|= 9.93906D-01\n", - "\n", - "At iterate 41 f= 4.21023D+02 |proj g|= 1.72380D-01\n", - "\n", - "At iterate 42 f= 4.21020D+02 |proj g|= 1.42276D-01\n", - "\n", - "At iterate 43 f= 4.21015D+02 |proj g|= 3.09880D-01\n", - "\n", - "At iterate 44 f= 4.21008D+02 |proj g|= 2.93473D-01\n", - "\n", - "At iterate 45 f= 4.21006D+02 |proj g|= 4.29870D-01\n", - "\n", - "At iterate 46 f= 4.21002D+02 |proj g|= 1.67519D-01\n", - "\n", - "At iterate 47 f= 4.21002D+02 |proj g|= 1.17928D-01\n", - "\n", - "At iterate 48 f= 4.21001D+02 |proj g|= 8.71936D-02\n", - "\n", - "At iterate 49 f= 4.21001D+02 |proj g|= 8.00303D-02\n", - "\n", - "At iterate 50 f= 4.21000D+02 |proj g|= 3.38228D-02\n", - "\n", - "At iterate 51 f= 4.21000D+02 |proj g|= 1.31580D-01\n", - "\n", - "At iterate 52 f= 4.21000D+02 |proj g|= 1.35270D-02\n", - "\n", - "At iterate 53 f= 4.21000D+02 |proj g|= 9.32350D-03\n", - "\n", - "At iterate 54 f= 4.21000D+02 |proj g|= 3.37681D-02\n", - "\n", - "At iterate 55 f= 4.21000D+02 |proj g|= 3.88365D-02\n", - "\n", - "At iterate 56 f= 4.21000D+02 |proj g|= 7.63436D-03\n", - "\n", - "At iterate 57 f= 4.21000D+02 |proj g|= 5.06961D-03\n", - "\n", - "At iterate 58 f= 4.21000D+02 |proj g|= 6.09781D-03\n", - "\n", - "At iterate 59 f= 4.21000D+02 |proj g|= 1.29008D-02\n", - "\n", - "At iterate 60 f= 4.21000D+02 |proj g|= 9.76488D-03\n", - "\n", - "At iterate 61 f= 4.21000D+02 |proj g|= 5.60555D-03\n", - "\n", - "At iterate 62 f= 4.21000D+02 |proj g|= 2.08369D-03\n", - "\n", - "At iterate 63 f= 4.21000D+02 |proj g|= 6.59905D-03\n", - "\n", - "At iterate 64 f= 4.21000D+02 |proj g|= 9.45605D-03\n", - "\n", - " * * *\n", - "\n", - "Tit = total number of iterations\n", - "Tnf = total number of function evaluations\n", - "Tnint = total number of segments explored during Cauchy searches\n", - "Skip = number of BFGS updates skipped\n", - "Nact = number of active bounds at final generalized Cauchy point\n", - "Projg = norm of the final projected gradient\n", - "F = final function value\n", - "\n", - " * * *\n", - "\n", - " N Tit Tnf Tnint Skip Nact Projg F\n", - " 1025 64 67 1 0 0 9.456D-03 4.210D+02\n", - " F = 420.99984120700907 \n", - "\n", - "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.4s remaining: 0.0s\n", - "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.4s finished\n" - ] - } - ], - "source": [ - "clf = LogisticRegression(random_state=0, C=0.1, max_iter=100000, verbose=2).fit(xs.numpy(), (ys > 0).long().numpy())" - ] - }, - { - "cell_type": "markdown", - "id": "fdd5ec72", - "metadata": {}, - "source": [ - "### Test Images and Inference \n", - "\n", - "We have a classifier, now it is time to test it! We will predict the probability of patch being foreground given an image, and then process it with a 3x3 median filter to smooth it out." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "d17d189e", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "test_image_fpath = \"https://dl.fbaipublicfiles.com/dinov3/notebooks/foreground_segmentation/test_image.jpg\"\n", - "\n", - "def load_image_from_url(url: str) -> Image:\n", - " with urllib.request.urlopen(url) as f:\n", - " return Image.open(f).convert(\"RGB\")\n", - "\n", - "\n", - "test_image = load_image_from_url(test_image_fpath)\n", - "test_image_resized = resize_transform(test_image)\n", - "test_image_normalized = TF.normalize(test_image_resized, mean=IMAGENET_MEAN, std=IMAGENET_STD)\n", - "\n", - "with torch.inference_mode():\n", - " with torch.autocast(device_type='cuda', dtype=torch.float32):\n", - " feats = model.get_intermediate_layers(test_image_normalized.unsqueeze(0).cuda(), n=range(n_layers), reshape=True, norm=True)\n", - " x = feats[-1].squeeze().detach().cpu()\n", - " dim = x.shape[0]\n", - " x = x.view(dim, -1).permute(1, 0)\n", - "\n", - "h_patches, w_patches = [int(d / PATCH_SIZE) for d in test_image_resized.shape[1:]]\n", - "\n", - "fg_score = clf.predict_proba(x)[:, 1].reshape(h_patches, w_patches)\n", - "fg_score_mf = torch.from_numpy(signal.medfilt2d(fg_score, kernel_size=3))\n", - "\n", - "plt.figure(figsize=(9, 3), dpi=300)\n", - "plt.subplot(1, 3, 1)\n", - "plt.axis('off')\n", - "plt.imshow(test_image_resized.permute(1, 2, 0))\n", - "plt.title('input image')\n", - "plt.subplot(1, 3, 2)\n", - "plt.axis('off')\n", - "plt.imshow(fg_score)\n", - "plt.title('foreground score')\n", - "plt.subplot(1, 3, 3)\n", - "plt.axis('off')\n", - "plt.imshow(fg_score_mf)\n", - "plt.title('+ median filter')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "c5ee6bce", - "metadata": {}, - "source": [ - "### Saving the Model for Future Use\n", - "We are nearly done, let's just save a pickle with the classifier.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "a33e2980", - "metadata": {}, - "outputs": [], - "source": [ - "save_root = '.'\n", - "model_path = os.path.join(save_root, \"fg_classifier.pkl\")\n", - "with open(model_path, \"wb\") as f:\n", - " pickle.dump(clf, f)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/notebooks/pca.ipynb b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/notebooks/pca.ipynb deleted file mode 100644 index ed93e7dd7fbd61e0d70654e14ab63c487db03f61..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/notebooks/pca.ipynb +++ /dev/null @@ -1,405 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "6825e50e", - "metadata": {}, - "source": [ - "# Computing the PCA of a Foreground Object\n", - "We show in our paper many figures with object parts colored like rainbows. These visualizations are obtained by computing a PCA of patch features on the foreground object. This is what we will compute in this tutorial! Let's start by loading some pre-requisites." - ] - }, - { - "cell_type": "markdown", - "id": "b89fa7dd-f5c8-42b7-896c-8287d4e97105", - "metadata": {}, - "source": [ - "### Setup\n", - "\n", - "Let's start by loading some pre-requisites and checking the DINOv3 repository location:\n", - "- `local` if `DINOV3_LOCATION` environment variable was set to work with a local version of DINOv3 repository;\n", - "- `github` if the code should be loaded via torch hub." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "0ebd98e9-13eb-4e7a-a72b-27839dd463d0", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "DINOv3 location set to /private/home/vkhalidov/fairvit_dinov3_release/app/dinov3_release/\n" - ] - } - ], - "source": [ - "import pickle\n", - "import os\n", - "import urllib\n", - "\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from PIL import Image\n", - "\n", - "import torch\n", - "import torchvision.transforms.functional as TF\n", - "from sklearn.decomposition import PCA\n", - "from scipy import signal\n", - "\n", - "DINOV3_GITHUB_LOCATION = \"facebookresearch/dinov3\"\n", - "\n", - "if os.getenv(\"DINOV3_LOCATION\") is not None:\n", - " DINOV3_LOCATION = os.getenv(\"DINOV3_LOCATION\")\n", - "else:\n", - " DINOV3_LOCATION = DINOV3_GITHUB_LOCATION\n", - "\n", - "print(f\"DINOv3 location set to {DINOV3_LOCATION}\")" - ] - }, - { - "cell_type": "markdown", - "id": "d70b9a21", - "metadata": {}, - "source": [ - "### Model Loading\n", - "We load the DINOv3 ViT-L model. Feel free to try other DINOv3 models as well!" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "d3de7173", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DinoVisionTransformer(\n", - " (patch_embed): PatchEmbed(\n", - " (proj): Conv2d(3, 1024, kernel_size=(16, 16), stride=(16, 16))\n", - " (norm): Identity()\n", - " )\n", - " (rope_embed): RopePositionEmbedding()\n", - " (blocks): ModuleList(\n", - " (0-23): 24 x SelfAttentionBlock(\n", - " (norm1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n", - " (attn): SelfAttention(\n", - " (qkv): LinearKMaskedBias(in_features=1024, out_features=3072, bias=True)\n", - " (attn_drop): Dropout(p=0.0, inplace=False)\n", - " (proj): Linear(in_features=1024, out_features=1024, bias=True)\n", - " (proj_drop): Dropout(p=0.0, inplace=False)\n", - " )\n", - " (ls1): LayerScale()\n", - " (norm2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n", - " (mlp): Mlp(\n", - " (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n", - " (act): GELU(approximate='none')\n", - " (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n", - " (drop): Dropout(p=0.0, inplace=False)\n", - " )\n", - " (ls2): LayerScale()\n", - " )\n", - " )\n", - " (norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n", - " (head): Identity()\n", - ")" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# examples of available DINOv3 models:\n", - "MODEL_DINOV3_VITS = \"dinov3_vits16\"\n", - "MODEL_DINOV3_VITSP = \"dinov3_vits16plus\"\n", - "MODEL_DINOV3_VITB = \"dinov3_vitb16\"\n", - "MODEL_DINOV3_VITL = \"dinov3_vitl16\"\n", - "MODEL_DINOV3_VITHP = \"dinov3_vith16plus\"\n", - "MODEL_DINOV3_VIT7B = \"dinov3_vit7b16\"\n", - "\n", - "MODEL_NAME = MODEL_DINOV3_VITL\n", - "\n", - "model = torch.hub.load(\n", - " repo_or_dir=DINOV3_LOCATION,\n", - " model=MODEL_NAME,\n", - " source=\"local\" if DINOV3_LOCATION != DINOV3_GITHUB_LOCATION else \"github\",\n", - ")\n", - "model.cuda()" - ] - }, - { - "cell_type": "markdown", - "id": "f88f6c7a", - "metadata": {}, - "source": [ - "### Loading the Foreground Classifier from the Other Tutorial\n", - "For this tutorial, we use the classifier trained in the `foreground_segmentation` notebook. If you haven't already, have a look! Once you have trained your foreground / background classifier on patch features, you should be able to load it here. " - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "e4307e83", - "metadata": {}, - "outputs": [], - "source": [ - "save_root = '.'\n", - "model_path = os.path.join(save_root, \"fg_classifier.pkl\")\n", - "with open(model_path, 'rb') as file:\n", - " clf = pickle.load(file)" - ] - }, - { - "cell_type": "markdown", - "id": "b37d8361", - "metadata": {}, - "source": [ - "### Loading an Image and Applying the Right Transform\n", - "Let's load an image and process it in order to make it a multiple of the patch size." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "a069633a", - "metadata": {}, - "outputs": [], - "source": [ - "PATCH_SIZE = 16\n", - "IMAGE_SIZE = 768\n", - "\n", - "IMAGENET_MEAN = (0.485, 0.456, 0.406)\n", - "IMAGENET_STD = (0.229, 0.224, 0.225)\n", - "\n", - "image_uri = \"https://dl.fbaipublicfiles.com/dinov3/notebooks/pca/test_image.jpg\"\n", - "\n", - "def load_image_from_url(url: str) -> Image:\n", - " with urllib.request.urlopen(url) as f:\n", - " return Image.open(f).convert(\"RGB\")\n", - " \n", - "# image resize transform to dimensions divisible by patch size\n", - "def resize_transform(\n", - " mask_image: Image,\n", - " image_size: int = IMAGE_SIZE,\n", - " patch_size: int = PATCH_SIZE,\n", - ") -> torch.Tensor:\n", - " w, h = mask_image.size\n", - " h_patches = int(image_size / patch_size)\n", - " w_patches = int((w * image_size) / (h * patch_size))\n", - " return TF.to_tensor(TF.resize(mask_image, (h_patches * patch_size, w_patches * patch_size)))\n", - "\n", - "\n", - "image = load_image_from_url(image_uri)\n", - "image_resized = resize_transform(image)\n", - "image_resized_norm = TF.normalize(image_resized, mean=IMAGENET_MEAN, std=IMAGENET_STD)" - ] - }, - { - "cell_type": "markdown", - "id": "550fc889", - "metadata": {}, - "source": [ - "### Model Forward\n", - "Given the input image, we compute local features:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "2ec4daee", - "metadata": {}, - "outputs": [], - "source": [ - "MODEL_TO_NUM_LAYERS = {\n", - " MODEL_DINOV3_VITS: 12,\n", - " MODEL_DINOV3_VITSP: 12,\n", - " MODEL_DINOV3_VITB: 12,\n", - " MODEL_DINOV3_VITL: 24,\n", - " MODEL_DINOV3_VITHP: 32,\n", - " MODEL_DINOV3_VIT7B: 40,\n", - "}\n", - "\n", - "n_layers = MODEL_TO_NUM_LAYERS[MODEL_NAME]\n", - "\n", - "with torch.inference_mode():\n", - " with torch.autocast(device_type='cuda', dtype=torch.float32):\n", - " feats = model.get_intermediate_layers(image_resized_norm.unsqueeze(0).cuda(), n=range(n_layers), reshape=True, norm=True)\n", - " x = feats[-1].squeeze().detach().cpu()\n", - " dim = x.shape[0]\n", - " x = x.view(dim, -1).permute(1, 0)" - ] - }, - { - "cell_type": "markdown", - "id": "d49232f8", - "metadata": {}, - "source": [ - "### Computing Foreground Probability\n", - "Let's now pass all those features through our foreground classifier, extract probabilities, and reshape." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "aafcc322", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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Z9jttW7j3ve+97Hln48aN+D//5//0nnf+7u/+Dk9+8pNx7LHH4g1veAOWlpbw9re/HY94xCNw1VVXbTOj4corr0Rd11t93vnABz6AL3/5y/j2t7+91dyKo446Cm94wxtwxhln4NWvfjU2bNiAL3/5y3j729+O5z73uZifn9/uc74t/OxnP+s9l6yEq666Ct/+9reX5UwIDj30UPzjP/4jNm3atOx5RqHoYZUV2V85VrIIPfnJTyYA9LrXvS699stf/pJGoxEZY+h973tfev073/kOAaCzzz47vTaZTJZZda677joaDAZ0zjnnpNf+4i/+ggDQhz/84fTaeDyme93rXgSALr/8ciJie8yBBx5Ixx57bM8OubS0RPvtt99W7Sa3BbFs7LbbbvT4xz+e3vSmN9G3v/3tZdtdfvnlvfHMYjwe06GHHkp3uctdkhX2+uuvJ+ccvfa1r+1tK/aS2ddn8YpXvIIALLOFzGJb1twc22vNHQwGBIAA0IYNG+iv//qvl21z9dVXk3OOPvGJTxAR0dlnn/3fsuauhJtvvpn23HNPeuQjH9l7/aCDDqKjjz562fbf+ta3CAC94x3vWHF/27LmnnDCCel8TzvtNLrkkkvorLPOoqIo6GEPe9iKFtyqquh//+//veMnplAo7vCQ37sr/REccsghtOeee9LNN9+cXrv66qvJWkunn356ek3+/T311FN7x9je30HT6ZQ2bNhAhx12GDVNk7a78MILCUDv94v8Ltx///17ZQx1XdOee+5JBx98MI3H4/T6Rz/6UQJAr3zlK9NrRx555Iq/s2ZttNddd136dzm3Vf7jP/4jAaCPfOQjvbnae++9aePGjem1T37ykwTgNq25RNv/LPDKV76SANAHP/jBZe/J74k3v/nNBIDe+973pvfquqbDDz+cFhYWaNOmTb3zW7t2Ld144429fT3qUY+i+973vjSZTHr7f9jDHkYHHnjgbZ7PSpCxz8/P03HHHUevfe1r6corr1y2nYxra88KIQR67GMfSwsLC/Stb32LiIg2b95M69evX2Zv/tnPfkbr1q1b0fac413vehcBoG984xvL3ltaWqK73/3u9LKXvYyIuntw1ppLxFbY0WjU+z69/OUv3+axdxRf+MIXyBhzm7baF77whQSArrnmmhXfv/jiiwkAXXHFFTt1fIrfPNwhrbmCpz/96em/169fj9/+7d/G/Pw8Tj755PT6b//2b2P9+vU9++RgMEjWUe89br75ZiwsLOC3f/u38R//8R9pu49//OPYZ599cMIJJ6TXhsMhnvGMZ/TG8bWvfQ3f+9738KQnPQk333wzbrrpJtx0001YXFzEox71KHzhC1+4Xe1NLrjgArz1rW/Ffvvthw996EN40YtehHvf+9541KMetcxOtC08+9nPxje+8Q1ceumlyQr7wQ9+ECEEnHzyyWm8N910E/baay8ceOCBK6q+OW6++WYURbHV9LhfFS677DJ87GMfw1/8xV/g7ne/+4ohRM997nNx3HHHLSvA31kIIeC0007Dxo0b8Za3vKX33ng8xmAwWPaZ4XCY3t9RSMDAYYcdhve+9714whOegHPOOQevec1r8OUvfxmf+cxnln1mt912u90pwQqFQgGwHfZTn/pU7w8A/PSnP8XXvvY1POUpT+mpV/e73/1wzDHH4GMf+9iyfT3rWc/q/by9v4O++tWv4uabb8YznvEMFEVnAjvttNOw2267rTjuJz/5yRiNRunnr371q7jxxhvx7Gc/O/1bDADHH3887nWve+Gf//mfb8fsMP7gD/6gNw6xZsozh8zVk5/8ZKxbty5td8wxx+A+97nPdh1je58FLr30Utz//vdfUZE0xgAAPvaxj2GvvfbqhQyWZYnnPve52LJlCz7/+c/3PveEJzwBe+yxR/r5lltuwWc/+1mcfPLJ2Lx5c7puN998M4499lh873vf26HnE8GrX/1qXHzxxXjAAx6AT3ziE3j5y1+OQw89FA984AO3y2YqeM1rXoOPfvSjuPDCC9P8fupTn8LGjRtx6qmn9u415xwe8pCHbNfzDoAV77c///M/R9M0+P/+v//vNsd2j3vcA0cccQT+5m/+Bpdeeime9rSn4XWvex3e+ta3bvf5bQs33ngjnvSkJ2G//fbbZhu3EALe97734QEPeADufe97r7iNnKs+RyhuC3dYa+5wOOz94wgA69atw13vetf0D27++i9/+cv0cwgBf/VXf4W3ve1tuO6663r1dhs2bEj//cMf/hAHHHDAsv391m/9Vu/n733vewD4l9/WcOutt271l+bWYK3Fn/zJn+BP/uRPcPPNN+NLX/oS3vGOd+Cyyy7DKaecgi9+8Yu3uY93vvOduOCCC1KSWj5mIurFdufY1Yl024vf+Z3fAQAcd9xxeNzjHoeDDz4YCwsLOPPMMwFw65gvf/nLv9IeWM95znPw8Y9/HO95z3tw//vfv/feaDTCdDpd9pnJZJLe31HIZ2bTiZ/0pCfhZS97Gb785S8vq10homX3rUKhUOwIHvzgB+NBD3rQstd/+MMfAuCF3lnc+973xic+8QksLi727Iaz6bvb+ztIjjX7e7coiq3aKWePta3x3ute98K//Mu/rLif7cHd73733s/ye16eOeTYK53n7OL31rC9zwLf//738YQnPGGb+/rhD3+IAw88cFmOgxASGa9gdi6vvfZaEBHOOussnHXWWSse48Ybb8Q+++xzm+c1i1NPPRWnnnoqNm3ahCuuuAIXXnghLr74Yvze7/0evvnNb/YWEVbCxz/+cbz61a/Gy172st48yDPa0UcfveLnttd6StFSLrj++utx7rnn4rzzzrvNRfn3ve99eOYzn4n//M//TJbsk046CSEEvPSlL8Wpp57ae/7cUSwuLuKxj30sNm/ejH/5l3/Z5ng+//nP44YbbsALXvCCrW4j56rPEYrbwh2WiDrnduj1/B+Q173udTjrrLPwtKc9Da95zWuw++67w1qL5z//+bdLuZTPnHvuuTjkkENW3Oa/qxxu2LABJ5xwAk444QQcddRR+PznP48f/vCHy0J0cvzbv/0bnve85+HpT3/6ssjxEAKMMbjssstWnLPbGu+GDRvQti02b96MNWvW3L6T+m/igAMOwAMe8IBUHwwAL37xi/HEJz4RVVWlWg0JM/rxj3+Muq5vs1XKtvDqV78ab3vb2/Dnf/7n+KM/+qNl7++9994rrgb/9Kc/BYDbdWz5zGzIgdS/5Issgo0bN95mjYhCoVDsKswuwv13fwftyLF2BMaYZYQDwFYD4rbnmWNn4vY8C/x3sNJ1A4AXvehFOPbYY1f8zOyiwY5i7dq1OOaYY3DMMcegLEu8+93vxhVXXIEjjzxyq5+57rrrcNppp+GYY47Bn/3Zn6045r/7u79bMSAxV9pXghDEX/7yl7263le+8pXYZ599cNRRR6XnDcmO+MUvfoHrr78ed7/73WGtxdve9jY84AEPWFYXfMIJJ+DCCy/EVVddtc0wpG2hrmucdNJJ+PrXv45PfOITt9li6aKLLoK1dtnidg55rtDnCMVt4Q5LRP87uOSSS/A7v/M7+Nu//dve67MP7/vuuy+uueaaZerStdde2/uc9Flau3bt7f6HZEfwoAc9CJ///Ofx05/+dKu/fH7xi1/g93//93HIIYekNOEcBxxwAIgI++23H+55z3vu8Bjuda97AeB//O93v/vt8Od3FsbjcU+B/PGPf4yLL754WVIywMFK97///W93mux5552HV73qVXj+85+Pl770pStuc8ghh+Dyyy9fVuB/xRVXpPd3FIceeijOP//8ZQT3v/7rvwBgmTPghhtuQF3XW7XcKBQKxX8H8nvnu9/97rL3vvOd7+BOd7rTbYavbO/vIDnWtddemxwxANC2La6//vrt+v2Tj3dWFfvud7/b+z2622679Up5BLNK4fZC9i2q3Oyx/zuYfRY44IADbtMNtO++++LrX/86Qgg9VfQ73/lOb7xbw/777w+AFetd9bzz7ne/Oy3mroTxeIyTTjoJ69evx9///d8vU3vlGW3PPfe8XWPOn3fue9/7ptd/9KMf4dprr01zkuPZz342ACZ069evx89//vMVXXFN0wDg+/n2IISA008/HZ/5zGfwD//wD9sk6wAwnU5x6aWX4qijjtrmwvh1110Ha+3tej5U3LFwh64Rvb1wzi1brfzABz6w7EH/2GOPxQ033NCLJJ9MJjj//PN72x166KE44IAD8KY3vWlZw2CASeGO4mc/+1mKSc9R1zU+85nPwFq71VVH7z1OOeUU1HWNSy+9dMVY8JNOOgnOObz61a9eNhdElGoitobDDz8cANfe/KrRtu2Kqt+//du/4Rvf+EbPOvahD31o2Z8/+IM/AAC85z3vwf/9v//3do3h/e9/P5773OfitNNOw1/+5V9udbvf//3fh/cef/M3f5Nem06nuOCCC/CQhzxkhxNzAeBxj3scBoMBLrjggp5i/653vQsA1xrluPLKKwFgWSS7QqFQ7AzsvffeOOSQQ/Dud7+71z7rm9/8Jj75yU/iMY95zG3uY3t/Bz3oQQ/Chg0bcP755/ce1i+66KIVfy+shAc96EHYc8898Y53vKO3cHnZZZfh29/+No4//vj02gEHHIDvfOc7vd/bV1999e1uxZbP1a233ppe/9SnPrXi7/hZ7MizwBOe8ARcffXVK7aGkTl+zGMeg5/97Gd4//vfn95r2xZvectbsLCwcJtEZs8998RRRx2Fd77znSuSw9vzvLO0tISvfOUrK7532WWXAVjZVi141rOehf/8z//Ehz70oRXJ3rHHHou1a9fida97XSJ+OzLmQw89FFVVLXve+bM/+7NlzxuS0v+Sl7wEH/rQh9KCzD3veU9cddVV+M///M/ePoQ4394F/ec85zl4//vfj7e97W046aSTbnP7j33sY9i4cSNOO+20bW535ZVX4qCDDurVNSsUK0EV0duBxz72sTjnnHPw1Kc+FQ972MPwjW98AxdddNGyVa0//uM/xlvf+laceuqpeN7znoe9994bF110UapTEJXUWot3vetdOO6443DQQQfhqU99KvbZZx/ccMMNuPzyy7F27Vp85CMfSfs1xuDII49c1oc0x09+8hM8+MEPxtFHH41HPepR2GuvvXDjjTfi7//+73H11Vfj+c9//lYtE+94xzvw2c9+Fs961rOWFeHf+c53xjHHHIMDDjgAf/Znf4aXvexlKQp/zZo1uO666/ChD30Iz3zmM/GiF71oq+Pbf//9cfDBB+PTn/40nva0p/Xe+8IXvoAvfOELAPgf+MXFxWSVOeKII3DEEUekbd/61rdi48aNSd37yEc+gp/85CcA+B/YdevWYcuWLbjb3e6GP/iDP8BBBx2E+fl5fOMb38AFF1yAdevW9epUTjzxxGVjFQX0uOOO683Z5z73OfzO7/wOzj77bLzqVa/a6rn+27/9G04//XRs2LABj3rUo3DRRRf13n/Ywx6W7p2HPOQheOITn4iXvexluPHGG/Fbv/VbePe7343rr79+mQL/9a9/PS1yXHvttbj11lvTPN3//vfH7/3e7wEA9tprL7z85S/HK1/5Sjz60Y/GiSeeiKuvvhrnn38+Tj31VBx22GG9/X7qU5/C3e9+d23dolAofmU499xzcdxxx+Hwww/HGWeckdq3rFu3bpv/ngq293dQVVV41atehec85zk4+uijcfLJJ+P666/HhRdeuGKGw0ooyxJveMMb8NSnPhVHHnkkTj311NS+5R73uEevVu5pT3sa/vIv/xLHHnsszjjjDNx44414xzvegYMOOij1gtxRvP71r8fxxx+PRzziEXja056GW265BW95y1tw0EEHrbh4nWNHngVe/OIX45JLLsETn/hEPO1pT8Ohhx6KW265Bf/0T/+Ed7zjHbj//e+PZz7zmXjnO9+JpzzlKbjyyitxj3vcA5dccgm+9KUv4c1vfvN2ldqcd955eMQjHoH73ve+eMYznoH9998fP//5z/GVr3wFP/nJT3o92aWOd2ttTQAmog972MPw0Ic+FI9+9KNxt7vdDRs3bsSHP/xhfPGLX8SJJ5641d9n//zP/4z3vOc9eMITnoCvf/3r+PrXv57eW1hYwIknnoi1a9fi7W9/O/7oj/4ID3zgA3HKKadgjz32wI9+9CP88z//Mx7+8IdvMzBoOBzid3/3d/HpT386tY0D0OuHK5A2QIcddljveeTFL35x6mN65plnYsOGDfjoRz+Kyy67DE9/+tN76uSrXvUqvPrVr8bll1++Yk9bwZvf/Ga87W1vw+GHH465uTm8973v7b3/+Mc/fpkz4aKLLsJgMNhmLXHTNPj85z+fVF2FYpvY1TG9uxpba98yPz+/bNsjjzySDjrooGWv77vvvnT88cennyeTCb3whS+kvffem0ajET384Q+nr3zlKyvGtv/gBz+g448/nkajEe2xxx70whe+kC699FICQP/6r//a2/aqq66ik046iTZs2ECDwYD23XdfOvnkk+kzn/lM2mbz5s0EgE455ZRtnvemTZvor/7qr+jYY4+lu971rlSWJa1Zs4YOP/xwOv/883stO2bbt0hc/kp/Zs/v0ksvpUc84hE0Pz9P8/PzdK973Yv+5E/+hL773e9uc3xERH/5l39JCwsLvZj82zp+3kaHiK/N1ra97rrriIjj+5/3vOfR/e53P1q7di2VZUn77rsvnXHGGWmbbWFr7Vs+8pGPbLOlimBbrQywQoz8eDymF73oRbTXXnvRYDCgww47jD7+8Y/v0H6f/OQn97YNIdBb3vIWuuc970llWdLd7nY3esUrXkF1Xfe2897T3nvvTa94xStuc14UCoViJaz0e3clfPrTn6aHP/zhNBqNaO3atfR7v/d7y9pB3Fb7rO39HfTXf/3XtO+++9JgMKAHP/jB9KUvfYkOPfRQevSjH5222VbrDCKi97///fSABzyABoMB7b777nTaaafRT37yk2Xbvfe976X999+fqqqiQw45hD7xiU9stX3Lueeeu+zzK/2uu/TSS+ne9743DQYDus997kMf/OAHl+1zJezIswARtxY788wzaZ999qGqquiud70rPfnJT6abbropbfPzn/+cnvrUp9Kd7nQnqqqK7nvf+y77Pbat8yMi+v73v0+nn3467bXXXlSWJe2zzz702Mc+li655JLedne6053ooQ996DbPsWkaOv/88+nEE09M13hubo4e8IAH0LnnnkvT6XTZuGS82/o9Oju3l19+OR177LG0bt06Gg6HdMABB9BTnvIU+upXv7rN8RERffCDHyRjDP3oRz/a5nbbugevuOIKOu6449Kc3fOe96TXvva1vbZERNxaxRizYoueHNLK8LaeoQS33norDYdDOumkk7a538suu4wA0Pe+971tbqdQEBEZol9RRbxiq3jzm9+MF7zgBfjJT36yw8lwH/vYx/DYxz4WV199da/W4NcRt956K/bff3+88Y1vxBlnnLHaw9lhvOQlL8Hf//3f49prr12x5cqvIz784Q/jSU96Er7//e9j7733Xu3hKBQKxa8EIQTsscceOOmkk5aVyyj+Z+Caa67BQQcdhI9+9KM9+/OvI7z3uM997oOTTz452W9/VXjwgx+MfffdFx/4wAd+pcfZGk488UQYY1a0eCsUs9Aa0V8xZvs+TiYTvPOd78SBBx54u+LJL7/8cpxyyim/9iQU4LY4L3nJS3DuueferrTh1cbll1+Os8466zeGhALAG97wBpx55plKQhUKxW8MJpPJsjrS97znPbjlllu2aV1UrC4uv/xyHH744b/2JBTgbJFzzjkH55133m3aqf872LRpE66++uqeBXhX4tvf/jY++tGP/srJtuI3B6qI/opx3HHH4e53vzsOOeQQ3HrrrXjve9+Lb33rW7jooovwpCc9abWHp1AoFArFbzQ+97nP4QUveAGe+MQnYsOGDfiP//gP/O3f/i3ufe9748orr1wxkE+hUCgUv3poWNGvGMceeyze9a534aKLLkrWjPe9730piVWhUCgUCsWvDve4xz1wt7vdDX/913+NW265BbvvvjtOP/10/Pmf/7mSUIVCoVhFqCKqUCgUCoVCoVAoFIpdCq0RVSgUCoVCoVAoFArFLoUSUYVCoVAoFAqFQqFQ7FIoEVUoFAqFQqFQKBQKxS6FElGFQqFQKBQKhUKhUOxSKBFVKBQKhUKhUCgUCsUuhRJRhUKhUCgUCoVCoVDsUigRVSgUCoVCoVAoFArFLoUSUYVCoVAoFAqFQqFQ7FIoEVUoFAqFQqFQKBQKxS6FElGFQqFQKBQKhUKhUOxSKBFVKBQKhUKhUCgUCsUuhRJRhUKhUCgUCoVCoVDsUigRVSgUCoVCoVAoFArFLoUSUYVCoVAoFAqFQqFQ7FIoEVUoFAqFQqFQKBQKxS6FElGFQqFQKBQKhUKhUOxSKBFVKBQKhUKhUCgUCsUuhRJRhUKhUCgUCoVCoVDsUhSrPQCFQqFQKBQKhUKh2BYmkwm+//3v75JjHXDAARgOh7vkWHdkKBFVKBQKhUKhUCgU/6Px/e9/HwcffPAuOdY3v/lNHHTQQbvkWHdkrBoR/cQFfwkAqKoK3nsAABHx3waAMQghwBgD4wr+25j46QBrbfxMQNs2AAhFUQBkYY0FEaFwDgiUjhlC9zkAcBbdPuNfngjTpkY5HMI6hxBMHBBgDEAgWBDgeV/GGBB5BApxnxaWXBovv08govRzCCGdW/4eQCirAiEEngsLgAiGAGsMrDPw3oOIUJYVKPBn+ZwsQvAAAgJ1Y+PjeYQQABCcc7C2SOdNIAQieO/5M9bCWAciA2scKARYg3QcmcP0eaK4b8CAZBrBl5KPb62FtQZt26AsSxARPHkYa9LPIbQIgeehKBwA281L3JfcJ4ECjAGssWh9i7atYYyBcy6NS47r7ADeB8hFtkWJ6XSKwWAAay3atgUAWGdB1AIgGAsE72FtCQMXbw4Tz6HFeDyGKwuUZYmqqgAA9WSKoih4DESo6zHfL2RBgeepKApYaxDIw3ufxug9/zwcDkEBMPH+JRAAHrtsBwDOOYQQ0PgGxsp9ZgFTpG3luyRzYeABENq2hW89jCmz74SDcxZAAIHvE/me8L6B6XQK7z1cUcDE95q6QeEGcNbxtbIGiPe0MQZkDAiU7nELg+D5XvQhoBxU6Xod9MjHQ6FQ/GbgGPvE1R6CQqH4H4JPhQ+s9hAU/4OxakS0KDrC5b1PJMJ7D0TikoiU90B8IOYHbEJZlukh1lqHEDwoANZ25I6IiZEcpyOv/JAeAkUCAhgCjLOgSKpkXBQMABsf7pmMYmafzjkgMEHygYBIBJ1ziagRMQkEMvLbG0tAURQ9cmpNJM0UQAR4HxLpC8EDZNI4bSLVFgYdQQ0hRCIW0jHl8LNkxVqLEOesKBy8R5o/OQeTEY0cxhhQto1shziWECzKskwEvCxLBAqo6zqSzwLOyWcM6rqGtfwZAGhb3823KWCMRVE4EAEtWhgYWMvkUMg6z5lHiIsRxjCZE/Ir9yFfZyZSRIR6WoOIMBxWkQQjjasoClRVhQBC0zRMKAH41nfkP16/pmkAsnCOCSvfQwEhLmIIuXTOpfvZFg7ByzzyOkoIfmZOI0G02eKMQe/95eDtvPcIBFh01yndcyAQhTiv+SKHgbUO3gcYYxECgUJAWVUwwXb3Bix8aHlBKLsv0r3uQ5rvEPe99fEqFAqFQqFQKH6TsWpEdDKZZGqZ7ZHEgI68wQA+iHpkQIRE6IRsOFfAuSI+DHcqjPceznRKUl9VBcgaEJiAGiApUz3iakTVi2Q3EgR5eG/bFtZWGAxGmE6nnYob3xcSN6skAssJsrX8nnOOlatIcmw8VyGFHWGdnVUm6yAkIpETl9mH/kRAQrdt27awxKQNxAqXNTaRhrRYIHOYHYfV006RJATeNr1WoSiKNA5RJNO5EJ9f23r4wEolE7UCBtnCRCAQedR1JKfGwTnHylwAnC1YFTYWRAZtWwMAqkGJ1nf3hpC/tKBhCjS+BhFQllV2HbttjDEYzY2wuLSEpmkwHA4RQkjXrCgKWGNRFCUAQgimN2fee0ynU4xGo3St5V7gMbCazPcRAENpQUPmTtTUoijQtm1SG41beZHDe4+q5K96WQ4RfEhzzosYcm42LXZ0izwWbevRNB6sUhs4WwCWFysM2US6KVuwcM4hgFdurLWgQJjWTbbo4BAAJusKhUKhUCgUO4hLL9gLB+xX7pR9ff+6Bk946s92yr4U249VI6JG7JreJ2I1rVmJMi7aaxGSCumcjfZBtkl2FldKVkXvQ3z45WPYooBvPdpI3hCtgiaSKjKI9kHWEUOgRLzAwiYSQxLbMBFgxJIbYKLlNBBFiyLYVkwUbYg+7og/27QNKCp0zjkmCVXFBCN4mMgVy7KM1kwm1yCCDy2sc5EcWz6274icMWyzFbqZW4CFxAMm2VHTPMTTs9aiLAq4oorEiBXi1vukog4GQxSF66mkMv/G2qhaBwQhmy4qaERovYcrXLo+sojAaiqfv7UWRVnAkeP5RbwexqJpajbKGv6wsQZt7dO94L2BtTyvhSvhfRuJqOfFiWgenp+fT2pmUrTBqmvwBAMmtG3jeU4DRWIpExst2JFIF3IOFNC0DVy01lbVMBI7IBChraes5JZlukaICyshLlq0Uc1NBJV8WmDh62DRRpW3qGwi+YEINlmZu8UNIXoU1XNnCzjLizvNtAYM0rUqnOWxBl70cM5Fq7DDaFQm0suqaEgE3QDJWVAVHbFmk29Hao0x8Ok+4RWTtOCkUCgUCoVCsQM4YL8SB/12tdrDUPw3sGpE1Bauq9c0QOs9at/COgvfNDCmq6HrbI19O6HUHsrrRVGAIilhZdXAliUGonyBMmJlYMmCYNA2TMzKsgRFhaitPYylSJIBYwFLBnUd4KJN0RUWAMH7FqH1sCXXHfoQQBTVTgMYw3V4ACXbJcC2ULExim0zt+qKisqKlUFRdLZTHwKsLVCUVSSWJinI7NPEjIWWxxoCIYDHZ8iyggcXKyEtK5ohwBAB8LEeM1dM665GsWnRtkyk2e7KKmjwkcVTVLud7Vl/y7JE23oABs5KPS0gxARAssl67/k6WgtYB08EFxVDagmDwSCpxzw+DyKDqirgXInGE1ycIx8o1Xm6glXU6XTaU9gRLb5crwoAIb5PsLazWruiQBXvGYFYTut6gsIVKMoCII+mmabrzcqpQSAm33xfmriAYFBUZVSF26SWCzGlaN11xgDeYzKdYjgcpPulabj21XsP8gYu2t+LouBFDQN4YpW6bVsYa1JdcAgerW/hiWACE3ZRjZOlHBY+NKibJQwGw6h68+1GYGu6qOtFUcT9u6QWG2fT/U4AKFBPIVcoFAqFQqHYXgQK8BRue8Pt3Jdi12PViKgQDiKCbz2Cb5OSI3VonWW3sxuK/RFAZtPsHmhDVsdoLROrFKgTCaJzrmcJnA0OKooCg6JMttymafpWxaaFNYCzDoHE2mhjCM4oqoKIYTlMtkBs17S2xWQyicE1thsb+OG8iCQvt/V2ITwGxjgAAd5zwI/UOxIoKYWsoHXzIEh1s9YAcGxjps4eLaoqRbIi5HHW3ivzYQx6tbrMSKSm0PL8ZNdHiHTT+BSSIyTMORuvmdhNxQbNCwxCYIkIFBVdIsJ0WsfxsxpdFDyeum4iQS6ifZoXParhAE3bYuAGMNZgNBqhrplcyz3ksmsgx5H7hcOeLKxBmje5R+R9Aw5SEkIpkPrIuq5RliWapsFoNErXRRTWRDxzFR7sCpBFmLIsMRhU2T3C6vVkMonnUi6z8+b3FBGTeGNYMeZjN1ybnRHupmnS2Pi7woR1QRYeiO+7LriLkg25W5Sh3vddrutszbZCoVAoFArF9oIjFnfOM8TO2o9ix7Cq7VuE7HApGbO3pmlQVUMQ+fTATcTqGdB/+O9IQ7x5MltieuAGEnGi4FMNo9hi5YFY6viEpJbVINkeixiAk2ycllDaEnUzyRRZCfwxIOqTNwML6xyMcSjLLsRIyIEkvrZtm+oZc+U31Zv6LIU0BFaP47hCrH2EkbrQjjwAiCQzWpqjHVfUOIoKmHUWIP5Sd/WY/ZAimTshkjYqmhw+RYkUheCj6inJumx5dfHaFI5JmY/WzZbkXmBFlQCU5YDnE0yAcgW9uweAtgmYn5+PQrAkJndKo1i/rXNwZYE2eDTkUcDCGVbcx+MxQggcRJRZmmURQM5ZbLQ5SczvDanlNdECnZOs6ZSTdYW8C0kUoieflz/5z6mmU2zVxqQ0YGNsDJgq+rbr0JHbuq7T4ocxBpPJBJPJBIPBIBLOOs01pzTbdO7y3eAFD2BQDaK1mcOL2DLfpfXKd6itAygLFrPWJtI/G+alUCgUCoVCobhjYdWIaOu53pEiYSqKIUzw8cG2QFUOYuqogXNlInxM1jp1B8aBwOqktNLg2jWHpg0g30ZVrEkWV8T2MNZ1p+8Kx3WH8YG59S2cK7qHZApopd4uEGxRcJJo8DCmiPszaNoWgUJUSwOa1sObgKpiJRPgDCAJSUIkE03TsJUTTByEBOSEiMBKsbMFJwkTwbchzqeHsZbPI9pwCQbGikJqYicbrvGTACYDUbFCVHENDIpE3hCTVHn+YiiQs5Fk8nshELwXdZr3wTWVJpFRY2ymvHnAcOpq0zQI3qMsuS1I0zSAtXBRka7bFj608L5BVfE+RWW11qCsSpBpQZbb7BDAdYiRQLWhgTEOg+GQFyxi2I6JdLGNixOuLNCMx+iapnDhLHlRFQGKCiFbXUPXTiZT77338K0HRUus2JHrpoY1FuVggMIUaH0LAlt0Wey28Dx5rKADSd230UY8mXYWX55TaaET7yfj4CzfSyHUMNZiMBiiLDmEyMX73fsWRVFiMBjAuQLTmBJsjENVlonwMqEvuGVLsrtL6JaFMaxk9xc8ugWQIlqaEQht08CVXdsgJr9sEYYqogqFQqFQKHYQIf5vZ+1LseuxikQ0oHJFT62S/oRMjjqbpShq/ODfKSuGZUgUjpWgpm1jLSYSySqKEsZKGxF+mG59QFEYJjuI6hoQ26SI4kgIpvs5hBZt2/LDe+lSGmxZVkkpE1jqLJYmhh/VTd1TmPhEZ4hmCIBZ3hoFiJZGE1uMgIOYgg9MRr0HJeVUEm9FaYqkLMaxSq/OfAygTv0EOiWMj8vKZ1W5mCocLbWGa1dD8LHVSmfDbduQqW+x5YcPIONTfaIrC9QNq3TUEprMwiqhNnCOCT8ZDAZll35sbVJWEUObRCUNsQ3L4uIiiLgva1HG9GPLKmlZlLFGEmiaNllri7JM+xY77rSu4aOCmyuYrEyzXbaupz2V3higGgyS4ueKCs7FfqPTNtqIRW1FrDXualQhNvHMJiLW5Lqueyp1fq8YiM3WwTqHpvWpDQ6nSiMppsPhKI13NBohBB9VT6Qa2M7uzr1pnXOo7CCpvKJ05opmL1U5cyewBbizHRtj4FteIHHFqhozFAqFQqFQKBSrgFV7AnS2gPcc5mMdMB6PE3kBkHpoSlhOXsOZAliIYCIBHAwGSblqmiamr5ZACKntirwu9ksJYwHYJizptnldZB4exCE7bd+GmVlYhRR0Kb4+thDpQmdyG6dYh0Gdwtf6FuPxGMPhMJFbsRDnihIAtIFVybptYNCFE1lrYZJK1ilQHYFBOjc+r0GqaZQWNumYhQM8Xw9rWAHmNF1CE5VM51hlrSqx7PJCQZvVctrCxWvOKck2WlzzulMAWFhYiOnGWcsU0S+jwuZNm/Y9nU5j8vAg7g9RYc9II4XUliTVDkf1Va4rgBSKJarnYMBBQNKSRUgdhw6V4EAtoKqQFjmMMQhZCxSuyXWpbjNQSL1JvfdofYN22qQaVbl/5H3vm979mIf7yBwURRGvf2ddb33oWXuFKLOjgMm31IhKSJTcx6x695ORZTy5TVvutZSabEz6TFKHvU8/W2NSmJh8v2V8CoVCoVAoFDuCQGA32U7al2LXY/VSc608dBfJ3ictNQxsVNzkIdf3iJ8gEKE0FkvjcXoYLssqs+gSQpDkV9MjjVVVoa7bOAYHH8OShJwZANSiR4ABJitCRvMAo2UP3ZZb0HDZIyUCJoE1oiK5+FlWElllHY3mQKBECiSUybfRzhxVJWsNmpbDY0xsAyLkgtU/wIcGNp63cw71tI5KXp/o5YTDGJNaysCYGPiEeE2Yr1vrQB7wbUDhup6bbVv3g6PAyllRFBAOxunGgDUGdd3VxErtIB8XPXLPKhsrhtNICNmqS5hOiRVy2KSwl2UZVTzL/1DFFjSiyjHpNCjLIQBgfn4+nb9N91KZyGEbQ3vSfRXJJgcPFb37RHrS8kIDwTqKwT2xNhdSw9ztLydjXS/bLqxKtmuaJtU3y7y1bQsKlFRivn5lPI/sno7XVepTm6aJ4V82vS4BRXI9JSwsT3SWseTbJwLctuneDiGkVGMigidKNdrSLzYE6iUPKxQKhUKhUCjuGFi91FzEdg9tgxIFyAcUrkQbVcRef80sVGgyncbgn5ho2zZZPSPSwzGTWxd7fBoMhyPAsCrDD/ZdQBB/zoJMbH9CHWkVBVFIkiia3nuMRqOYAtuk41prgUAghNRfMoAJxXQ6TYqvc47V0sxmCeO4flTUL1fAitWRAnzTgnxIpMY5B1c5DKoBJ7VmtXpiiXSWbboWFkXhgOBQN1OQ7QKNvG/ZzosufEfGSPBRwLOxrtXFPqkBxhkY6ghtiGRbPk8wGAyHcEUMlLIG1jiETLUWciREXUgMt3wRAmq4RU5cQJC6RWs4FIeCgXHdYsAg2mIdHFzBNcQdGZOa1zopf3Lt2uBhnYOPCxc+En7ftGk+27aNluwyknmKNZccQhRCbGECSiTbkkFVuUjmDabTCX++MDDBpoUYUSq7RQ7Tu+fkmsj3oXBFsmY7a1G6OIZo621bVpknkwkAdg2wnbZIxL4sHaT2Wr4/ucIZlyYQyMRwKm47Q4HvGaKQvn/iVJhMJj3XQRqzD4AFTOBFCgnD1qwihUKhUCgUOwpNzf31x+oR0dgiJISAMqpOHCYD+JiQ2lkKbXr4HlQV2uzJla2SXf0g1zQW6SG4ieolq4gFDPoP3R2BNAixr2WwIap/LpLMLhU0pYdmlkdBshYHCWAq0ja5Upon0OZ2Ywlv4rkICIZfk8/0LJGOSWtSMrNaPZkrVppiH9L48G+dg6OudQwRk0q7gqVV5kjIXlGUQCyF7dRUpPPwwUPa43AyboWiqHjeCw7JkdreIl3zkCyvMpfGWoSkKHJirsw/AIxGcz2bbVEUGA6HCCFgaWkJ0+kURBwyBNtXrUVRlM8l+zA4RErmTSy9kLJNdAsNwXu0MTwrV99lv0LiuvulI+uiDqZrbrsWPslSHokdBz11bU/k88lyGyQ11/CCQ5xPCFnM7lO5jrlVO783c7IrY2dbsU2WX+89LNge3SU/h9RWRj4/GAxS25euxjukRQofg7WapoazJc+zQqFQKBQKheIOhVUjokVRJJur9wEWSA/mYu/rLLAAwDZOAqtCw6hG1nUDwKT6TVa6bFJ2pD0FP0THsJ+oIsmDP9djdqoqq7FFUrmk9UVqNwNRtqadChrH5ZwDRdumHFcexp1zqOsagwEHvlhjQM72iKbUenIdogOoqzlFnCPvPfdnNB35lXOS4+YqMSHWk8bnfedKlPF9JkohZuR0xFzUyUBtJJVlUsjy2lhJkmU7cN7z0qGNPTONaVKf0KZpUshN3itTLKZpDPFcqsEAxliYoiNQ0+m0Z7WWz0r9r7QIISK0TQtjOxI5HA7TcfL6XwCwZdE/NwBNXYM8K7FVVSUCXTc1XLS3ymty7+TXk+8bSmRbjjs7BpkPuSbS17Msix6By+3aeR/OpmnhrIVx3OdW9ilKs/yct0+R886VaKBrjWMjCQ3gFN26rgEDDIdDIKYI57b0MrY5ml18cc5hMplk8+HTPU4BvflTKBQKhUKh2B6EzLe3M/al2PVYVSIKGLBoZZLiJH0f5QG9aRoQWHUpioLDXrJ6vCJZXAmDqkIgBwNWi8gaFM6hoYDgWxhnUddN2v/c3By8b2P6K0Xls1OrxNrIFtFOfQK6UJi8VjSpTL26RkZueZX6QyYcHRmD4TY0sl8XA3JihWBsESNpsTOJqcbCWK4TtERwlq2ggVhldkUBCoHDhgzX/XFf0wKtb+I+kXpk5q1J2CLK7WhCS9HazK/nwToyL0y+LVxp4QPPYdOiF9KTVNOMJE1jexLftkBsA4MQYOI+JdAnBK4tzOc+V1iTLVmIbgAIrC4LwbemC5iSGsUAivcio20atG0DA4NpPYWzXbJzET+TW2Xbtk3XVYgdky+XFMmyLNM2eUBRUtcNMB5Poq23C3IS5Z9rOYVESsiTQVFwMrGvW64vpq43qdSD5uRVSGE/xMrAWBuvNQGG+8Fyax9Wf33wCD6gcA7GsmU69eklgm89rOPkYTm+HEv+O7kBAqFuW7RNvbV/JhQKhUKhUChWhFpzf/2xetbcwLWXIRCM4/YbFPt7skU3D84Zom1rtn3GB2WEAGcMAhF808CDW2lYZxI5GC8upgd9sW4KyeK/xaoYbZjRxsr/zUFHuerGtYo+ERhRm2aTROVezuv++JxDT8WSUCGKZMAQUGbWTjIArMGknvYIm8mUszQ2a1FGdTSpbTwKno+2hZTlGZOF7hD33bTOIoDJn4Prxk1s85SEWSZLJrVDSQE/1gImAOA6UO8pERKKNYvGdsQ5r/3Nbc5CsouoBI7H4x6hAV+taHFlkicqsajoPbU12pbLsoQPAU1Tx4CeAkVZJqXWGANLiPW9sdVPyEOuLGC7cxZlT/YlYxPFUc6Rz8v0LM9iv81tvLL4UQefyK73bbp+bCHmBQHpFGSsQVGWHDqUtfKx1qIqKw6sioRb7tXZFkJyHSSsiGuaOZzKB0r1vECsa4ZB6VzP5psvCJTxGBI4JeeWW6+FANsQYBBAoWvdo1AoFAqFQqG4Y2DViKgoRNZ2oULywEpEqebNWhuJXbd9VXVKZFUWaJo21ho2KEqbyAERYTqdpjYcEozTqXhNIlhFUSIETq4VuyX3Xuzsm3ld32xtn5AR733KgM7VJ/k5r5XL50LIca7oyXHyFjM2kZSuzcpgMICJtbW5vTVv78GpxC2vHbUesAROLjaJsHBNoENR8txKGxmppZSWLLmalpM+AtcwWhPb2wCJRMmc59bMvN5Rznl2fuVYeY1koDYRPEmFnU0tljkQy7a8nxJcvecgI+qs2vkiAV9voKoG6bXZGmFjTLJ+55bt2TYnk8k02WHlHPLtBUwWHfeLLUvw+kI+vi5ESGyzQtIpdJbooii4zjZrKTNroRXMzi0r61lP3Ez1t9bCtw3G45rb5cRk31QrSwTRk+W4cg/K9RQLdX6v598FhUKhUCgUiu1BINqJ7VtUEV0NrKIiGtKDKT9kd8qJMf2aOX5QlodltiIm0uCiElOWTGaI00CldURd16muUJI85QEaoI48gsN42tYnkux999AvVk950O/ISj/wxRiD0hWJvOV1l3JOK9UH5qpYXr+XWzxnayplHIn8ZvuQ95xzXDNIrEBHZzNa38ARhxXZmJzKAUkeTV7Dl5FBIWv5+HIiJaTEOp7LAA4wkjmcTCY9ZczHGlJRCHPFWs5XkmgBpBTaQGzXNdE6mrcbcTNq8Wy9sYxZlOMifk4WOfLQoaKo4j3Xxv12JFL2JWqhXD+5vkKuiqLsjUs+W9c1iLrWJal2tGnRNg2qokBZVkm158/zOXd23o5otr6BNTZTZ7ven3KNmqbJeod2x+yCvgicAuxjW6WO+KYUXFGLidvhlGXVWcsziINA6lLzhRq51+S+ygO/FAqFQqFQKBR3DKwaEW2aKdq2xXA4ZOIJIHhCiDV5ZGxXS2bArVdcCWMLILbGYJLio/2SbbmF7R7s8zpOtqf6GI/K9aAU26rk9lBRT0UF6kJcomUYgLXE9mB0bVZYCTQIPqANDUrnYMH1jiRjMZZtl5G7zQbXDIej+NBegZufEJqYSkpACsdJFs+Y8is2SJ+RQ3HBmmiLRSTzFDwCm3ATwWjaFkCAMzal6wqEPAhJlF6oXSJrgA8tQKwgWuPgigIeHdHw3oOildpQrAEFYVAWqJsabVPDZWpy3lKFFXGueXWFJP06NE2Ltp7y2IgXHXiOI9FuWk7p9R6TyYQDokBwRWzdUzg0bZuCj4T0dosCSASWiK+FnC/PQwljOa02tB5kszpPMOkHgLqeoCgGPSUTQFpUECKerqkzKKyB96LKu1RP6lwR7zukBRUT045dUcAA3FNWVFKipETLfwshJJIFBJPOO5HDqLz6tkn3TDOdwBUutgOyCIFiH1ek7wu3GeoWSoi4VruwDoVzqW5VQqVYLZ6kMSoUCoVCoVBsLwJSDudO2Zdi12PViGhdczDNZDJGVZbc61IUqaLqagINuOUIgLqJ9X+ZqibEgEOHrDwHQ0JechtlkJAceViGhbWZvZHYmkvRCmxj4I+QAVFZRdVxLoa9eFYcIVZJY2HAfR4pRKUxtgKh+N9CDHLSxYFMZeo3WhYOIELwHG5EyBTIWP9owPbKNj70S7sRRNIURCkNgetDwSEx1hm4aD+W+UK2fyElMjZRhEVd7uyvXJvLimMBawsEkrYfWZKv93Cxb6o1JtVhOmsRjIeJ/ytiIrL0UyUiBO+Z0IdOOePz4H0NRnNdLScI9XSS1NuyYLJkreVWOK5Lrs0XG3KLqhy3C+EJ8J5i0rDcl9z2pq0bBB+AwqZUZFbWO0Vdjj87t7maLgsSBdlkKc6t3Llqzj1VWb0MoVMXvY/ZcZniKK8755KqyePovivcHqntnbvcY2J/l3NAXAjgFjfcWidXVSVga9Z63DZt6s+auwmk/Y1CoVAoFAqF4o6FVSOieQsK5xymsb0Dq3keHp1l0hSdWmWN4VCiqMC0bZvaoUggC9dDNsmWmIJ2ehZYfmgXm2PTNEmlAbpkVyHHuX0wV0opEsXUZ9FaFNYlS2hVVcm+aS231/ChIyQSbiM9MIuiwnA45LGjn8pqcgIOTpSVc3NlBWe6gJicoDhXwBUl91AlnwhB8B7Bsi1ZrNFy/vn1EYtrHs4k526dQwhtUmWtZfU2EBM3+VxZlrBYbkcGuO9k8Pw6J8q2QFabaGB69Y5CboWg9ZJybZ/EAV1dqrMWgboemLnlWvaTE9TBYNBZcTNrtDEGbfCxHpLV1WCQtS5xsX+q1K52dcS5JVXmUwgi3+NZkFRcAJCfc5tt/j2Svqe51TYtvkTLuPzMc+Nij9Ll30m5F4UYy/XKxy625lzJlDlayaYr45fjy8/yPZM6UoVCoVAoFIrthd+J7Vt21n4UO4ZVbeCX14DOz81jMp1kKlBXmxlqqbcr2CbYtBgOByjLKj3YNk3DSp9hohZCQFkU6bYKITAZiwSodFzDKIQRYELkAwc4F7HWT4hJToIkDCk9mDsHCr4XXGOIyVvTNIlMCrm0rgtiErLBpMqmmrqcpIcQMJ5MAGsSWfDBw2b7cEQYlBW4tUlIMdRCEAEO93FxrLJfUfBmSUtO1mSsOdk2BmwBjbWaxpQwiPbMmJbrfSQrsRbSx1ApaVMi10UIXVJAA8HHOZZtbRZ2lJNGSZhlImQB8onocR0mB1ElRTIjaskOG8+riLbdnCRJyxUJrkptbSgmBDsL8p5V4OyaSV/apaWldB1EYZUAJbkGXdJvt9ghY8rbrlDg3rHy3ZHFBqlvBdALqJL7VeZZ5gnw6V4TXpvPqXxexp0vHnT3hPQ7NV0dL3FYUR62lSvCjW97DgVZUMjDkxQKhUKhUCgUdwysGhHNeyl6H1A6g+FgjtNZmxpVxQ+vw+EQQEyMNQYBFmQM6in3/+Q2Hy4pfG3TwAYHZ7iFRdM0aLzn+sx47JxclWWJ8XgMHwLqGGgjtlKge4DPbZJCIMSqa43hQCBEFS/aTmVfLQVOaAWiJTW3wXZ1rHIcUXTreDy4AkDLPT/zAKQsZGYyHaOsKk5PzUQzDitqYAmgwsHZAkVhkxWaVV+TFOQ8VEdITjdWbkHqA2ItLYHAhIwoticFAYHTea0pYEBAILR102v5AcPE2BgDHzyqcpgl7Fq2XIcWxnJNqjAmAlCYIoX9NHXN14AdyODK2aikg4lpaAOMsUAwKIxBWzMhclUJOLGQIhEsIpMsp6I4Ahz0BMuE16HAdDpGoABrDSxxuJGzJYCOsAoZK4oCi4uLqKqq1/KFiXIR1U6gT8g7e630Pu0SiQlNplLLvC5TrLM66S6QCXCOVfOclMv9DSAlJOcLPXyvuEjIQ1SZSyAQDAFty/Zrh27MMF3/1MIUvaTkrv5aoVAoFAqFYscQCPA7ScgMKoiuClaNiA4Gw1TDGchjOq2T2uecw3g8xsLCQu8BG+javogi2bYt2jaqVkUBUMthOHEfRVEAsbWJJNl2NXC879GIQ4LG43EXVuQ4YEVUsLyONLesNg2nlVrTERrDxZydamq5p2eeriuw1qUAmlQnWHSpu13bmE5Ra9s2KpFd71KbiBqrwnlNYeEKhLZF8AEUOtJRVRWm02ki1kBXv5i3UckTbWEoJtXW2TnYRIjkZyJC6ztFuWmadG4A4AqpK7WxdU5nkfVikQXQ1DWKsoIxcY5CgC1KFDG4yWbzIPMPE+AKg6YJ6FqXcm2x3HNyL/mk3EXySwYhcA0zK6RIgUZyH1prQT7EMl0mZc4yAZaaZbFIC/LgICF3TPSnvXtZ7n85Vk7YqqqCb31sYRMQqGtdk2pkTZeQO1uXmt9jRK53rWQ7UWFzm65sN5lMUJZVtB7HeaBu3IjX3BoO0bLGIoCSrd7ZGSuvtb0xKxQKhUKhUGwvNKzo1x+rRkSJgKKoYK1BKALqWCMaQsBwOETbtilRMwUXAb06s6qqYC0nhTZNg6rk4JOyLJPKlPeHFHLFtY02BugyKRmNRnBRAZL0T1GIvPeYTqc9C2FP1STqEU1Q16rFOgeKbT9mU0zZmkgAOvInRDNH13ex68kYIlHLkey/htNwhaDl9uJc/ZLj5EQkJzTymbydSqBuDDmRyGsahaR04w7pulRVFUl2Aw9gMplwr1JXpbF536Iou9pY37QwllVNawx8Rtgmky6YiOfFw/sGRKJwUpo7A5taicj9wL1EeRsO0LIYDIaoqgG8b5Jqnsh1vI7kiZXb2N+zcF0oEsX/5fef9x6j0ahnTWV13aJp6vT6bL0q0FnYxWcuZLqoypmk304JzetJZ3u4yv7z9iypjjezYM+OdTgcci/amChso7U3tS5KXUQ7G2/TZosF8c1cEU7fIYVCoVAoFArFHQqrSkTZwsipqEVZsrU21dtxuxJjDCs/mWKWPe4C4F6GznVhNrm6lCuYbJuNQT/EibvtTJCLJLE2TZOIg/ReFCVTlDwhngZAWZSp3k7aqJRJMfLppDnQJT58W0Q1lK2gIIq1h5xkyv1Q45maPvmjQOk1Y0yy/YI66zGIW8sQMwdIf1VCR07EHmqthQ8Bhrj208bU4BR6ZB2rfDC9FFegI/jOus7WTF19YR4AlFqigFOHqyrWh4KvMVHoqXG8KOBjnSdfO2NcInhVVaH1bUx9ddx308q9xbWQubJbVVWy+aZQq7bFYDiEMTZdq7IsAIR0D3nv4VvPZuQQEHxAUThU1SBuExXReH8E6nqizpLyfGEkBN+zfed1uUx8xXoL+NDAmq5/bP/71P85HTPWTYc4t6KISy0yK+aOv39B6kPzmtg2Kr98r1aVjbbcFp6A0nWtWIjYoivfA4qLF6lPaRxX4Yp0nyoJVSgUCoVCcXsQYOCxc54jwk7aj2LHsIrtW9jaWZbcjzGEgGJQoZ1MQBkJ5YfuqPYRwRmLwtlM/XMobAEDftAORCkMRtSfVCcH8ANwJLfOOVSxHYv3HpYopdwK8cyVxLyNC5C1pvANfGA11VkHY9hqWbc1nLMYxFpV37TwjcdwOGLiRz4+kBsmQb6BK2Nf0GQSEEtldzyKRD0nFfJ6IeE9IRLFADjr4BHggweRh4FFUQ66WlDiukx42R8TYkn3LSuxRQNAp6LNqq3eB4SmbxMVdVYUPiJK7V+KosCgKqMFmW3IgXzqjTob6uPJM7EKlMgY17UakGVl0RgkazJgULgqknpGMah6amNhHcgE1JMpiqqEc0DbdinHSVmMiyHGGAyrASbTaWzhApgo9fngYQzPMwccuV5irSBPYG5jT1WZq/yeZRW6S5ltfYuQ1RhLyFSuhMqxnHXc35QIHl0KNIhSG6PCRvWVfCScJtqku2NwSx6bgq3K0oEJelfTnFuu29gGhkOx+qnFBkgWcusc24vjApNCoVAoFAqF4o6FVSOiufWTH7q79h05aclbRuRpphJsI/Vsca8AulYsQnY7EiA9IWMKb0bGxBIrxxMiMWtbza2TeXsUIu6vORgM4AwHGhVFhaaeJrWLzwspBCYpRZEAEwhkKD3c5yqsQGyvvuXU3JyEdsSMyYL3rJM6N2AiHrh9jDVdjR8rjt2+pfYyT8+dnf/Za9H1pkSyigqhyu2sQj5lvLll1BiKRJnSdS3LMtmhZR5yFdZFMmPIZIsDFhwAxeQ+r5mEYZUuBelESC/Lpm2WXes0r20XCkREGI6GiVDzWIqkdsqYpbZUzlmQE3mZfCHdK6mE8rO1JimJbCWul/UHFeJelgWCD+k+zUOo5N6W6xQ8RdW5SnPdX/TIr21HdoXsm8x6ns+tqN9yHfM65OA9QlStJRBLoVAoFAqFYnsRaOeFDGlY0epgVVNzx+MxK0zDYU91BLo2FEziys7iaCyKGRKRBxCVBRPUxcXFRAqSehf3K/1BW9/CEj/4j0aj9FDcEUzH6bV1HetRbS8oqQuJYQutEEFYaRPSgFNk+zWleY9TIbXpXEKL6XSa5ii3wPbJORKBJKI0pjxUqI3EammpQVlWMNbA+xbBsC2WSasF4JKCXFVVIvky5tQqJSOBObkUxVJa5wiZyhXlvMYS6PellPYys8E1+WfzvpsUMiJJBGNsIjs+hghZW7DyHS3MbF/uB/vk5Mx7D1d0abxCDGUcRVmk+ZY5l3kaDIYIgdJ9JecgBHElyFwURcn1svHc5L7qQpiQrmuycMf5c6bbl9xHMk+175RlIbiDwaBno82Jo7FdnWleE5zXq/I8de1WuFa1a9OS269n61Pz+98Yg9Z7XhjIrMgKhUKhUCgUijsOVrFGlDAajZLlryhcT/UqyxJLS0uZBZQ/1/oGKAoUBbfJmE4nsLFvJdCpOaLA5GmhxjkYx/0MQxM42bZk9bKpG1jX1WByGm8X5sNkgS2sk8lSfMA2EBVWiEkIAeQ9nLUIsT6PKLJGw2TYOCZFsVQVBsC0aaLiFWJ9pkGQ0KJI8Iy1cNGqHAJbQA04iKedtv203Fjbya0+AB9aHq3hhN2mnkbLcglrAGcMBkNuoeIjEcvVtNxC2Sl0tkc4jHHxfCm2NZF2NQHex/RVY9h2bRH9xvx323r44LvQHNMPqXLWggIQPLeMKYyETQWE0JFX6WPZ1EzCi6pE07awjhcwKC40zPa5NMbAAhhWg1Qrm1RdcK9Ya7PWKt7DEPebpRBV5cAhRZBrb7venkkRjLZwYwzX06JTlmVOO0VRFFNCUThYW8C3FvW05mToSK7r6ZQXGhBDiEQxz4g80IUcJeIdiTMR9//kECiujbXWoW1rhECpv6qQ+jzAaLI0Tkq6HCPVp4LYdk/E6cXGwEc7rpBQteUqFAqFQqG4PdAa0V9/rCIRjSmo1rCNMEvvBJASPBOxIg7bkbq7PCUVYFuntIfI1Rmga4NhjIm1acQP696jntYpzVVUwbxliVh/i6JPWrxvwLWgnglkVPQGgwGM5/6WzjkOVjKxX6gxHDUdSbV1s61bPAyF2A+1q0PtiImHI7HnFpEUeZABd8/MFEXnCkjIrwHB2i6ch0CoRNWqaxgwOfBtg2owQDkYJmVMFGJR2njfLtl6RbWNVxVEXTCTKMPGAEXhMvIn15/nLVBI7U6klYqE5fTvGVFJW0xrbntC6BS/EALKogITtwLT6RRk+Z6iWMcoKrnYf3MlOYUEhYA23o8GBnVscZO3islVcwNWmpMtOgRY17WyAdC3kBsJlwKauoGz/fTYPNlW5isEH63Wsb9uWSEAibxPJ5PMOoyeupqrkrniHwKrm/I6L+AYVK5K11nOle8/JqhCxgGkdGYAvTlNgVPx+kO+OzGYqttfZ9dWKBQKhUKhUNxxsGpPgGwxlB6FRXrozRNDh8MhxuNxskoCYCUKXasSIMAbfmgejUagqLxJjaP8txBXPp6dGUvAZDJhdVLq5kJINtV8X2yDdQiBFSJ+kA+RLDCJdOgIoU0kw4Lr6SyM9JrMGrA451hJa/stQ8SyyVbYGMgUCGVZoCwdt/6ARWg9rLE9FbFLa+UQIEkAzkOOXDYvQjikPhUABjHMKW+5ITV98rmOAHOP0RCo6y2JrtZyttZUlGdRAVNNZujIT1LsMhuztRZkuqAkIYlNUjuZIFeDAZq2SWpuft3zWmT5b7Ebz/abnU2plftBrpEgn8ei7P5b9gmw8hzQKaA8/6Z3zeQYeZuZZIuOx/aeFyBEgZytrc6tt/J5mX+ZZ7me8rMcy1NHPPNrJ/dWXdfdQk9mqZf9y/eE0L/m+Z/8/pytiVUoFAqFQqG4LfidqIjurP0odgyrSkS9F/to7MUYLX51Xae6zLm5OX6YjsqOb1s4m5MKrjG0hu2YRUZo81AhITyi1pio1oVQdPWD2YM8gKTuiCLb1Wr2z4NbfSCNz6B7wJ7WU1Z9iyojo2zz5Ad0H/cT0DYNCssKat5+JrcsG8MtaISYGkOckEoexvaV4FSjCYPQMnGvyhJ1U8MACERAPIbUwIrd1bcBNlOfh8Nh6qValmWPjHbEkjJyVwCwKxIRvi4e3nf22Jz8Wvb0JvUxHcMgtoihFCyVE8HBYIC6bpLyyW1nOC1ZXluusnYWZpnv6XTaC60SVVFIX6eOZosczqGOn6sGg97CBdDVPEOUyBiQ5MoC0laHe3T2e4nm+wAR21ytRROtuVyjOuh9xqCrLw4hoJ5OeR/WAkQoM/VfrmNeF2yd66mWzjr40C2IyOIMK8tN+v4IWU3BXGBX+Wy/WraHW3G1b7WOVqFQKBQKhWJrCDAItHMIpFpzVwerRkStLdA0NQAbCUkBSTkVu2rTsApH3oN8QN1wq4tgopZoApM+YxEooG3a2KczButwvwiQJ9gikrpIKC1XV3JfSKJ0TCHC3rfxub27MYUs8QN+1jYl/nfMEAVFK6IrClgKaNoWZFoUDjAmb33CiqEQtMFwiKaZooj9TClwj0d58HdyDMNKa90wQfUt9+3MH+hz0m0t4CzbhUPwqEqxaiJTWeO+Yh2jNZbnPftiztZU1vUUbVtjOOSwHuZLTNBcZjtmcD2vcxWsNfC+qz00BjCms7IyiQmRCnF7keDZgh0IMIGJcV3XvQRaVhFLmKhIW+fggulIaabYyTnkIUqzyjCApP5ZGAyKEq33MESA9Wh9gLNl7J0aeMQGgCEY8hyYFPuvBstJyG20wJL3bBP3HPJk4eAMgYIHczS22OZkjUKAi/XC5YD7oUrrI996FNZy+x4DwDMJhw8oHFvIS1fAOscu2UgWcwKaiHg97e4hV3DbGgLI8z/TLt5XAFANB2n+QR2hFfLJ38O4kANu5WNd3N5wXaqGFSkUCoVCoVDc8bCq7VvKskrEM69jy2vThIABSEqmPLdywIqHLSwoUFKbZP8hhseUVVfHmNsUQyBYZ3vql3y2q03t1FUhNHlNoXOWH9StRTsTAOMj2SjicVH0FbIQagwGozQuIUNd70wPisfJSaCEJwnpzq2fdV1jMBgA6Ehd27SJAPu2hckCYvg4XXqpNUykWWGzmNY1ptMmzU1+/QaDQbTi5u1IkI6fb5vXLTJEMQyQl6x1PRVaEIJPqrfYhCWlVpRAIZNlwaqu7Mdag9aHXgKuzKGMpWkatG2LYQxrEjuyKMASvlS6zprcxmCfFAxFISn6PHSTiDEXxXZqu4zXADC267eZ13Ty/YeuttJwuJGzMeE4q6/kHrud6iitb1LbFiKUUodJBMoWP6Q2VBYtvPew6JT4EAKK7J7Ok4bz85F5cTPfFZ4JXvQoyqL3OtD1GFUoFAqFQqHYEWhY0a8/VlERtZhOp5Gc5NZT03u4lYddeRDOg1O4X2KZlBbnHKbTMUIIya4oYTb5A3r+oNxZeFlx6xJAO2ulkJp8LHmfTO8D3MyDvZDHIlOe8sRS2Xdd16nWkPfdtRYprEMbrZM50cutrvm85PWFOeESeO9R1zVGw2HPukyEnkUT1sRerAMMBgOUGSlJCqG1UfHsrMwyJiFwMsdcUxmiAtr1HpW54vMtEiHKLak21lC2bTffQtaljlLqQ6218MEDsa9o27aw8XrmNZC57VXmNtUgzyinYkFF7P8q1ty6mYIIaCnAFQXKqgv9aZsaVVnBmJiibLrj5Am6PqYr5wsfAO9jMGClMaCzO0sytCw6dNePWKVEv21MbkXO60yFWMtiQkfuYy2t6fqNEnGTrrwVT4j3b95yRuZMriGAHiHOF4Lyn+V+VigUCoVCoVDcsbCKiijXSzIBQaoXzNtY+BhMZJxFbAQCooC2DUmBGVRdmmmID+RoPbcsib0K8wf9RCzQ9fSUpFYiwmQySfuTh+rpZIq2aRORosB1mRQCXFECVkKMyqzWkRVX70PcN4cMDYeDWJtXJ/JV19NMcTSJsBTOwTqHpm1Tym1OYmWegK41h3M21l9KGxe2H1vDZL2KpEr6RrKyZ1FVQ7ZUeg8Dg8LxudioQheFg29ZtZTPeu9hrAOi7bIsYhosEZqmTum0XIraBUUJcRaFTdTXPMQo/29AEly5zNFA6oAlWKq7ZziJ2cEVvH0bPLf3iS1o6mmNwaDiZOK2C7HiOuSQlOimaVAWBZyzfL7RemoM4AqLoRmibT1gLKqihA+SdtzAWklmFutz3janjteAiWVqdxNCrw45qeKgVJQs7VmEjCOSQm7VYrJ0aLZuh9gKiIjrn53l9j8GMTQp8BhSa5l4fxH4eol1mLJFmWRJjzyyqRtUVWaHjrWzXcsZ29v3bF2vBhUpFAqFQqG4PeCwop2zmK1hRauDVSOidV2nB3AmYSa9DgDz8/PoUjmZbPCDroExRab6GfjApIS3NSjKGLxDXGsoxIatpKzIFLEfZ9f3kOsVJYG0rqdJsWx9C5NaXnbkSYJhuCWFiT0YI0Ez0uqDyVvbijJFMMaiLAdRyasjSQ0oo3UR4HEFx/WFRVFwm5UUcCSkpesTWUZLqHFsER5PllgljmTHROXSEFN6Jggdmc1VN5P6qTIpk9o/WK7FNVF7c4VD3dRc72gsyJqMwPNni6JL8c3JvSwCyOtCpHMLcCK70b7Nu3GgYFBVFtPpOEu8bVCWTJIpLkpYZ1EV/TRmmADftAARhlWVLKhtPE5VFHBFBd+2aJspQAW3KIk9RJu2jhZngm89RqOK6zoNt3qpygpBFMdI0BFt4waE+bk5lEXJxDoQWqozVZgXXKT1jFwbaZkiP6dALQKcsfDUcg01ddZwaw3IBwQKqY1KII/Q+Gx+4zWOtdKisEpYVFt3icMyxjLad9um7b7MxP9n48KGfO988HFhoN9PFeiSh0X9VygUCoVCoVDcsbCKqbkhKXKzREXsnUVRAESwpq+cSB0dWyYtmqYLPMnth8baRB7z9FmB2Dq79iwE71sYw8RWCOtgMEBbN2k/XfKr1Ju6+FnfG0e+Xa9PZbYf7sOIaDXtFFqxrQLcExQGqJtpUp2qqkLhqmRBBQjGOvgAwBQoR7E20Tnu00kBCATrAO+ZRLiyQDAA2SLaMV0iuYG4tYixFK2c3Pc0tG2aUxvnNwRCMAFFRphk7Hkfy3wOckWUz7HfXgVACr3Je1Vaa6Ms2rV34VpjVkO5Lnh5zSHFOslp02a9YYt07fNa4Ol0yjbYomsrlN+nfSXXJOKZbNuxBU9XI2lQREsvE2degBmPxyjLIt33+XdCFkFyG25uGXbOgTJFlxOPO3KXf0bGKim53FLIAHFRAkRoPQeDzS4EzF6H3A4udaUyThODlFzhYG2RQrzkvKqq6vX0lRZMmpqrUCgUCoViR0G081JzaSftR7FjWNVO8ukBOiNmeT9CUR2RPeQLcRQyUZVVsgsKuTEm9i+MybiC/EGeH4Tb7NgS/mIjGTUcNIMugTYP5anrulNFTb+lS04ERPXN61I7wh1AZBLRknOS+rzWe5RFAQJQlhWsJfjgY9owwVtKpLUoHcgARTFANRjBlCWKsmRCZbm9BkJA8IFnJXhu+xJCnAfAGkSrZ40AA+ukRypbbj0IwQKtjyq2tahGBdqi5YRdCiDqaglnE2nzXpfyMyC2235AlJCV2V6akh5rrNi3+foWjtuhEAVM6ymGw+EyginkSci+2LCFgPVamfi2F4SUo65rDAdz6WfnXJxT6RlawrrumBYdgZUFDxjuNep9i7KsegphVy+K3lxKLedwOOQ9GpOdHyCKfq6eyiKI/FzF1i1SA8323/5CT06GZdz5uGRBIfVGzUl54PuabfH9eu98bPn3We25CoVCoVAoFHc8rBoRlTYPnd1W6ioDnJPwIAvnDChYOCCF0QRwvaUnoPEenkKs7ytgwDWX1lpYsmwfndasPEXbKhGh9R4gQlWVkUSamFBrUBWx12ZScyxsUQISUARCM62jJdiiqhyMtWzn9B7M/fh8iAKAaB1GACVebCKBNfA+oLAWwXBbD1FiAQPfNHAVmCzYAqWL4UfWwvsGBAfjHMiVcIM5VIMRXFnB2IJVTOu6lh1EsCXXKrJRVNSzLmlYbMKGi2uBSCSk9lPqJKNRGURs3SUK8E2dbKree5i4L7IebdsgtCHWb8YaXABt6xHi3MNQUlthDfcytbZrKRPrEF3BLUpMPDcZlJBVIUhC/IKP82kMbMFhTK4sMBmPYbxJ4VbGOdRtg7Iq4WyB1rdcB2w51AcU65LLYU/xDCHAU5d465xllTTaWmdb6yAuXDjrYMjAEPjcrE19QhHv996CBwiFNQi+TfcAAFgn91JHfmeDuYQAigLNpJvgfcN1vujIft6/1hi2FldFyQs7oUsgTqeTkVMuHzVoGm41QwiQrja+bXqhXSbWURurRFShUCgUCsWOwe/E1FytEV0drCoR7UJMQiSEoqL52BbEom2aqIpy/aB1NvYx5BumaZtEkAJFlXQw4BRQIoSmjQ/4gKW+wmRS+IpF8C1crJuUusa2bWBtgbb1cI77J/JYEAkQ60o+MJEsouIX2gYuqn1EshUAMIkoHKfyOtu1rAEBZVGmB3prLZwt4CPpIPJwtoItuF7Sx5Yx1rE9shzOwRYVyFrUeXgNTC+Uh4iiAsbj6f7wz8YWAPnYG1P6Ypq01axyaTIS66oRrLHpfLlGkn8KwYMo2mCBOPcBThJyg4/tL2OANhEKE8OfHOABGFcgGIM2eBgDeFgYcB2ttRYUlbXUS5OAtqljzJWHcwWTP2vRtC0kGEoSb1l9drCW6x1hCrT1FOQDvOG+oEW8RmQMbFHEfrOO7bdRWfUhS5gN3A9WCLYxBk3dJCWQrzMvYsCYFKwkQU9lyfWkUkMLw3NljYmBV9JaBeleEkIpVnEhs7ldPCm9BmzdjsjJqNhofd0geO7lSgaJ4ArKsuxZnNO9FMmmi3W6oWlAASloycTa5Z6dQKFQKBQKhUJxh8Cq1oiKcjWZTAB0SlBVlV2bB2tRxYfttuX6vqIqEXw/vEXScLntSBdCAwDD2K4EMXVXVJkya+thDZNEGQ8hoHBSz9bVkcoDfWfLzXooSogR2No4GAySpbILy+nqRGdbt+TBLUyEfRynQyCAQkAIGYmwjpVaY1HXDWwAihJw5SBZifM6zLZte+1RuppMCS/qW4jzbbraz5l025hWRpF49Kv9OospXAERvoR2WN5xrOUNsWYRMVgpdBvGulUiSaHl8ypnWpIwMeZ9SmLyoKwimSc0geCNgSmHMNZiNFqIAVdMjln1DWh9l1JrrAOChydKBJPJG6cLmxgoZUJcGGkaPgdrUu2kRd8SDmQtdqjrvTqtp2jqGsWMRT1vZyOLCmVZpvtcFi/yHrdCRPNrmtvg8/u3abjfreyr68FqWRmP94Gk+Iq1Gehaycj3U1wORITCFel+ma1dtc6BQr+9kEKhUCgUCsX2IpCFp52Tmht20n4UO4ZVI6ISfuKcw2g0ihZWxJAepFo+SYwdRlLX1A38RNSpKoWwSBKrfA6QlipFRrj4uV8ewIUoSOsMeUAvigJN20RCvLy3qTzYS4uNvD41BI+y5BCc8XgMSYJNtZHg8CHpIzkb0CMP610fSEQLrY2BOEIA2dI7cAXawGTOuYL37QPqULOdFOiR6Mlkgqqq0jGc42PMqmddLR/S3ORBM0AkjSYS2MgybZx39mhGohUVVfmsjVZfPmEhI0JS4otCzoUI5XWWmQqbg4hgST4ROZ7lMCFRZoX4UabEUWDN1FCX0CyLFqZt4NuGrcPR0s3BQNF6DYqJymKP5bReH1vBsGWY4KNVlojgis4KG7xnSzYRKKbrBgoITYArHApXdEFJWZuj1ntuLYPlPVFzcij2Wqn5ZCLdhRgZY1BZrtEFulpt+ZyxrNImBdd09dvSezRQ4GsaF3mKwmE6ncZ7zqR7tiPh3AOWgolBWsv/fVAoFAqFQqHYFgIMwk5q3xLUmrsqWMU+okDbNvAxFMbG4BwhSW0bYG0BYxxcUaHxTMasq1L9pKSkChHMa9fytNE8vEV6T1rLD+LT6RRVVSEEANaxAma4HhNwsFbUNh9tqUKoXCK/chw+FuCD79XZ5SFHXP7IxE6CY3Llq40P5kwSCAYE7wFQbL/RWpSDEQwCnHWYNi02bdqEAIMNd9oDZTnAoBrAE6Vzy9NPB4MB2tiCxLki2o9tIub9MCVKYzaGe31y3Ws/XMrmtaS5MomMDPUWAzIyS9EGC+GskXCHSDSNpLt2N05HNU1vf8YAlIitvG0AQ+nz0oYl3wP1uocQXHpPzicAxOSUr0NIbXAosJZKga2roeHrWQ6669/4hvvBRpJsAxAM0IYWnjwMAcPBkGtM40Bd6dL5yWKJN1wj2oV4EYxFapWT23Obpo1W5CL23+U63aoqAdj0Dy6fW3fdpK41Lcg0NYg8BuUAgbjtjSHuNWqdTSnMbaAsoIm/Xzb2UZUxeSIUzsIRf08aansWfYVCoVAoFArFHQerSES71i3c2qGcIY1dvVvXAiTW9JVVv84ta50ipCRPZ81fywNmVrKksjaXWUozTCZjeO8xHI5QFK5HqEQtZKLC9XB565EUGBMCJpNJsuSKSsbqk0WZkmw9nO1UKuccSutAsAgBqAYj+Pj6cDhCUTEBda5A27Sw0nYmknNJqGUVmAN+moZVU2kVk6tWco1Erez6jnaERZReOb+VFF55LSfjs0m0PSIZj5eGsUIa8aybM09znd1W9jF7zN559ve2bN8gtiBT/FksxdZ0pFY03iF1dZKiwBpRZClw8FJa2Ai8TXZ/BPJJoUcICG2LEDzXoZJNtclG7g1EVdFYIH5WUnytIRDx9bfx/qKY8oy4UJK+P6FNCj/IJat74RzapkFbT1PNpzEGwQfwMomBAQdQgQALAhlZaGJ7tMxToACyvJhhMNMTVaFQKBQKhWIHEHZiWJEqoquDVa0RlQdQaUvRtWdpU32kqI6TyQRzc3NpWyE/xrCdt2kaDIfDnq0UkP6cPimkYjksyxKGOMAmV/iAft1pZ8V1KRiGVVz+XN7mItXzRTIg5yPHdc7BFSUMOuIxnU4TKXXWwlNATrBzktXGsCUYg9IVWFpcwnSyBbfeeivudOc9EqEpigKTpkagkNS0uq5TTSFbnnl8Yh+WnpByLnnPzzzgKZ9DCdMR4ix9OXPiKftaiYTKdrPkdRY54QeWk8rtITKzNYqzyF+b3R+/F6lmnhjMb/Y/F1OdjMl1W6k5BSupyV5MSWVO6UrwkV7GaN0QE4cD99MlCl1/zthmiHzLBDEuEDRtAzdwQEwtDp4VzKqsEMSSPBMuRMT3uFh0JYDIGAs3GMK3bToL50oQDIeGWZtCtto2KrzGoCpHqe6Uw6oC0NZoPActsYJLcNHmq1AoFAqFQqG4Y2HViCjXk9mYQupjAmiR6uqKkpU9SRMVEiVkR2Bt1zoif50fhitYa2LiKD/AS6orSaKr1C1arutjsajoEV4msV2tp7WuR6Latu1IhzEoSrYXDwZFJC4EC5v4htgRhdwlIu4sWs99PYuiEPMkUx5r4NsWPljMzc/DFSXm5+ZQlSWm9RRr1q6FKwquHQS3G6nrOtV8lmWZyCj3CmVFrq5rOGthCocQLcshtmAR1VNqTAEh1iGmCbdomoCiKNP2s3/3z5XrCMWGnavTsn0e6pOIUkbq8lpdRJIzq3yLYjur8M4qor3QpdsgswaAFbU2l2nj+Fb8jOzXZMQViOolQODWQH2rsSQ6cVsTsiWKYtgdC9mCSSTFfC+bRHJ5jIFrXUkCmLp2RGylbuFbn+Y/hM5V4FxXA+pcAU/AcBBb5RjLiymGk5vHWzZjMh1jfn4Oc2vmUnhVURZRQe/uYgNK6buhbdFOtrB1XRVRhUKhUCgUOwi/E8OKdtZ+FDuGVSOi3PbCoq49WGUpIgHiOr/SFqiKMraM4BYQXZ/IMj00e+9RVVUiS8maKCQgsFJkrQEZfsAmBDT1tNeGgsUsDusxxiU7J4CUiCsEVPYvfwrn+KE+sP0QlUuf5fpItlL6aIuUFNU8GKZpGlSmQmEtXGxbUhSW7ZTxs2VVIpBDUVYYjydYGm/BZDwBHODJgOBQxrrTNgYxyb6lJnU6nWIQrc1103BabSBuX1MUyEyl4J6i0tMVAIpk3wyhARAQAtA0AVU1hPddiJCkIovSLUFOTODbSEY7a/Ts51IPS6BbMIDw+qgIzirRETkBXckCLP89+7N8dlvI302fmdn37H6YG5q0Az4HtrTaLIVYNmB+2Z070DmU2d0dU3XF7h1tw2zRNdwWyKBXvu8wn488kvg45phEzPuP5Li3kCAKr0GI52KMAUJAOfCo2034xc23YP1u6zEcreHvZchINRCVX8AWBWwBYEAYzM0DRL0FJIVCoVAoFArFHQOrRkTLsopExceUW59spd57tGhRWgdXODTeJ2urj//d2UCZcErfRbEICsk0JqbUUgsrBBI21XQmNbVtAdiMxHSpotLSIrcQSsCPqJnCFGSMotIFCiiiTRcA19ZF5UpqOGVf0l7FJAWYH9ApBFhjYVyJwpVwRYG5+RJFUWDL5v/C7nts6Nlupe5zOuW6vsFggKWlJQBI5JBCy3WGxCQ5+ABjuV1OrhDmibtd39fOsiyhR2z3LXvviRrLc8zpsf1UVttrKZPXmuYKaa6UCuHLLcD567L/WYKbvz9LXGfV0Fnr8LYsvSt9Lj9e9+bM5/P/3A5FFpmN1mX3U4+spxZB6L23Um0sGQljCoB1CBSVWWOSyowCaAmI6zKdGk2ddZsDt4Zomhq33PJLrN/NYX5+fsX56v9sAMvfQavOXIVCoVAoFDsITs3VGtFfZ6yiDm1hjINzRVIaASQ7LPck9Fwrmtk4pZZRVBQiVizztM8QAqbTKdq2gVgXQwjwLYe5iD02VyWLsgtL4pYUlI6ZbwuIlTEkwlRVg4z4du1d5LOyz5wMCYEtigJVVWEwGCSCxWNv0/7FJllWI8A41E2DTVs2Y9OmLainDfgyMuEsigLT6RQAUl/V6XSK0WiE6WSSkTu2KYcYoBMopHHKOeQEZqVkUyGNQlRFLZVrkxO4XMHmfXeWWglU6izASJ/Lbdddi5zQq+HNLb6z9mC+Ryj9nW+/kkKa17MmpY6oS0iaUVhz8imqYV5zvLU/OSlbaW5vi/TObkszP/Nr0dYt4qRJRu/sPT6OjYs2RiKIDLgWFYCFBQUwe6WuvptiDbL06S1j66S6rntzPjtflM2liWFGCoVCoVAoFIo7FlaNiAohIPJwzqR2ERJA5KxD6316WJ5VxDi1lq2uQkCapklWXWttCjHy3sOgC0WhwDbcEABrHJwrEbzsV3p8BljjYI0DBcAaC2cdgufU0+A5p6up62QV7RQ0G0mxhY3KE0K0Q4ZIdixQNxPUdZ3mZDCoUJTSMsbH4JkWbTAoBnMoByOUgwGGoxFGwyGador5NQuYX1gD54poK+a5bNoGPrQYjgZo2hr1ZIK50RC+qdHUE/i2ZqssDIKPtuLWw0VbplwjHnOAb1u0TddPk0IARfLPRJLDd9q2hrUGdT2BMYS2nQLgayyqNi8aGHjfREuo2Ha5pY8sHAhpkcWJnHjmZFnax8yGHiXCmSclR4uv/O2zlj/WmESKcjuw3HeYIVcGSPeBXFsmrZF0iSKLtBPmg6ZPVrdmI579vszWwfa3obgeQfDkEeL1WOmP2HDzz3IZa0jXA+BwJGsA3zZsb0d/DkLgys9qMMSatevQtB7kG4S2RttMEOL1ZWW/I//d2GV/WiOqUCgUCoVix8CpuXan/FFFdHWwatZcttT6lDpLmXVRrKqucL2WIEwMbCIpTID4s6LGiZLFLSxMPI5LJFPA+zJomhZE6KlqYun1nttrBO8BirZVIBENWKBwRY80MWHoSHI8WM8OyucXQ2WMS+PxgQmBnLexBtYVIOMAW6L1HuPxGEXRYDLmVjLVYATApORdTuEtUJYFppMJCMD8aITpeIK2aVAWBaaTMQDCdDJJoUZt26JiBtzZo9uu9UeIRMxn82yMAUUbcds2KMsqph1LnWcTFdK2Z4U2xkQrb5H+5sUEkxYWuMdpm6zE8rlcdZ1NSA6RqPpod0Z2vDBj5c2JrLyX17PKNnmLoR6BjNdRrh3AaqKMphc/ZAy3TbGz9aAdVlIQVwpUyklrPp6unYtsQz16t5I9N0e67/MXDaJSztbq/PPJph0CrAWGozkMR0OMx4sYDEpQMGhbxOAutmUb009Nzs9BoVAoFAqFYkcQdmJYUdCwolXBqhFRQFqihPiQ2j1gS/9NoHtATg/9IFYm4+vSX1QgZEPqEhFJiKS/zhIN+W8hQ7IPQkCgFm0dEokSK6kot0KQZNwUSRy/FltnZAm0RAQLwym1JpojA3V1otZAlKOyLBHIIMCiqoYwRZmUXuccxktjtN7jzruthysc6rpO1tymaWEoYFgNsLS0iIn3GA6GqX4WQKq1FeQEDUBSj31cKFhGxIRMAIkwNg2T0bquY8IwUj0vvzdIRDIPwxFSKZ9ji253TfJtxL6b19RK/W6+GJAvYMyGCMmYVlq86HrW9q23Mke5HbmbE1ERO+euhPPkYwpbIaP5fbmSQipjzs9RXk+WbzLcGwbYJrGbJbz9UKXltuj8+FI7vZwQWxABaxbWgHzbcyXIuc0uxCj5VCgUCoVCobhjY9Xov6hjEjgEdPZDqcmcJQS58tgRwn6tp6hg8jkbyaqQDa4n7WoRq4pDk6RWMxGN4EHko03Up3RT2UfT1NFGutxC2TR1r95RCAvXt8Z+nUAiWc45HlckYESEuqkxbRq4ogQZk9TQpaUlTKfT2O6G91PGsdd1DWsMyqKAb1tMJxPMz83Dtx7jyTgpzQBSTaqEGM3NzQHoWssI6crrY/P5z9VlDj9iSzITzjKFF3XkyialMqmaUdEWi6wosWy7luvY2Z6bpkl/y7wyGeX7RIKr8lpf2YZtw93CgSideU1ororn5LRHvFcg5VLPy/dVtwAh87jMjgq2C8tnZxdH8gWSley4+TgF+T0t36tlxyYJKKLUMobDs3K7bXfu8hkZV35s2YaPx0q+MQZzc3NJ8c6t1Pzf6dufzlP2rVAoFAqFQrEjCLA79Y9i12PVFNGmqdODOdeQMdkQEiMkTQhnjjxddpY0gIDSuvQAnStqxoKJAvttUQ2GKc2UuHAUxkonRBvJI4/LGheTS2tI2xJrxfoLWNupfIBH23LLmLIo4Az3EGXBijCtpwC4X2gIbbQhc39OgwJkAFiLwo1QDudZRQMwrAao7RTeeywtjbHHHnuiLEq0TYPBwGJYFqjjvI5GI2zatAlbtmzBaDRC62ssjbdgbm4OIdgUYNTZectEiF1Vpjm3sAix5rWuJ0CsC2wDXzOx5vq2RVGWaH1XE9g2NUrnUE+nGAyHqKdTFEWB0lmEtkFZWITgURZc3+oMK8KhreNrS5ws7H1Uz020+fLnREV1roqkiGCMSwppp0hzXaRzrNzJ/SAqnfx307RwjtvLAIC1XZov17F29aTdgohB23aGXLZcC2HsFk3atsVgUKFteL9xJ6m+NUmp6FKJiQgU30/23nhPByI4J0QuktKQJQ2D7zfbs/cCgaROFcli7kwXMOVEuSSuzZbWPX0Lba70tzCGWxsNBkNMxoup3ltqZ52Qzti+xwIAcVsmmTeFQqFQKBQKxR0Lq2jNZTUyV21CCJhMJjCma/0BdH08TSQqRITpdAKgU/CMYcurhUlKlNRN5qE1gTpFp2nqpMxITaG0gRH7ZqrNg0nJskSUxs5jiKQtKoghLE9rBZeEwhiDsixgDFDXlPqTyrl4DzRt4H6LrgARMJ1MAGvRTuu0XVmUkFAkadfinMOgqjAejzGZTDA3NxdV1EUMhhWGwyHG4zHmRnMAETZv3pzsvFu2bMGGDRu412lmQXXWRfUQaQ4SObEW1hVJ7W1bbpEjqmiItl4hqlXsccrXtetXKWMXQixqZSKMjq3R/HMNwEXF0fesuUwEDYg6ZbRt6+y4vGggY5D6VLlGVVkiUIg9TrM5cA5dL9XOLi4Elsmr3Fe8jZBJXqgISa3lbQKAzposSq8gVwh9FtS0rCZW7N5Zy53ufu3UVNlGXpfPyj07m+icW4llPHkSch4WJfvge8VwOnNMbWZrNcGHrrdv8B6wNpLc0Pt+KhQKhUKhUGwvPAGeds5itteKoVXBqunQSXGLD9FikXXOoaqqXq9E2YYfmENUYShZekU1ddFiKw/d0gYF6KyE8vAcgkddT9A0U4TQomnqHoGVWsV+qw/0lFh5uLbW9OoG83ETUY/Ash2z64vqXAljHIgMfCD42NdxOFyAdQWKwmE0GmE4HGIwHMIYgy1btmD9busxNzeX7KZCjOu6xmg0QggBS0tLqKoKVVVhMpmAiHuXLi4uwhgmDbfeemu6Hps2bUrnJyRLzk/a3siCQV67O51Oe+SlLMue1TYP9REV01qb/k7hVHFRISeXdV0nMib9YWWbvAZUCJqo5fJzTjhn03bz3qg5GRLSJSnEYhHu7oN+Wxq5T+QekHsjtzgLWeP9hx4Jy4nkbEua/FizwT5N05HU/NxSkFB2/+bnl+8jtzBLDfFsSFJ+rfNFo7zeWM6vKAoMBgNwinWbPre1+tBOcVYoFAqFQqFQ3JGweoZoAnzre3V2bL/s6kLzBFtJoc0VG+e6mkciQlM3qJsGMMAw2k7ZistEoijLRHKlFlNsjUQBJlok27bhekZjYo2pZbU1/mzAyazSuoNCgLMWFDzq6aTX0sPAwBhWgKKsCGMtfCDAcP9PGAuKf2xRoigr2JhAO61rLC4uop5O0dRNrC1N8irm5ue5xYvvSNp4PMZoNIR1Fps3bwIAzM/PYzweAwAro5MJmqbB/Pw8brnlFj43a7Fl8+ZIPmKqr7VwroCxFjAcqOScg3VdaNNwOEwkJHifWqC0kWyPJ5POHm269OL8NSGZQuyEQAqpQbSZ5kof0KmbQrqcs/C+TUpkp6R3FlZeOJBtOttu69tIGl2yl+Y1oQAycr78b7kPAaR62OA9ptMJ6noK71tMpxN436bjstJK8J5VZ0kZljYreS2mEGP+bvg0/px4dmS3qxldqfYzX1DIt897ts6qpbNktusZy2M11qJpW3gf4oJGPxQp/+5SCIhZYj1bskKhUCgUCsX2IOyk1i1ea0RXDas66zYSQg4ckpYgdtlzad7bUCyS3QNyZmU0QONbkDEgA7iyAAy3jSAE1PU09e1kS2qJoqhgTLT0+hYUfOKMFGJokfdAkF6KgDMGVVGCfEBoPU9i3I6859q82DvUxLHA8t9t4BpVV1QwtkQwAW1oEQzQwsC4AeBKuKJENRxgMBhgbm4Oo+EQrfeYTGq4osT8wgIa36INHuWASWtd1xgOhwAIi0uLKEuHufkRxpOlVDc6mUzgg8f8wjyWxksIrcfccIRbbr4l9RAdj8cwhvusEgxcWaIoByjKAQajEVxZoChL+JC33yE0daztnE45MCkEkGEy20YVUwiOqKGyCCGkRpTdPJ3WGQvfNLAw8C0HRwFduFVeTyyLD3K/5DZcJkMe3nMtaNPUsLFuWEgr79eCqLMGS4rvbFhPTkRlPGJ/LYsCNv5tKMBQgDNA6WxUmNvUM7VtGyZkvMzQI6feN+B64gbGSJ2qtFTpApxyUinjA9Brw5OT09ltZolnPv/5f88qqM51PX1BAWX8PnnP32X5rubH7Mioh6GA4Jsd+4dDoVAoFAqFQvFrj1Ujoo33CFK/5lyP0ITg0dkcO9sjwG1NhADIZ0RVy9W2ldQbeXCXPyvV5eUENz2YiyWYWvjQRvLQ9lSlvNdlnnhq0ClVuXrbpZLaWCNqULgKZVmicCWa1qOeNphMJtiyZQsm4wlctC7HEWNhYSFaIBuUZRFDjJZQVQOUZYGlpSV477GwsIClpSWEEFLf0KZpsGH3DZhMuNZ2MBjgl7/8JcqiBADU9TTOT3cuZVkCMeyHz5/rfH1sj5PPZb9tC1t18xRb+Zycj1hZ5VpNY7CRqIApbddaBOrmOW+7471PBDcnp7Kd7FvGmNdVzlpiAaRxrmSVze+VXPXLE4YlYVlSmfNtRHEVxbafLttZo3OiK/dvIv4ZCc0tw0Iq8/sw/07kx8ktzLm9PN/nLCHN389TkGW/ZVmuYIXv5i//PstYFQqFQqFQKHYEARaBdtIfVURXBas266LShEwRk4fXtu2CUSSNVuo2c1IpYUSzfT6BfpuNSbShGmMwGLDKKK1apDbRWMtKWyIaXe1dIIIPLdqWW7ZI4JFA9iHHBbKH/ayHohxfLK1MBAAfCBQMnCtALJ+iLCoMR0PMzc0x+bYGS4uL8N5jzZo1iUwLUWraFqPRCACwZcsWGGMwPz+PpmnQti3m5ubQNA3qusbCwgImkwkmkwnmFxawefPmNF+/uOkX6Xz4GpneXBZFdz3y8xXiWdd1qg0NkWhOp1OUVbWsvlJISVEUKahISGOv3jSrx+2uD7J2L0WPeApyQib7E0LUhSYhLWDM9pnNP5vbc1cK9MnnIg85yvvcEhFbnLNtpH2L/N3VHds0TiGCEuAlJFD2k75LcVEiH6P8d36c2brUvFZWjitEUYivbCOf698jYJU5fg/lmuTqqYwhn69ZkqpQKBQKhUKxvQgwO9Gaq3kVq4HVCytyLlojbVIMu96gnfVSHq6F2IXAaxZSmyktUCSZ1JloiSXexsDCuRIAW37l4dd7H/sqRppAbBUO3nObltD10aSkePaJlKhaMITBoEQ1KLmlhiFYC8AQpvUkloIS6maawmAADkwK5AFYlNUIg8E8YJiQj8dLSQ2V4xnTqcBr1iwkhUwI3JYtW1CWJdeALk3g24D5+TVoWyY8w+GQa0iXxtht/XoAQNM2WLtuAb/ceDOsMxgMSmy+dSN8UyOENtYrxlkiC+NKuGIA2AJFOcBkOgWMgY+EXeo8WT01KGwBCwsEwBiH4AnOFqinDawtYGBhbZGs2QBSvWhdcysaH1pMmymMM6gbTmTtyKAFYGPSLf+cq6J5DWqeOsvvcx0s3xcrJM+aLi1Wjpm/lwft5KTPWAtjLQL7ztMYAQNrLIxBUjOF7OZKaq4cyzZ5LWpO3KbTaRpnPh4Zd05CZwON8rnJz3dWqU59bjNLbtqfdZwaTQTyLcrCsUU9eFRFkeztBgQb7e6GCIYIvg0wsMBOSrxTKBQKhUKhUPz6YFVTc8uiTKFAkvgqKpiQ0aIoMBwOu8CVuF3bNDEMSMhlrCH1oXs9/v+gGqAsymX2Qyac3FrFex9bd1j4tktCtZZ7eFpbwNkC1jjY2DYlV3Hrpo6qkINz3De0LItomWWibAzQxqAagDNa2jbAugI+EFrPgS9FWWJhYQGDwSAl4E6nU4TgsX79+qiOda1HptMpRsMhyrLE0tJSVEDnMZ3WaJsWg2oA7z3G4zHWrFkDgLC0uJTUVO89dtttN9x0040wBqjKAktLi3ApzAeQ4CIiE9N8KxRlibKquKdlVBRzi6Y1BtPJFGVRoqmbSMJ4QaAoSlAkjk3doCi6NiZCMquqStZWAsEHD5MlJYtSx+SeiaUoqjmZylXSPNE4BIqKO/cQzUlebi/OfxZSN1sfmkPs5iazzfIxHQJRPO7KtttcQcztw/k2OTmV+zAnoTLG3HYLdOm2uTI8S3zz4+d2XFGpczs8gGirj31bfQsQoSh4QScED8dFuFl4F9J31Rqbvn8KhUKhUCgUOwJPZqf+Uex6rBoRlYfcIqt7lKAaUUrzWjSg680oD8Le+55VUx6q8z9SS9dE4tprH2HyNNS+0iUqI8Bk2NkCxjgYw3/nD/lt2yYLrDyoyznmdYN5K5nUdmU0j7n5NRgMh5zc2rao6xobN27EdDLFdDrlvqExERfgBFxrLSaTSZq/LVsWURRFSset6xpzc3OR4DfJtrtp06ZkDx6Px1hYWEDTcG3pwsICbrzxZwCYTIzH42W9K2W+5Lylf6iPScFisQ2e+3GKRVNqQnPbam4Tpaimyr5zIpkrcHlblJws5nbhPCkXQLK0im0VEGtu11IlT/CdrQeV85f65LzGN7/POjss9X7OCWVuVxXiKedYFEW67+Scc9utEO/cnmuMSXZo2X/q2ZkpmFJrW9d1Ty3N62xzhVbuYzm+fP9m2yrxnMY+p3E/OYHNPy9zwHO2PDRJoVAoFAqFQnHHwaoR0dmUT3kYLssy9Y8U2yERqyedSloymXIFykjEvA8Aou0wtmwxsR2GdZZrQqPNNtWSxvYY8vDM9aAhkSYYbrdio9XSWgcY9EiIEKjBYJDOK3/4FlIi5xVC/Cxx+5YQDBrvMW1q+JjcKkm5VcXkczKZYGlpCbvttnuqFRTS0jQNqrLEaDTC0tISmqZJNaR1XUfVE1haWsL8/ALKssSWLdxHdDgcYnFxEbvvtgH1tMbmzbdiNBrg5ptvTuRgOu1ahgBdbSeARJpEsbZ5qxRrWSkrXEoqFmJTlmUisTK/rfdJmRRyJOfZNA1IVLuG267I/gBaFtjTpfB29mohenLt+21XKF0rfq2zn0rC7qy1dbZ+VMZDUfEU5Fbu+EqP9OZWWiGKec2xEL9Z5TT/HgkRn130yAmm1ETL4o8snshCjRBYIY9C6mWfMqdyL8hcFo4dABS4uiJPL87PU+ZH6nHzutQVRGWFQqFQKBSKbYJi25Wd8Yc0rGhVsGqzLg/MdV1HZcynPwASuWnbFoYMEAAHB2dcrCuzkcjxaTApAowNcA5wjkDwCNSC27fE1iowcMbCGQvpNUkUezIaoG4atL6FKwsEChhPJmy7JY9gAqyzsEWn5kkCrSTSSo2kqIRt40EtAS1ggwUCwZNHNZpDMZrHcH4erijYzhhaLC1twWS6hMWlzWh92xHOukEInIA7Go1QT6ewMCitw9KWxZSO2zQNW3XnhiB4LI23oKxKWOOwZfMirCkwNz+PxfESTDyXX266BRvutAEhANPxFKU12LxpU1RDPdq2BowBN0XhekdjHKwt4i1kURQVmtbDlSUm0ymMs/AIaHyDYlBg2k5hCoNgAsgSPAJgDTwRGt/CuhJ161EOhqBofZaFh0FVpV6t0VGdqYaup5QDJtabmmjX7dRUIP+cjW1TpJ8nE1zvm2ij5lYpJrVVoVQTLIrirGLfEUsDIv4znTZo2wCQReFKONvZhXs25oxgiuIq9cRsy+6HLOVEWBZugK4Vy2zdqCT4CsEsY0/dtNBjbeozK/dw/lk5noR+yXultQhti6Io47m23WeMSdZcoFN1+Vy4rRL/2fn/vigUCoVCoVAo/mdj1YhonmCbW2LF3hpCiD0xAVCXCNq2XVsLa2x6wO56Fnq0bROJYNtTL3NrYNM08b1oX8xq4upI5kRJclG5k1rQnJA451DO2CLFBgt0tbCAKEJcZwnj0LRsiRUyW5Yl5hfmMT8/37PPTiYT7L5h92THlX6b9ZSDewaDAZq65hTc+Xl47zGZTJJSu7i4BYPBEMPhMLVxEQW1jPWoG2+9FWvWrMGWzVsQvEfbNLh140bYbM5y1U1qLHM7rNh4q8EgEXOZkzyUR1qzSBpubpEGuLZXVLnpdJqOX0+nsDGwqVP/wrJ6TWtdqpMEOktpbr3NbcK5MinnMBtYJGOYTYTNkb8mn+c5YCprXcGdQiMRFsVf5k7+iC1W7MR5jWuuUMoxUx1tZgWXcct5y7nlrVjkuybnLG1mZBEF2Vhlf3mbHgA95TUQz890OsVkMknnILXduWU9/2+FQqFQKBSKHYUnu1P/KHY9VrV9izzEiq1V1D/5Odn6oo10OBz2UnalLlOU07wdRxeyk/V3nElFkRTc3KYpSlHejiWE0LMG5/ZOIgLM8vYteeiOnJuQrsFwhLn5eQzn5qMFtwIAjMdjbN68GYuLiykxdjQapXrCsqwwGAwwHo/hvcdgMEhq2fz8PEIIWFpawmAwgHOscJVliUE1wKZNm1Lrl/HSEgBgbm4Oi4uLICLscac7xfrOAj//6c9gwenDS4tLca66kKfOntmpe2IRFVVPyH5eyyrkhgOAunkXwiV/SyCRLFYI8ZZr2G/J0u8DOpuQK+1/ZH9yvNnE25yUymu5Gijn2bvuQG+/ct4CuZ9s4VgdDi3arPYzV32FXAsBl33KeOWeJ6JEYOW9/LxlDLllVwhufo5ybfKen3kwkYxB5qKu617ddD4n8t2pyooXSOo6JTTPks9ZO3Nek6tQKBQKhUKhuONgVem/j8qbjXVjjE5ZFBLYtk0iXEIEACQFx1nDls3g4axDVVYwMTMXBATvUU+naJtORbKWk219VHIAeaiOLVtiUiy7MgmB+n0qrbOwsf6UH7jBfUgpHjQiBA/vG/jgYQuHcjCCJ4Np3WBxcTEFCznnMD/PauhwOEwkaOPGjVizZg2GwyEmU1ZD5+fnk2osvUKXlpZ6hLaMdaOLixxitGbNAsaTMZqmxmhuDvWULdHr163HeDxB03JtKRHBWYuf/+ynsJmlM9VhRpIjcyVkDwDa1qOqBvHvKqXSstWzCxpysXWP3ANyvQF02xQF6qaJarRBG4mS1FDmCbYh5GSRazyLwkXL7fKApFwRzJOP89cAAMbAOk5N5rrjWIOahfbkNZHyMxP1gKLgRNmiLLg1UJw3vi9CIt65IyBXdvNthExKjWVun5Vr0yeK/J0Iwce/Rc3lebHWcG0nsTU5n5uc1Mr3Uu4tfp16RLyua7bY+9iaKNaOLqunRRYABa6lVRKqUCgUCoXi9iCAe4nunD+K1cDqhRUhwBoARNyKBYj1f7ESMT7UA0i8rrPTZmqVAQyzTRgQ+OMGNtaSWsO9G0WtkQdjUY1a34IAGGti3aODMQ7OOljWBUHkuc0EmMRO6yka7wHLN6733BMxBMTawPjVMISmmWA83oKWPKgoMJhfQDkYYTgcYX40wnA4TGRv48aN2LJlCyaTCQD0rJLWWqxZWJNsj0VRYDrlVN25uTkYY5IdV9q4WGvZjjteQt1MsW7dGjRtjaauMRwM0NYNlpYWsWHDBkynnFQ8GAzgQwvA479uuAEmJqK2TdcSJCdKTEINnCthjIW0QwmBQ6WapoVz5bK+lmLxzNVUUQWJCD4EWOdQNw2KskTrPTwRXFHCx9pZJjpINZ1EPoUL1fU0hQ7l4Tqi9AlxFAKak6Y0LgqxH2jgGlkKaGMLGb5fOjtxbkPlOmdKY6FAsDAoXAGDzs4q/U7zezK33c6qnkIm5VrL9RAyN0vmm6aGMUDbNgihRVHYNC5rDdq2ARB6TgIZl5xTXg9LRKiqEhwWxfd423pehLEGTVODoiVbPiN/54FSgQhN22bfdbXnKhQKhUKh2DGEnWjLDWrNXRWsavsWTkrN7XkSsNK1yxgOhytYbjt7oFgW86TRvCWIfE6QW0tNpmjl1kFRz+TzPvZEzAmC2IJZnSrScZkAtFm7jRLlYA6BLKyt0Lacgru4uIgtW7YkhW8wGGDdunWYn5+Hcw7T6RS33norhsMh5ubmMJlMUNc1FhYWADApH41GqT9oVTHRZjJaJAtvCCGl6C4uLsb6VbZ3rlu3DiBgy5YtWLduXSS187C2RAiEtq1x48//C84A3jfcJxL9WkigIxuS4iukUq5RG8NsROHLFUD5rMxlThpzAiZqn3M2HRPoSJ3cA7NptnntZF7nmvfUFIVUgnhypTG36+Z2Wd92r+f1lPnxZvt2yr0lJFpIqNRGS61lbl3NW+TMEtb8WhhjsLS0lOYynz+gS6WWMfYJZp9IywJIXpuahxDl1vQ8SdcA6Z7L5zmfQ+89vCRHg7B5aRFXXn31dv2boVAoFAqFQqH4zcHq0f9k7csJAtJDtPT+zEmhPIj3Qlfig7ykxQr5EGK0tVAUJq3U66sor8vx2lYUWMC5rt5OSJ88aAs4oIVVHrGOBiJYV6EazMG6Eq4okwV3NpBo48aNKeRlOByiqqrUZmPdunUIgUnnIIYBjcdjDIdDlGWJ8XicCO1kMoW1bPWVNjhr165NyaiiWG3cuBELCwswAJaWFrF27VpMpzUWovIKQ5hOxtj4y1tQONMjoiupiACS0ilkp2uZsrxNiSwc5EFGs8RwljRJiJSQRiG6+RhyhVXuLdkmJ8iyXd6jc5YIzp5fTjCFgMliRtc2xie1czLpWhDl1ls5t7ztTL7N7JiEGArxy/uiypjylivyR8Yxa4HOa0Wbpu4FhUlNqHwf8rpaWayRpGghoELegb5zQeZG1NwUWNU0+NwVX8Xr3/J2/Olrz73Nfy4UCoVCoVAocnjYnfpHsetR3PYmv6IDu5Ltk0ZIS0BZFnCuRCAPZ7jnJgiovYdzBbeCMBZt8LDOwocAg8wS6QOmqGN9WtcnFOjIDhDArT0MDCwG1RAUADJcMycEh0JAUZUwAEIb0LQtqjL+7ANgOgskjE21dCEQCARXco1kgEUxmoexBYwtMRmPUTfTRDKLosD8/Fz6fNuyTXc8HsM5h9122y0Rm9GIldHxeIz5+XlMp1MsLi5ibm4Oc3NzWFpaSn1I6+kUIZRYu3Y9Nm26FVu2bMFwOERd19i8eTPWrV2D8Thg8+ZNGA4HsLHX6Lr16/FfN/wERVFgcctmuHUOv/zlzRjMDbFu/e6xvjDvARlVagCIhH06ncA5i7alVN9Y102sa2yjnTckNTC3pDKRk56VPJ8yV92CRKeGM+Hk+6NTRV16jcfbEd28HjSvOc7DlpxzCMR1v7namqvxsF2taG43btuW57+uEUJH5JJqaLpAovy+zMlpnpQrJFOs2LLQwgFOZZqjEChtw/W5fsVFHD7/gLKsIjl3cK7oJQnnNcECGb/Upoolm9sfsZthOJqDIUIYj2NNL7fQIZZcUTmLcVPjiq99HR/92CfwjW9/H+Nmgoa0MkOhUCgUCoXijoZVI6JCmMbjRVTVAIPBMKWjtr5B09ZcoUnEQS8UQN6gKDgkSJQbDiiK6lWsCfTGwBhKgUXyQM3JryE9zDvbkQRRhBIxiD1Gi7KEK1jlCiA00xplUcQepl1vSkTVaDweA5ZANsAVFddNFiXaNqAyBsPRCEXpesrU4uIWFAUrR0VRpYd9CYFZs2YNvPeJTObpuEVRJMut2HGttZifX+AwpDDGwvwCJtMxxuMxp/CCsHnzZszPz6MsAzbdeisWFhZQ1zXG4zF233033HDDDQCAzZtuxZq167B50yaM5uYwHJY9ZY/QJcwitV2p0LZ1RlpsRqws2pbVO0lizRVPIZJCrIQU5WRvOm2S2s37YtLvXJH2I0FJ3odkCZbjSXsS2b8sAtR13WvLgxjykyukovLJNiEEGHShPkxUDZwr0njluHK9Ze6EXEtgkajHcg6iPOb1pHnyrHPcAgZAIpBl2V0fvi07IivnwoFFTBJzG7Wcg7QnEtu4KLU8d6JqErwnlI73I9shU3cpEGACrC0Aa3HDL27EZZ/5HD7xmX/Df/3XL2BsAGwLonan/tuiUCgUCoXiNx9EBoF2TjNy2kn7UewYVlGHphSOI/WX8pDN/UNNesDvHtD5Tx6ikieJzqaXCpGQbXOrJj88oxdmkytI8pnpdNrV50XiBeSkycLarnbOsFwHHwhkLIbDOQwGg0QQNm7ciMXFRSwuLibivWbNGqxduzYRgY0bN6Kua9z5zndGCAFbtmxBVVUYDoeJ3FRVlerxFhYWEpmR8KPxeIy1a9em2sGiKDAcDpM1dzgcYvPmzSAirFmzBps2bcLCwgJctApLL1Tftrj5F7/AaDDAeGmpVxuZW0nzVizO2Vjn27fYCqkU4t+1WOkH9aTAong9hGB1gTlVZvvs1MbcNiopsPl7QsbkXslJ5Wx9KbIaRwA9G+ysfRYwPcInBE5U29m2J/m2ud1cXpex57Wgcq/LfQwg2X5nbdJik5W5kHntamc7BVj6tOb11jLnea1oHowktnSpL5XzCsH37oeiLOADofaEq7/1n3jt/z0fl37scyjn12KwMIcWBE8GxnZ9dxUKhUKhUCgUdwysYo1oR+ZCINTTaaoLDSFgbjTipNJAaBu2sFbVANa6RKqE+OS1cYPBICk0sp28PxqNuK9jfGgWxahTmFxqPZG3kRHCQAQMBoMU7gIghRbJwzfXX1q4ogKMRdN61DVvU1UV1q9bh3Xr1iXSubi4iM2bN2Pz5s3JFsyBRAbj8Rjr1q3rkUYhD9ZarFnDtZxN0yQCWtc15ufn076rqsJoNOrVkG7evBkAsG7deiwuLSEEwtzcPDZt2oyF+QWsXbs21v7x/5YWF3HTjb9AGW23nArL6Ce3dsSzqip4H7JrHNI556Q/D4DKa0pzda4jrgWapk1EWEiPEM+8BrHrE9qRs3ysuSIp90De5kVIley3r3iiR54BbgMkxDrv3ykkVyzIAHr3ixwXQO84OTkVpVa+G3L+rKB34+KQq0mvnjNXUbtazbbXSkdUUTk3IfBy7Px1UbBn62gBoCxKEADnLLxvMZk2aOFw+ZeuwDv+33vxvR/+FDfdugU/vfHnaH0NT4Rqbh32u9cDbsc/IAqFQqFQKO7I8DA7sUZUFdHVwKpZcw0RLBmUtoQtDYzt0miXNjWYm5tHVXAoz6SpYQmwtrMYAhyME3yDECQx18C3BCIgUOzhabsHffIBBqIeWYTQqbBtE2CJ0EwaJsmO+4wSBfgQw3kQybPriE0IAT60qNsptzBxDsZWmJ9fD2MdrCtAAKZtjc2bN6GuJxhUFXzseTocDTEtClRlhS2LW7g9y3iKvfa6C6bTKbZsXsL8/DzmRlWqDR0Oh5hOpyjLEuvXr8d4PE5kdGlpCUtLSylRd8uWLZibG2HNmjWJmK79/9n7s2Bb8ru+F/z8h5zWtMcz1TxJJSQ0gAAbMNCmHQJsYwNtHNGOjg6HHNH9YDs6PDw4HA4gsAkebpjwA37z0ECYG26414AYLkiAJJDQrFJJVaWaVNM5dc4+e1xjTv+hH/6Za+U+quuqgiMOSPmN2HF2rZ0rV2auXKvym9/v7/udbDGbz3EItra2WS2WRDpia7LN0eEhcaSYjLY4KY/D/J/wnB4fsr9/gclOTFmsyIYjhFCb91NsbNChykWgVJgf7NpTW6Wvq7p1iWU3mbU9H6IobsKN2kqYQHQDcZfnUnZbwtWSTGPqtWraTfRtiV43YKg7A7me/3WBkEsh12mv3W7P1prbtNauiWiroEZRRFEU64Cp9u9dAt3ua3ssuknAXbLdJa9BaVXr0KewrRWRkqERS2yClsqOBXoTPGSamwNRY/tV6+MAgeS271lLpNv3r11HsLCz/gwpqcALhHAkccSqcvzWhz7Cf/ml/4bwCWky4N7tPXb2LzAcj8nzkslkTNV0yvbo0aPHNyr0lcu3bV326OSNLfgG5/Pb+rr/KTo3qHv06NHjjeKOEVFr66bSRFLVFUkSr9UZazeqldaaQRTsjm0aJ0Ach+VV05PYXrx3iUlXAQsKnyeKQriNczWt69F7jzUGJcMFeJzE1MZCZz6uS3Rgo8YppTA+LGu9RyFRUVCGqrpCufC8NE0DgSwiokhzdnaGqU2YKQV8FghPmqWY2obgoCYpd7VaMRqNyLKM2WzGeDxehxNZa78qRbcsSxaLBcPhkMlkwmq1JIo1WZatbb7b29vMZjMABtmA2WxGFEVcvHSJ669eRekIqRTCWpy3GGu4du0qo8mEOI0bUrexha4Di9iQmNBV6c+pn92k3Ft7M9s5zpaEdYOEugSxa9sNM7+bWc/2b+GGhV2nI3dnHbtqYJfs3VqL0pLiOI6pTR2OhzhfAxOwUX2786PtudJN423Po64aCxt3QHfOc63Sw5oUtjOhUbRRldc3SWCtmHdnYSOtQ7BXx368uRlQrY9z177bHsOWuHetzu2xCqFONsxxS0lVl+hIkhc1tbN86gvP8oefeIwL9z4ESnPh4hXSbMCyKBFSEQvF6Xx+zn7co0ePHj169OjxRuBvY/+n73tE7wjuGBE1tsYbT5IkDAbZuZk47xx1oz4lSQJKri/s2wvioiiI4xjJecLZtWW2BGYT/FKemwdtRgHDOusKgcOYCnBIFePZEISWLHUJclVVxHGMjgJx1TomTgcopUkHGco6lNTY5gI/9IYGRVRKGYKDmu201mKspZhO2d+7gPeC5XLJcDhYW2vTNGU0GrFYLJrOz0FQkBsFNEmStTIaRdE63GgwGJAXK6SUbG1tMZ/PyfOc8XjMarXCeMPu7i6np6dYa9nZ2+P05JjhaMTZ2QnSCfBhJvH69Rvcfe+9RLHuhOJ0+lo7x93ajQLZqqLt/raEq5uG2xL7W62g3UCh9oZDV03tdpB2bamBLMkmVXYTWBQ3x787u9qSwm5lTFc17ZKlrurZTaK91QYLnAsbapXQltAB55T11r7cnnMtqW1t3e12hm1gPa/ZWrUloUKoPQbtNiilcB3lVwixJrvh5k+0nje9VaHt1gW1z2u33zmHxK/3RWnNjZvH/Ppvf4iXbxxwPC8wPmJ3f5usmYF2xoEj1BfFKbu7e+RF+TX9runRo0ePHj16fP0hWHNvj6W2t+beGdy5+pZzCaHNf9Mk3AqBdYH45WVOkqYoKagqg2svpHWEqWuECIOuQkqkAC82qaJBmSvXBEA3ClpIVA3JuniPd6E6xtTlhnQaj45jvNuEKrWE1hiDd6HGxFiH0oooiZBKgxAYYymKkmVeMB6NgxIbR6RpQlWFyo2yKimrktUqJ44jpFThXyuZz+dMJltEcUKerxgMBiRJzGIxRynNIBuwXC5YLYPqKZVkuQykM4ljVk31y9bW1lo1TdOMfLViVa8YjyeUZbFO3q3yguVqxe7eHtPpGWVVMBwOyZfLoJo19lNJSCk+OT7iwqXL4Xi2NS4tUWssuu1MrZTt+1CdU9dCtUoI+mnRktNbk3I3naSbEJ+WSLUKekuMuopemCmtiKK2WsedI8Yt6XotBTLUk0DbcxtpTW023aXtPG+LtaJpDLqT6tztAe3aXrv23u7zYUO22/1p55q7YUhhnaEX9hx5FQLVEt9m/6qqQjb73S7X7fls7b23BjwF0h4189S+OZ71WoV1rgmu8g7nBZX1/H//26/w8c88QR0+1OBA6Zi7H3iA2hnyvCTLBpycTrly+RKVharoU3N79OjRo0ePHj2+0XDHiGh7oe+co6zqNZEJF8EOHW/6G21d4aVEq2BAtMahtAjzl953CEGY5dvUboDzlryoSJLQlSnauU/rEL6xjNpgI9V6Qwq0jkLyqxJYZ3EuvLZzDikESiqMNRjjsF4gI8l4mCGVxlnBYDBESk0SRyxXCzyORblRfnQUMRwOUVqjlGI2m+FKx2QyQSnFfBEqVdIsYZUviaJ4baEtlism4xELU3ByfEA2yEjTlNl0xXA4Is1ilsucqqoYjUYURUGRlwwGI1arVWPbHayVyVGjjE5nUwbDIR7PajFna2tCvlqE/lYgXy2oiwlyNGQ5n7G1s4tvwoBaCBGShW0zfxiUOUFbSblJeN3UuARyI86Rtm6vZ+gc9WurqRCbhNy2L7QluC2h3KiBmlBVEvpJtT5vTe3ahduEW2MMSofnWedQOqLukLjujY51x6iA2pqm33aTwtve2GjJbrt8S8xvtfp2FWLY3LCp63qteLek0Tfn7/o4NRbbvCiIOrOw3fnmW4OM2uMQqm421TKt2hoU3Gpt4w03DjYqsfcOhcfLiN/4wO/wx596jNoJEA5XV+g4xdSGg2tXqY0jGw6piwrn4fj4BKUThIhu87dLjx49evTo0ePrHQ5x26y5rldE7wjunDW3UX3CbFmNZGPFbNGd5VvP20mJisMcXJqm51JMux2L3nusOz+jJ6VcC3Dee5zd9IA657GWjhJnzyWUhqTcYJ+03iMSRZKlSOOQWqO0BqkwDmxdNzbcqiG+YZtGo9Hagpnn+doimyQJoftzuJ4HHY0CaQTWVS2Fz9nemlDkBScnR9S2pFgtyBcnpGnK1vYuy8UZcTpkOBivSWfbPbpcLhkMBhhTrR9vLcPj8ZiiKJplMmxdsZjNGI3HzKbTQFyE4Pj4mOFkgowiVqsVg8G407u5qUpRSq3DizbKW9EhnvqWecpw46AbFLRJt7Xn/gZyrS6G9W8IbreWpSzLtaU1qJDynMLaDR7a9JKGvxtrUSpaW3YDsd2orbf2dkJH5Rchbbhbd9L2gG7I9IZ4dlNzu793j0OXZLefB2M2Cmn72l17b/eY1MagG5Ldbmu77ija9JSeq7AB6rpC683nR+v4HHkP/nXFS69c5//4/Y8g0wGRhyQSZOkAhKasLCpSZGlMnKYgJMPxGB1HoWtXbUKvevTo0aNHjx49enxj4I5ac9doFJ+u1bEbCuQ7s4OeQEZbFai1LXYv7DeVIec7Gb0U1NWmy1I2s6fQWiNZp426NilXCIy16I7KhgDrLKWx6DgJKbrO4zx4BFEck6VpUK60pl4tMMas01611usu0FZ1unnzJlmWsbu7y9nZWUjKHQwoigKtNZPJhPlsyvHJIZPxGCU9hwfXWUxP8KbAGcfd9z3IYLKLFIrTwjAej9edooPBgKqqmM/nZIMwNxpmUIeICObz+WaedLVkMplQlQWLxexc/YkQoef0vu2dhgBVmxnCzkxiN1CofR/b5XSjNrbEbDMj6ddkcHMDwK8J1joUyW9Uz3b+s6tytusIFTLnk5ZvDSrqbu+5xzpqYV3X6EidUxjbc7irkq6VyzjG1Jtk3tZe2z1HWwLZ/r0lehtb8ebmSzdMqGuf1TrCWnNOxWz3p24Ci9owpna93f1tLc5hxlSt97VrP25fd6NQVyEdF5r3QeKF5LEnnkInA95y4TKzVUkcRyipyQZjtrf3WORLyrpCac1wPME6zzLPSbMEHce36VulR48ePXr06PGNAncbZ0R7RfTO4I5FRLX2Tes9OoqIo4gkipBChI3yoc+zPS1asuE7c3POOWpTU5tq/eOFD3slmwoWNh2VVV0TJwk6itCRRuuYSMeAXMeTe99acP26Q1E1RGydoOs8QgSSEeuI0XAc6maiCGcq6rrk5PSY1WpBUYSQoa2tLSaTCYNBRlWV5PmSo6NDptNTlssFFy9eJF8VGBOCg1obZ+gUhel0ymgwZHt7l9lsysnRTV598QVefekFXnnxK7z8wvM8//RTPPflJ5gdH7A4PWIxOw1pxJFmMZ8TRRFZmrJaLnHWkaUZy/kCIUSjxi7w3hInMfPlksFwRDoYBIIpgk01TRK8tZwcn5AmIWTKWItv3tNuOq1sVFIhJUIKpNTUtQ3kpZlB7JK/W9Gd+RRCAqJ5f1gfn1al7IbznF+HhabOpJ1p3KitwR4cbKkOYy1SKaq6WpPqta3WOqQI9ST4YDUWSOrKhMeQOAeRjhuSHZRb76GuDVKqpgNVolSEMcG6rPV5W2ptaoSS4WWax7oktCWpWmuU0kipSJIUYxxCKIQIr6N0tLbfhplqjXcunMt1jakqlBBYY4iaGyPdmw3OWbSWwa7uPXEUYY1BNOnTztYoIfDWgRdYYzF1ST4/ZSgsA+kYasn2eMjJ7IQ0zShKAzqiNDXT2RneWdIowddvoBqgR48ePXr06NHjGwB5nvMTP/ETvPWtbyVNU+666y7e//73c+3atTe9rg9+8IP8rb/1t7hw4QJRFLG3t8f73vc+/sf/+B9fgy1/87hjiqgnBNysOx27iZ3tvGjz01VyWvtn+98isNZgKQSMNcQqKGHWWpQQxHHcqDqWsgp2Tec8WoDzDinBuZbsnE/GhdAFWTeVMtAmloJQCuc9dW0oq4rBYIBek4TBWo2q65qyLNeBO0KwVkPruibPS7SO2d+/QF4s17bjPM8xxoQaF2uZzxekWYaWgps3rnH9lZcoiyWmLnHOMJ9P2b9wN4vZgst330td5iRpQpxkxMMBy0VQPcejEbPZjOFwSJZlgeSOhozHY2azKUkSqmZOTk5CYu9qifMOj6coch64627y0rCYzxlvbYftzTKk97jO/GQ4Vo2qJ4LCmKbpuTnI1wr8uTV5NtSjsLH8ds6BYNvdVIp89WzmedvprfUtQkisdWHO1NU479BRhGvUvnYfnNukLXdDj5TSm3O1Dchq1MhW5Q0zrIHwCiGRsrWQbxJ019vfbHsbNgRirVK25x/NZ2Kj2Jq1OhuOi0YrSd0EdXnv15UutqPqngtAqk3nGBnaeeiWqNbNMVwnCgvRLOcpS8vBtWtkCrJIcWl/l92dHWbLgnmVM56EQK00y1it8jCnayyz2ZxYKLTorbk9evTo0aNHjzcHdxvrW27Xev6sKIqC7//+7+cTn/gEV65c4e/+3b/Liy++yH/9r/+V3/zN3+QTn/gEDz300Bta13/4D/+Bf/bP/hlCCL7zO7+Te++9l1deeYUPfehDfPCDH+Rf/+t/zc/8zM98jffof4472CMaLoCTJMHU9WvWVwBre+VGGdtYNsOFscR5F+yOzXxpe3HfXkB3LY5iXdcSSIdUoYc02Dk3ZKgloevQm4boOueC+udhPByho5g4SYiThCiKmM1meO+Zz+frHkYhBJPJpLHZKqbTM4wxLJdLtNbs719ktcxZLBZkgySot1XFZDLBGMNsNiNJEra2timKFadHh7z8wnPcvHm9GV51SOHJ8xJbC06OTqmLFaOtQ7I0YXv/EnE2YDIZr9fV9pBGUbSubnHeBrKwDI9vb29zeHD9XECP1pqTkxPuuf9BysqAZ32c4yZZGM7PhkK48dC1ULdo5xu7s5DdDth2Xd1woG5dTBRpnOOcLbX7frf/dq23m3W5c3Uk3eWMsSGFubHbdutauqSwawfvVsJ0t6c931uy2U2rDTcmNsfjXJquCLLorSS6nfNsH2uf196wEUJQliVane9o7e5fa19utwMh19uvVGd7ms9BO5vaHoN1UJOS/M7v/j5PPP0sFy5cwAs4mc/xQlJUNbOy5sYLL2KFJs6GCCXZGo3YGg6pVguqxSmX777yJr45evTo0aNHjx49vj7x7/7dv+MTn/gE3/md38nv/d7vrZ2RP/dzP8e/+Bf/gve///18+MMfft31HB4e8q/+1b8iiiI++MEP8n3f933rv330ox/lfe97Hz/7sz/LP/pH/+gNE9uvBe4YEXXOrTsdfWPDbWfRuqoVsA576ZLQdqYzpJk2F/zeh7TQRi3yQpAkSYfYOKw1697FuqpwSq4vsq11HdXTr9NJQ0BRSPpVTV2IjOIwF1rXlFVNaxFVSpFl2Xp2tQ0cOjk5WQfDtIQgyzK01o06OQIPZVkyHo+p65qzszPGozHb29sh2MjlFMsFL77wHAc3XmVVFHgHuGDLjKKIxXKJLgqEr8jOTkmSjEdUxEgEUpFlSUgMFoLJZMx8vmC5WrK9vUVZhqTdra0tjo+PUUqFWdLFfE2AWqVvOp2yvbtHnq8YjccbotSQsG6ojvc+WKw785F1HZKSN4RfrY97Vyl9LeLXnX2s66BKdhNhW0gpztXGdAlleF1PVdfoxo6rtQLRdn9GOOu/6qZES/hai2y7/vacFo0V2XVU3/amy619pEE5l41FWSI6CuV6RpPzabq3qr7dSpd23eGGjgo3Tzr23KIowo2fDpEN5FWs39+NNdetyavt1OqIDmGNopgvPPllfvsP/pgXrh1wY5EzHI0Zb23x7BNPszMcYq2lrgzTvEKmS0aTMRGeajFHCcve9hhp57fvi6VHjx49/gJB33P3G1ru//Ph33tDy31rcva6y7zv8+9/Q+uq7Rtzo+z8wuh1l8l+47NvaF1vCO61x3V69LgVzkvs15EiWlUVP//zPw/Af/yP/3FNQgH++T//5/zCL/wCH/nIR/jsZz/Le9/73v/puj75yU9SliU/8AM/cI6EAnzv934vP/ADP8Bv/MZv8JnPfOaOEtE7eNRlY2uUoYZFCJAyVFB0wlSM2RDHcAEu8FgQDoQ/Z9mUMlzMt4FACIEDrPfYxvbYKpQAWTpCyQQlE6TUhOv4UBUCQVltU059XVFXJdaBSlKSNENHEVmWMRoN16FD7QV/nuesViviOCJNYyaTEcNh1lFfw3qTJGMymTCbTREyEOpWTd3a3ma5XFKsSsbDCc7UvPD0U3zxc5/lbDoLluCioqoqinLJMp9jbIl1JdP5lNPTQ26++gLPfukzVPMzfO2Znk5R+GDlXUwZjTO0ViyKFXEW44xjsViwt7cXbgp4QZIM8A6kEKzyFVGkWC1mYA2x0hR5viai3QqQblAOItS6tHOjsg2JWit9GqWicD5I3cyCSqwNXaMtGW3V4pZwSikwpgIc4DCmwnvbJBxvgoo2vaEC3cwFtwq6azoy27lPJTX4jdW2PRfaWU3YJOSuz7tm2bYzVSoVboi0XaKN5bybTNsqr0pHCNnWzHSCiW4hre22dBN+u2FI7c2PcFxCxyiw/hzdqqZ2Q5faeejwvnmk1AgRZmDbYyilDOFbdY1Umqs3TvmF/98HeOLp53AeoiQjSTLyyrGoPSdFwXS5QGsYJSmJkEhrmZ3NKYolg6FGYNaqa48ePXr06NGjxxtFuPITt+nnzuNjH/sY0+mUhx9+mG/5lm/5qr//vb/39wD4wAc+8LrrSpLkDb3m3t7em9vI24w7RkTDxbE9l3hqrV1b/9pk2fYC3Fq7JobdOcL2+VVVrRXV9veuldM1ylQ7s1mV1TmbojWbSg3gHMGIoggvBEIpVBSRJBlxo7QCLBYL5vM50+l0neQ7GAzIsoyyLCmKgrOzs7V61pKGvb299T7t7OywWITgoCzLKIoC6zyj7W2Ms8ymp7z68vN8+tN/wsHBdYwpUVKRxilKKJyD6WzG2WxGWdXUlaHMK+bTKYcH13nq8c+xmt6kmB5y46WvoGyN9pabV19hNMhIkwGLZcF4soUQgul0yng8RiqF0mHuVQoJzjOfzQDP4eFNlJYNGSzRkTr3XrbHETYqdjeZFRqC1zwGG9WxRXdWtH1/WpWyJb+3EsE2Wbf9W3uugNh001rbdJBuUmjDdjtAUFX1OTLdKsFdAtduW13X5/a73f52WdHZzlatX1tbO/Oh3nu03nSbyuYcbPf9tchwux3ddOignHrSND1HTNubJe2+FEVxzm7bzn+2n8+gNneqk5SiNiVSCypn+cDvfZAvPfU8cTphNN7jbJpTGDidLojiFGNhe2cPbM3OMGF7GJFqj5aW/d0xF/e2iYcjbs5Wt/GbpUePHj169OjR4y8fvvCFLwDwrd/6ra/59/bxxx9//HXX9R3f8R1sb2/zB3/wB3zkIx8597ePfvSj/O7v/i5vectb+J7v+Z4/41b/2XDHpIiWIATFDGCjLtlQ6HnOFgmcsw+2NkXvPUmSrO2S1tr13F/393DxLxAyEL2qqqjqGtVcmJdlAd52eiHDxXdLWIKiJZBIKlMjG6tlHMfr7tOWYCwWi/X2SqlI03hNpFvrcWuR3N3dXdt2R6NRmNcDJpMJeVFQ2RodeU4PD/nsn/wxL7/8MnlRUuZzkiRje7JDOhwhTYXxwWq6zAtinWExHJ2e4LwnVprncWRJGgjr0XUu330XxzduoKVn79IDOB2xWi0Zj8eUZbnuHZ2dnW3soIAzBm8cXlmODg/Yv3wJ6wKhjlTUECq9tqW27zdwrlbl3Axnx3rbEr9umm6XhPrOTYUW7bnQrZppb2xs5jvVuTlO06y/JWCtfbabynzrTGt7Tmzs3PariGm3oqU7+9weF9FZVsqmR7fzvPZYm9ogxcY22/0stJ+f9hzv1q6s51B16NttPyttZ227zW2PrJCyUYS/ukJGKokz9YawNjdkHvvSU3z0U48hooyt4YjlqmAwGrEoKxZ5zuUL+xweHHLtxgnv+ab7MXlBOsw4W62Y1YYiL3jlJpwsKgbZ4E/7NdKjR48ePXr0+AbF19Ka+9xzz73pdVy4cIGLFy/+qbfh5ZdfBuCee+55zb+3j7/00kuvu66trS3+83/+z/yDf/AP+Ot//a/zXd/1Xdxzzz1cvXqVj3/843z3d383v/iLv0h8hyv07iARDRUprVIlpQi5O95TlBVSCnSjCrWJpGvrYJNAKmVQfqo6pKMqrakbstdeTLdkNtSubBRRIQTeBeJmnUM1wS4hQbWd7Qu1G16AjGKy4RihYpTS2GaGoSUu3nvyPEdrvb7Ab9Wr+Xx+zhaJEAxHY5RSLBdLJpMtyrKkLApG4xHL5Yqz6ZTxaIh1NTdeeYVPffgPefrLX8YZT5IMcLamqA2Hp6cMspTBMGM4HFFXJd5birrGEppFhIBISExdkEQxdW04PLzO9PQmWmmW8zlSREz29nFaMpvNSNOU4WDIfD5jNBxS5kuCw1bigOn0lIuXLmOtYTmfMd7ZxpoKLRViHTTkQx2PVOvj1RId2CTqVlVF0swBt6m45+tYwrnRzuHCeTLYVTS7c5Qba61Y16S01TF0iGRLOrsKeVdtbW2w5ytOxLlZ2PU51ZkJvXVW9nwXaqOmtupn8zri3LxmCCtqSeH5WVG/TuQNM79BmW7JexRFFGWJVhrrHKxtt/bc8ffeE+mI2pzvI1UqzIrW6xlaEyqVdMSrh8f89gc/QukkexcvUFWG8VaCUIqyytnZmbC9M+LgxgGOiJOzBfdc2kEIweXxZU6nXwGhmc5KLJpYp1+z75kePXr06NGjR483ix/5kR9508/5yZ/8SX7qp37qT/2arZA1GLz2DfrhcAjAfP7GsjV+7Md+jN/5nd/h7//9v8/HPvax9eOTyYT3ve993H33G5th/1rijllzqzonjIUG62sI24lRKkJpHVSaKApEtDMfGC7AwdSWqqzxSIRUGOtxfmPZbO2vXQIgO8mrdR36GnUcoSINQqJkDF6BV8imJ1RqhdQRXkfUDqrKAmpNcNt5RSHEWh1dLBYURUFZlgjBuTlFoRSD4QjrIBuMGY0mLBcrtIoYDIas8pzxZAsdRcwXMw6v3+AzH/8ET3/5SfIqx2Pw3pCkKVmaoZRmVRQcn5xxNl9ivMChEDIQ6aqqKaoV08WU49NTbhwdssgXOFtzeHCD48ObvPzc03z58c8wPT7C1IYsTSmKAoEk0TFlUaCkQkuFkgIpPVJ6ZrNThDOslnOK1YpIK4ytcW3KMY3CaR3CbxRN0yE9bSdmqxoC59TuljRpHc4R33Z4Nr+39u5b6002zxVNxUtIya1NmElsZyK7Cmk3kbe1q7aPrW9mNOvtJvO2lutuwNKtoUvdxNt2XrRbdbPpNt0cn3aOtkXoI21mWYVE62jdJdrdfu89xrowe6o0viG37f60y7X/GlOjVCCyoQpJoJQMM7brLl5JHEWcLVf88v/+Ab70zIukWcpgkBJFcTNjLXjkgXt48O5LXNoa8i1vvYu9nYiDo1MqC68cnPDZLz7FsnbobMxotEWiNfmqt+b26NGjR48ePd4cnBe39efrDf/+3/97/sbf+Bt87/d+L48//jiLxYLHH3+c7//+7+cnfuIn+LEf+7E7vYl3joiGegrO2R67VROtetOtbVmrRrdc8Le21y6R6Fosu5bHLmlsH19bJjtqVW0MzkNtQOmMLB0zyEZk2YAoDuqa1nptM22fq5RiMpmsSVXoCc3XVtI4iskGQ0ajCcZYUJKdvT2KqkJqRZTELFcLtIJ6teDTH/tjvvT448zmc2xdM5/PKauavKwpa8NwOGKQJNTGM1tWnE6XFLWnqkuss1jngpooBNZZirJksVgEspznzBczqmrB9Vde4NoLz7M4OuLk6JAkialMhY40SRQjPGilEcIjBVhTU1c5r776Ct45FrM5RVGAd5RlsQ5lWhM2tUl0bd/f9r+7dtpWsW4TabvvL2z6MzfY2HW7ZLT9t1W4W5WyXaYd4u5WkbQ3LrrWb9ico60yaTqqe7dypqt0tsveWkvT7UFt97ndT6UUIuQLI4RY27RvVYi7+9oqpO262hsx7Y2S9d+dO3fcuqR1c6w2n7f2eCml8NaiFZSm5nf+4I/5wpNfYXf/EnVVUZYlWZrgTIX0lpdffgVblESu4Hve+8286+1vYevCNofTJeO9K9z30Nu4ePk+8tyzWBVUtSUdZH+ar5AePXr06NGjR4+vG7Qpuav/kxv0y+USgPF4/Lrr+vCHP8y//Jf/kve85z38yq/8Cu985zsZDoe8853v5Fd/9Vd5z3vew2/91m/xO7/zO7dvB/4UuGPWXFMbhHCIWCLlphcyWCB1o8iE4CHJhpxqrcF3bZdg7EZhawkncO5iP8zobQJsApGQmyoQa2nCcsOFuRA4D3EyRIg4hB05w2q5JBsM1kSgq2JprSmKYk1k2p7IlpQKIdjZ2Q13XpwjTTOWqwVCCsZbE5bLBVkaM4hjjm++yqf+6CM88fnHWC7mWOdRjV355HSG8eCcYLGoufvyHhf3x9S1oSpzrGk6UrXCGkGR1+Q6p7ZRqMlxnmVRYoxhMMhIU4XJ53zpC5/i8OBV9i7fzcnxAXGSkcYJwntsXbAqC6wNauxqOSdJYl699gpKeB56yzcxPzliPBmzWKxQKmLvwiUQCiU1zjq82AQPdec4W9LWneltbxZ01ch1cNQtQVW3qpnd+cvwVp4nvd0alZZ4tYStS8JaQtyuu9tD2j3Hut2fbe9ol1x3b3B01d7ubOrm3PVrpT1UEm22O6i3Zv28bmVNsOZyrku0u01KKhycO0btclprqiYsqktefTNzHMfB3vvsKwf80aefpPYJ85MztscD0sGQ+XRJrCVaQFXXPP38s+h3vI2Pfv45rh3PmGxfJItTDo6OqazHGsjiFC9Axwmu2ZYePXr06NGjR483CovE3iZN7db1/Nqv/RqPPPLIm1rHhQsX/kzbcN999wFw9erV1/x7+/j999//uuv6pV/6JQB+9Ed/tDPqFqCU4sd+7Md47LHH+OhHP8oP/dAP/Vk2+8+EO0ZElVY466hNSaQVzobE0CiKqRs1L4ljfEMMWjLpnAMPHo+QwHqeTdDWrnQDcrpzecIH5aclhqYOKqpWQYeSOmrmVCG8jEBohdAyJNQmCZFWYU6yIULdVFHwxHGEaKpDIi0Bj3MWKTXD0Zi4sdNa46jqugklynHWMZ5ssVotEMWKrzz1FM889WXyIqcoK8qqZnuSsDNJGKQRBydnHM8rposKnSRc2RmjvGWUJcTJGFMZjPFEUYLAM5svUMoR6YTBcEJtYbEsmc5ydnfGJHFEHFuuvvwczhmi04z9CxeJtvc4PT3l+PCAIl9RVDV33XUXs+NjtJJcf+kqw2TI1nBEXpUcRZoiL8iyAUI4xuMdRDYEqVFC47zFerfufW1V7RDOU+O8X6fotiE8cD41thtMpFT7/I3KGiym4FyYPzadzs+2G7a107bnllKKsizXwVPd3s4NqXVri3VtDTTdsu362+28dYa0S0LDTZPQgeu8RUixJo3hi0JijUOrUDdj6k0/aF1XzVy0Xe+/9249v9rtEm3P/9Z6HGqJBHQI6FohNSbMYwvR2IXDXLapaiKtqaznC19+iV/6lQ9wdDIjL0q2JmMipcmXKy7t7/PcV55H4IljDyLhyWeuEcUD4mxIbTRlVVE7qE1NlqZsbY8RIsxan56e3L4vlh49evTo0aNHjz8jHnnkEd7xjnf8ub7mu9/9bgA+97nPvebf28ff9a53ve66WtK6tbX1mn9vHz89PX3T23k7cUcL/KQKqtFqtSSJUryXGBPm9YT9anWnVZGgDVpxSK1x1hKqOfxa0WkvwFuiA3yVnVJKhalLkBqPozIV1niSJCWKElCKKI5Dgq+HqqxY5SuyweCchRFvkcJj6mAfFQiMqYMipcPsXJom7O7uoaIIZz0qjlBaUZuaNE2p6xpjDKMs44knv8inP/VpZvMVxoXuzaLMOTrzXNodMx4OSeKILJ1z47jg5GRGpiRaCVb5Eh1rJsMRURRjvUMrjfSBwFhjmE9npOmANEowwjKbFcRxTRortrYmnB4fEicZ06MTrPdMz07RSuKdZVkZhHVMz86wtWV+POPg6jW0gLwIYU2L5ZLt7W0WsxkPPvwog+09RtuhlxTZJOj6zfsBDfFrVMhuZU+rTLY9oN16lGDTbc8m0fxsgq0AVJNW670njuNzAUKw6QPthlp1z5mu8t6ef8ZaRCd0SUiBkhtVtN2fTVrvxuqrpATRWpaDUmnMedtw2yVaVRVKnldz20CiW22/xmySd9vPTff1aUio75DjltB774mbYwttOnVJFEdUtec3PvRHfOD3P86iCDb2nZ0JWgomkzEvX7vOav4SO7vbHB0eIH3EyckZ9933MLNFQVHNGGQDxpMJ2SBjtVwwHGUIPMZUTM/OOD46um3fKT169OjxFwln/+mNdfl9a3L2hpZLhXrdZT753l9+Q+t6w/iO11/krT/8/35Dq3rLf6pfdxnxyS+9oXXh7Osv0+PrGt5z22Y7/yKYs777u7+bra0tnn/+eR577DHe8573nPv7r/7qrwLwwz/8w6+7rsuXLwPwmc985jX//ulPfxqABx544E+/wbcBd4yIdu2W3nlqU+GxTY3EhjgC5y7k23TSliBsAlc26aPtslEUnSMVno1K6n2gLcGqWQMe40EgKcsSdIQWEuU8vrnwT5OYTECappRlSVVVYf6zztFacfPgCOc8ly9dwVjL4dERSRKxvb2FqS2z2TPEcRzmU6UOBBFw1oV5TmNYzs74zCc+yenplKIwVHWYVd3a2maR51w9OOLS7hbbowGX9yMGmeV0tqA2NSpKcCjOpku0joidoygqnPNkqSaNNXEk0MqzWC5QKoRBKRXhnKGsLau8wHtBvipxgJKSLI44PDqirGpWZc3sbA7O462nyEtOj09YLWZICc5a8jxnubfHYjYnn8257y1vIxtkRHEGQuIB5zezm+17vE6tbYKLujPDXYLYvoctuvOc3bTbbrhQS2rb86pbBXRrDUp3Pa+17jaEyTaP6069Srtsq6x2K1fCOSswtlPX8hopvJsZU40UG+W9a7dtX6c7b9tNZj6X0kxwEPjXmDNtj3mrBod9rJE6Yl46fuV//DYf+/SXWRZQFDU720PyfEVd5ly6uE+WRVQry/HJTe6+715eeOkldvYvouKU++6/TN1sw2KxQHjL9tYY7x35asnxzQNuHh52HAU9evTo0aNHjx5vDA6Ju03W3Nu1nj8L4jjmn/yTf8LP/MzP8I//8T/m937v99ZJuT/3cz/H448/zvd93/fx3ve+d/2cn//5n+fnf/7n+dEf/VF+9md/dv34j/zIj/CLv/iL/Lf/9t/48R//cf723/7b67/9+q//Or/8y7+MlJIf/dEf/fPbwdfAHSOiVVML4V24II50W3Fh1rUssLnQNsYgAB1p4jhZ2xC9ON8v2bUnOmtRTeAKNEqZC8mpQghiHeOdb2opdJgXNcGaqAcDdJySJinGOI5PjjCNJbK2lqoo+OxnP4uUkhs3rrKzs8Pe3kWSeMBsNieKYy5fDvUms7Mpo/GE2lhWyyVKN+mv1pPE8fo2jBLw1Be+wCsvvsxyucIagRSaoiyoqiVJlqF0yuF0SlFZtI4RwjMZRSgpscayt7vDxEwwdc5oOGR/7wLT6ZwbR8dIIUgTzXiUksSavCwDQZOKLEsQjapprAUhQxKxs+R5waoosT6kFQsWaKnwDqxx+COLkg6tFbVp7lAKyXKxpMwLoiThwpVLRJkhyYYIpZHivOoI58OA6Nhp4zjGGLOeHX2tLtlbK2HW85hdW2zHbtud0fzqapRNynK3CqZLSGtjkarTudmxxLbLt+S3q1B2O0Wd2yQLd7ej2+fp7GaetasGd4lktwN1fZ7fMpO63kZ3/nEhxPoGTcjM9XgBL796yK/+xu/z7As3KLygtDVaB2F1PlswGmZ88UtP8vCDVxhd2ueVo1NePTxmPNlhvLVLWVtYLpuU5WBr19JSlyvmixlnJ0fMTo6Zn50xHk9u51dLjx49evTo0aPHX0r8m3/zb/jQhz7Exz/+cd7ylrfwPd/zPbz00kt88pOf5MKFC/yX//Jfzi1/dHTE008/zfXr1889/iM/8iP8+I//OL/yK7/CD//wD/Nt3/ZtPPjgg7zwwgtrlfRnfuZnePTRR//c9u21cOd6RJXGOo9AQnMx3SqasFGSpJQ4UyN8IA+m9rRdkGsLrrXBciiD2raeCZUipHmiN0qQ86RxgrE21FNEEd7roChiQcbIOMP7iNWyBDRFWTIYDsmybJ3OW1vLeGuLV69d4+R0yksvv8D+xUu84+3vRekEbUpq6xgMU4Zb26H+Qoa6DaE2dRpCxCjpwFW89PzzPPnEY+RVHtJuhWUwyNjevcJ8PuPg6AglBbFKWBUVSarAWcoyRyvJIBtQFAVKalQ04Ph0yu5EcGlvh8loxOnZGUVZcHq2YDAcEOkIvGKalyzykskgItYgtUKIMLdblIaiKMmrGi8UWkqEkNQ+1LKAo5qVRJFEK4mSkkGWUFYFzjuODg8Yj4ccPrdD6RUPv+vdJKMtQjeof03SpJTCNqSzG57TtfJ2A4i6SqLzoSM09MZuZni7fZ63Eti12tqqnRB6N6XEdpTOlkQ659BKrsm0FxIam7lo1dpmnV17+Nom60IvqhQSdFDEuwSzRbt8azsJ3bnnVdGuXb1rcwbOBS1JEezQvp0D7ex7WZYMspSqLBFa8+kvfYn/7Tc/ysmiYpnXICMEjmyQ4qxla2uLvMgROuLqtVPe/o4dTuZLdJQwGY7YmmwxnS8QIhyDSENd5szODpjPTpnOjlmt5hhjqY0ljfe+Nl8yPXr06NGjR4+vWzgE9jZZcx1/Mepb0jTlD//wD/nZn/1ZfvmXf5lf+7VfY3d3l3/4D/8h//bf/lvuueeeN7QeIQT//b//d37wB3+QX/iFX+Dxxx/nscceY3t7m7/5N/8m//Sf/lN+8Ad/8Gu8N6+PO0ZEhRAorVBCYqpyrWYGZTI+V/0BdEhq2yupN9UgzuHbMCM2lt62y3JNeLqJuWzsoIHEKJxvKlmSjChOyLwnThOU1mgtKIr8nLp18eJFrLXcc9fdHB0d8MrVl/nYxz6MsQ5nDI889BA/+APvw1UVeIeQCuVrMB5XOYz3CGNYlguquuRLn/80JycnlFWNsaEGpaxKJpMJcZKwv79HscqJtCZqOiSHgwycpTIlZVmRqCiQDqASlqPTE2bLM/a2ttnaGjBmwNlsSVHUlHWJjjS2sUdPlzlaOYRS4Th7EVTPhoR5D07SdEs6hPAUVUGSRHgZ3s8sjhiPhsSRbkJ3BCdHhzz5xceI0gE7+/vs35eio+RcmFQ3ibYloF1F8VYr6Wupouv3G84R11vVyW7Scdfy3SqwbTJva7dtCea5c05s0ndFk8TbnTFdq9y3VNW8lvrZktBuXUy3axXauVq3fh3fWf/mHD4fztUl6EL4c0S+Ha5VSqGVpCpWCKV58ZXr/G+//gec5ZDXBuMsykMaa5Tw7O/ucuPgEK01Oo4ZDUc8d/WE/b0rXBiPyauS2XS6thwrIo5PTvDOsji5wWI+ZbmagfcYZ3ACxJ13w/To0aNHjx49evyFQJZl/PRP/zQ//dM//brL/tRP/RQ/9VM/9Zp/E0Lw/ve/n/e///23eQtvH+4YEQ3ptC4QGKUBd+7Ce3NB7ZBic5fCE8hGSz5agtpaN3UUoRoiIQTg7Pri3zu/tlB261faMJcoTkBqhGy6Iq2ldpayqhgM4nX3aBRFrFYrrly5QpIkHB0eIpRmvL1HlA25dvUlpmenHBwMuPbyS8yvX+fw6ITjszllWVCbiuVyRVmWeBwXL+5w8cJFDm8ek6YpSMUqL5r+zJrDoyOUkkRxHEJ7pEQqSRwpvK2RUjBMUwZp2hxLQRYJpN4iLw2rfElVV7g6qIVpHKOkpjQ1tTV4b/BIHICKsHjqylCWNXVVEUcRg2yENQ7lPVJ6pIThMMX5CKUCSVNCMkhjBlkSUlgzRZqmaC1ZLmbosuArT3wR6+DifQ8Rp9n6vWgtpl2r66bOJ1qrg10i1+Jcncr6/Noony1h7XaVtupm+1j7nLYyplu70p4f3VnSlsS2zzs/i+zXtt6uKtr+/n9WA7OuI2rIZrv8Ji3anQs1ei2bbrfSpXtsZJPOu1ZJm9cH8M5hhWBZ1fzKb/wuq9JTmXBeSDxKeLI4YjIecHpyxHCYYReeLB1QWEeUTRhEiqquWSxyvPAoLZmenFCXJThDXebkiyn5aoVv7l4qqdBa4Hz1Z/9C6dGjR48ePXp8Q8F5cdvCim7Xenq8OdxBRRRadVMpRVWHRM42HVVKH+Y3CYS1vbBu+0ClkCA2NRtxHMJhnHcI39orOUcyvHP4TqKqEE3IkY4QUmO8RAmBx+OsI9KaJEuJtEZryXw+Q0rJrJ4RxRFCSLa3d7hy1xXe/s5vxgvNKs/5rQ/8D5558ss89OCjHN485fjqi6zyHJwnizxZBOM0BZFiVcz+hUsc3jjBuUCGrYfEeqq0oixLnA82U2EM6SBpLKFQ2xohNJIQuCSFxBPUxMJU1EUN6FAX40Idh7EGXAUIrKkoqxIlIM0GxHGClFBVJii4XoJUSB0RRZoklmRakaQx1tYMs4TJZBSs1c4FBVTroEo6R20tEN5TYw2R89y49gp1bRhPJsSX70bIEE7kAaFCOrFSKsz3Nspnd66yDfJZz2y2xKtRQ12HFN5KELvqYjeReW0Brutgx20UUNmEA4Xt7ybKGoQUzUnsz62z3Xbbhip5tw4u6hLcrlrbDU3qdqC2pLdF2E+HM/ac0qpUeJ1u5k/X8hzQhjJtPkutfRkHOo75+Cf+hGdeeIUHHniQ6sYNdsfbGCfJF0sefuA+zk5P2dvdQacZRW0xHrLRiKKokHFMbkKNTlUXLFZzrCmpiwWL+RnO1jhjaLqXQjKwlngRlP8ePXr06NGjR48e31i4g4poIJoeR+0dztugFkWBEJxTQRulxxizCZnxgRj4phpxXdnhPVVZkSRJuPB255Wx9l9rDd7VeCGpLEwmW6goQWtNmqbkyyV5Hn6EEGRZRhqnRFGESz21NRhjG2VWsrO3x6rIuX7jOvEgZrw95IVXXuDbvvWv8Oh7/xrDRKGaXdJaNUQ81Hk885WXeeGFawTHayA3SsFkOMRlGd67zv5JBGFWUGACKZeKSCUIsVEMa2spyorKrKjqmro2eK/wHqI4hMeMs4SoipnPlzjvsdZQlDWrVYH3oGREojLSJEFJyfZ4yCBJSZMohOgAgyhZW1orYUBAURRr4mitXSey6qrCGYuUcO0rT5FOBqhBmJ/1hNlfvF+/991021aFvPUx15DY7nt7K/HsKohddbK9idH+262PMcbg4Zzd9ty6O/bgdv3rKhgp1yTT2SZIqSGFWinq5vW6dvTueQ6srcRdpdh5MKZJ8vWcU129D6FDeLA2BEeBX9uaw2aLcIxxeAmutsGirhSrquDjn3qcve1tqtUCV+ZE4zHW1YxGCfPZCXEUs7W3x6uHh+hsgBMRq1WJlJ6D41MGaUaeF3hbUy6XLOdn1MUKZ03o8PVi/VkFj0RjDOSrPoK/R48ePXr06PHmEBTR25Sa2yuidwR3jIgqqfA4BKGD0foNObDOEyl9zn4ppSRJEpxz5Hm+VoqE2gQXtYpqu1z7vNbGa2+pAAkdnRFJMsQDVVWt62Gc8+vI5NayuVgssNaSFwU60iitGY/HDEdjpAoE9q1vfZQrly9z8PYDXnjpZT7woQ+RxRnvfc87GQwGIa030ggZEmmzOOLqjZtYUweLqxQMsgwbBbJHZ67Q2DCvaa3FGUOShI7S2lisE3hnQw2Mtc1xdFgPQcwLXaZplpCmCUpqlnmBEA4QnJ6eoVVQn60DpSKUEkRKMR5kjAcJo0FKpCLSJMYZiWDTRRlIom2l7vUsZova1MhKYWyNkI7nv/wlVBJx5eG3Mdq5iHCSVl20zp5TPrszoV1Ldjcht1W+u3bXLtnsksXXqoxpn9uSwrZGphtmtLb0RhFVY7HtvtZ6TrV5rCW1tjYb8tsQzFtrXtYW8eb3W6tmrLVrG7Ho3KRp/1s26bcb8hzU6K71d52g6xxKhdlWCQgNxwczpodnXLqww/XDQyRwcvMmUZpSFCu27rmX47Mp87pGJjFSBRIplSaJI8o8zCh7WzM9O2G1nDGfniKtRSmBFBACqlsbcWPN91DXPRHt0aNHjx49evT4RsMdI6LWuVDfYh3uFjIQKw3Or7tAW0LSXkwPBoO10uOcW89utkQBNmE13Vk8622wIbbKldIgJEmaEiWDtbKWJAm4oCYtl0uklFRVtZ4PFVKiIr0OtjHGkC+XHB4fs8pXHJ6ccHZ4RrFa8bZHHyEvC16+cZV0METJCCEEO7vbjEdDoizFuQolwgW6aJqMhBQIGYdU4YbwhX8NUgnqqsJ6jwOKsqY2FUoGghOqbkLaqrEe5yXWO0xRYYUjL1YM0iHb2/vcNZ4ggOnZMdevvcxsPsOYGqU049GIJFIkWjIapIwGKYNsiLeWGoep6w5x3wREte9lO+MYalckOhLkq2DDtLbi+S96TG14+B3fhlYxzjt0EiOlWr+H7Tq7hK8lau28pbwleOjWlNpu6FXXrtslu8BanazrmqjpKqnrmjiOqTvhSG0abzccqLvului2567qKKSvdb+tJazd9OBuWFL3pkr7Oi3WpLSjugZCfZ6onwv+kkGRV0ohmteZns1xwnD94FUq4xgMR5SLgrObN1mtFsRKM10ZRijsokCqGOkBIZnPz9jeHjM9PeXk5JD57IyyWGJtjRfh9RAe7GbvlZLEiWZ7uI2S6et9XfTo0aNHjx49epyDQ2BvU9rtX5TU3G803DEi6qwPylczZicERCLMwFkTLt6dcyyXS9I0XZOdtY2yUYmwjrqqUEo3zwnzne0lf9s9GeyRgHBYV4cEW5WSpEPiOAMkzoWqkqooqeuKwSBjNB6G5wpBWVYs8xWj8YhsNGQnjtE6zIoORiP29veRSoXAWesCMWCT6BrHCe2sXhvEVK5y6sUpN6+/jHWAbAJyRJh3xTukUODAeYOxNdJ7dKRwlcE3g9rWBuJlXailsa5NX7XreVwpYbksECKolcY5FssZUkm2JtsMR9uczeZ4IZFEbA2HXNybkMYRWkKkNFmchPdASJa1ReFASHwz02tdmAN0zuJc6Istyoo4ThEh8wdTGQoKzo4OUc98icXRATJOyYZb3PfgIySjMSrJUFKDUBsF25pz4VRdkqaapNq2hoWG9Ak4dzNiPfPZsXnfek4ppTapr0qtlXRgffNDRnqt0sKm47Orzq5tvM02OefW9t+WDDtniHQbLiQ6j29U2K8KQ2qJpQhdpOv/bm8CNKc6bVCREDi7UR21UuANKMU8r/mTT3+RT37mMU7mK6Znc7Is5uC5rzBMBwwHA/Z2dplOZxzPCkQ0wjiPFwXZIAuBWVXJwatTinzB4uyYsshxzuIR+ObWirUOb8M2RnGEE5IoHfPo29/NpctvLIq8R48ePb5ekQr1+gsBiYhedxl5my+o1RuINn/hh/7TG1rX9Afy113m23/xn7+hdT30k599Q8v5ug/E+3pFH1b0lx93rkdUKGhIWhgck0EFbEhpqzR1laIW7YX5urrDeaw366Ac5xwOkEoGBbMhsXUdZgytd0gVk6QJDsFqlZNkGc57oiQmSRKKQgaitlgwGg0ZjMZs7e6SxAkqCgTpVhvoufTSaKPmdbddSgG0REWQpilvedvb+NynP8XBtSOkCJU2gTuaUG3hTEOsLS74GzFFgXVQG09Z2VAZYy3GVmGmVIZajjSVxHGEFhqlJTKSpElGmqREOkYIyenZIS+88CJ5aXAiYnsy5uLuDonyJJFkNMyQQmCrmrqq1/sSJwlVWVDXQfmqjKGuK6QUjTU3zDQKEcKonPPETqGVxhrPcpEj5SHL2RmR1MRpxtn1F0kGI3Yu3MVka5/x7kXSrS0QPoTbALiNQtpNtu2G/riGSK4fayypLfF8rUCjlkB2CaAQgqoKM8etCt+dJe2ej7diE17kziXWnq9cUSgp8d4ipcKz6dTtrntdG9PZbk+4ydBaXa111KbuhBhtXltLRVvlAiBUzI2jE/7X//03eeLpFzg+Pg7VRypCxhk6HrAsDYvVKYfHxwwnW1TWURSrQHCVYrU0rKwF4VktZlRlQb5arvdNSdmkhjW2Zu/BO7I44eFHv4lH3/52ssGYouwvEnr06NGjR48ePb7RcOesudaCdwzSBCnA1NVXkc32p9u72P7bLhvHMXUdyFEbitNe8Nd1DVKsKy+UVoBECkWcDBlNttFRjLGOKI4pyoKqrlg1M6iTyYTd/X0Gg8G69xIaotJJ5G1nG7sX+l10lawQGNMJYlKS+x9+G3/n7/3f+e1f/22e+/Kz4D3Cg8eCCOm43juU8OtgoNrYkDwqFba21KYCgn03jjVJmjAcpiSJJo4j0mRMEidEWUpVGaZnMw5uHpDnJTqC+bKgrA1pnLK/s80D914hkg4t5ToFdphllGW5tuMaU4MQGG8xxmKspTaGsqxwzqMb6zOENN/KWPCgVB3IJJKz0zlJEhPrGLUqmZ5NiZOI0+vXGIx2ufLAI1y6/wHS0Rh0UNKE0F9Vx9Lt/OwGCN06O9q9MdAqpHEcU1WBDEVRRFmW6xsgrfW7Ja9tR6gU8tx52A1Eaklsu47uOdudefU+aIbhvE2aGzKsz9eu3bhdXzc92nbVUDbzol0r86bWJjgF2oThk+mUD/3BR3j2+Zc5PD5lNp9x6coV8spSVoZsMCJNEgSO5WpBUVsGwwG4EuegLkyjukq0VlhrKMtinV68/gw7T92S4STlyuVLfOu3fTt7F++iqC2LvGSxWr2p744ePXr06NGjRw/n5W0MK+pLze8E7lx9CyGtM8zlbXoerbUkcYJjoyJ1Q29aq22rRgFrW2U3pMh7H6ytzYW5MSaksUqBimKywQghNKu8pLY1ibPU1jAcDhiNRqTJgDRNG/KhCB7iTf+plJsL/i4huTWx9atJwa3Sv0BGGd/0zveyt3eFT37sj/jiY1/g5sEBy0VBXhRYDLWtcXWBUlGTfCpQkSAWMBgN0GqEtTXgSJKYbDwkyzLiOEEIwWpZcjqdcnD8AgfHJ5Rl3SS6SrQWTAYZwzRma5QyGkQ4UxCnA2hUrbqqyD1oLfHeARKlFXVpcN5TVhW1MYGMWodAYaxr7LgKIQVNUwh1bRAOJArvLVVVI/WCQZahpMJYjXU1DsfLzxfMp0dE6YDJ3kV2L10hGm3j16SrmXVsjm+otjn/XpjOfGdZVkSxbizLYk0ybyWeXSXyVvXVuTDLWlcVugkbWqufnf7OtpaGJmyqJaVda669hby2dlpjDHEcr8+SaN2NKzapz94jVUchbfY3zGSeV+OFkEjVHBPvsGVJLCQXtnd58aVrDAdDIhURaY0QntFkBC5YxAcMqaZLpPfYuiDPS2pTg5SoOMHaEJollSBuk4x9IL/ee5I44b777uXBt7ydSxcvUdQ1Tz/7AoPBmMrU689Wjx49evTo0aNHj28c3DEiGipMBM6JQFhUuNhWQmIadSrYE11T7SHWxLSqqq9SRo0xYcVCYJ1dE4k2YMg5h7EeKSJinWIQxBoSqUmIiNMUJ2Fvb48kjgmznITeURHoY0tCAqEIfaZtcE5H5DxnF71Voeomq/qmb0MgkFHElXvv4+/+3/4+3/c3foCjoyOuvvgKLzz3LFdffoEbr77CdH6KtQ6po2AzjSRxpIkiRaQVsYoZDzOUViytZz4vmU5PODudUVUltfPMViuMC8dBCgVCECUpg1Rz78Uhd13aJ9IRUoDFMUyzEHzjLMYZqMP2F1VQPZerJcjwLpW1wSPxIpBVU1uUCsRGa40SAmcNFQYhPLYM86xSSqJYU5sVw0GGbAgjLKlNTVUWeK8Y3rzJ9OSY+x79JgajbWSUYqxDCt/MntaoW2Y+2wCi1sIdt0qqFuBdoyE7JBLfqL+iCVmKlEB4QW0q4igOKn4TBOSMJVK6Ua7PK65ShIhYD6iGVLYEsiWh0NpsA0lst7m9adIST9fWuDiHdSbMV7pgh9WyCSRqyLht55I7N25k87rn1VvFxb19fvxH/w4/VFh+8mf/PS9ev8liUQCKRAm0gLzKmWxtUVcGQUW+XOE8IDTOgZaC2lZUxqC1IE6zQLjzCikVw+GAu+65j/vve5gkiUBrXj06ZTqfkcQxSRpzejDFWvM1+Y7p0aNHjx49enz9wnP7Qob86y/S42uAO6eIdlUmRAgpsqCkQDUqFdBUTPi10iSlXFspu2m5m2TcTbhLWyHivSdNU8qyRkURcZIQxU0FhTMkccLu7j5RGro4W+VKSnFuVrAlE8aYc3N8XXRJ562Jqrfue1BVO49JgUhi9i5dZPfiBR54y0N8+3d9Oy8+/SUe/9xHePm5Fzg7m5IXNZV11F4R6QjvHbUBLTVF5Ti6ccThfM5iWWBqFwKcvMc4j/XgXSDXXnviSHB5O+Ghu/fYGccMB1FDHhUCSV0bqqrEOcdqtSKNNKtVSWUdQmiq2mAbJa+sDVpHrVGZNjJKCodxBuEF3hsUklqYNbkPKchhxlUrGQKaZAhaCu+hR0vN4Y05q9WMQRYz2btIurVLNt4KtSCA1ALnX3u+cm2d1TF1WRE1JM7VBq1jitqiVBM8lBdESuGMQzUhTUEFZ90RiuvMpXYClNZzmWzmObu1LK0y2xLSloR2Z6Fba67tkFPnXOjYbUmtkOcriqxDChGCkW6xuN96M8SYGoEDqfnIH3+Up599hnQ4IV+uwucCSZEXZEkMzuK9o6rK5vhIfGOrtsbivEAoEcLCdEyaZuxMdtjb20enKVJFLIsSrySr1SJU4ljLhd09rh3cZDIaUC5nf9qvkR49evTo0aNHjx5/SXFHiejmgnlzsWyaC+p1b2Ngp+tZUQjqT7d/cW3F5LUv+gGKokDrmDiO0VqFubw45uLFSwwGw2B3bTsZm9duA2lER9XqXti3M4Hdfeq+9vl93Kih7XO6BLrd/nY5gChOkF4wHo+QCBKlSVQEMSRCU1mojaH2BikU0/mUxTLnbDGn9mH9gT158BIhPMI4lBBIBdvbAy7uDLhrZ8QgEuAgzyusXSKEJIkT4ihpiEiFMYbC1OgoZlUsKauC2nqMMxjnqGsX1MV2jlJFeBxVHapvtNCAx9U1tjbrGwlhPtOj5CYZuVWYlQoBPkkUgXeslic8+YVPkgwnvPUd7+bKfQ9CPArvm1IY72nDoFplse2d9d5TloFkmrxCKUEqFaIqEThUDS7W1DLcENGrBfNr10j39pGjISqKMNZhEOi2mqVJqTVNMq+1dhM2xXky3Krnr0U822Xbc6Gdd+6e876ToGusQbI5Vzck05wj4t3zrN0GIQVCSA6OTvi13/odHJKyKMiXU+pcUsow96mzhLOTY4yXDAYDqrWdOyjQUkoGwyGX777EvQ/ex2i8z3w6x1lP7Rx5XpIqzd5exmy+QPrgbtjd3aaqCuJIUS1PeeFLn3mzXx89evTo0aNHj29wWC+wtynt9natp8ebwx2sbzGd2TZHZSxSSBIdh7AeKUPPqA9qno401oZeUOc9ptPrKVVQabwH18wptrUVtW0qNjx454galWaUJWxt74ZaEaWbC/U2lVYilQ6KX2PtbGdYjTFopUNYDRvSSfP7OQVLtfZjtyag7RxsN1CnS0yVVIhmts7ZmunJqzz3zJMcHByzzHOEDoTRWws+9I56LKt8xWpZkBc1TkikV7RGAynBCU+8ViIFWZawN465vDVkGEcIFEXpMK4OHZDOoJQkTQYM0wHOVqEeRIdO1UCGKioTSKqKJHGaYKxZ+xtaJdu6pshD2jXBRLKuRTHGoLQOFosmRBkRZojrRnH1HiKlqMuSPF+h85znnnic1XzJzpV7mWzvItIUJYNtOZw/GlOHxFtnHVoIhPdEpkYsZ9j5jGI6g+kcYQ1WKdT2FsOdXYpVzo0vPY6eL8kevB85nlB7idjZIrm4D3LS1Nxs7LhSKUxdh7lYT3hNpbBuQybbNN/uLHFLYLs3MpIkATa2XwiztVGkMXXjFlDy3LlkO+u2t7yOUorK1CghqPOCGwfX+e0/+ChPPvMCaTamrhbURU4cK5RQeKtZzucsZjNWtSFKIobDMdaCkIr777uf97znXWzt7RNlMctixdHxklVR4Tws84JIKra3xkjpkYQKmThNmS8WLJdLRmnE888+i817RbRHjx49evTo0eMbDXewvsUhlaSqDFIEshlCWzxSKXzopUBIAc6syagx5pzt0XmH8DL825AB3aajutDDaY1tCCPM54qdC/cy2d4mjoKNdJXnxHFMFgcFyjRVI1IptA4pqiGNNkZKSVkWpHESttMFlUc2ZLJVyVql6tb+ydaWCZvU3ZY0SBmspVoKTF3y9FOP8dRjn+f04IB8mVNZgzE11lhq4zFe4q3BmoqiKKidwyLwLoQZtT+BNIf6jCiK2RoPubg35tLOkExJJOH45WVJXhYUZUVtDUorxMKwO4TdccRomBJrRWUqKiOxtmY1q7DO4IwPfazO4UWjGhNswYKGnAOy6SPzQmIA6X2Y7XSeOI7Ii5JaS9IkwSOpjEVZj/QSrwJJK02NKC1l8QonJydsv/Iij77jXYz3LjBIx6EOBQCFc5YkFdiTE8zBAWY2ZTE9Q+QLpLVIIVCIkPAbxeiqwp+eIk3Nvo5ILl1CVwZzeJPYC8yNm5iXriKuXCR96EFWUiPjCC0kVVWS6AhjapwUSK2ofd3cXGhIZsfe3e027YZvtWp/NzXXWkuko5B8K3VzEyMQ3K5i366vG5wkpQyhQDhODg956ZlnODi6yYf/6I/xIkbpiCiKOClyirJGYslFSVlXQc32gLdEqebb3vVe7rn/IeJkwLwoWFQGm9eUVc1stsQ7KOuK7fGI3a1tVvmS5XLFXRcvMZ2dkRcFUgqEd1x9/hkOrr+C6kuke/To0aNHjx5vEp7bl5rr6VNz7wTuqDU32GBFM+/Z2BAF4MSasKz5FIGwxUmMrc05i6zukMCu3VWoUPXR/q02jvFoG4DlYsrCO7LBCKkjlssSb1LSNCWKIlZFjmqUUKUkdVlRuRKtFGmSkOcroigiTVIcnAub6SaotkpXVxltSWp7HNp5WKUkzhnqynD1pZe4+co1IuDs+IiTk5usihWmhrryeBxaSSKlSaOYlapwlcMTCDx+Q0Q9ikBILZPxgLsubrM7SZlkCcJ5qqpiuVpSmTrUwkiJ9JqqsiG0yBq2xmO2xhrpPUkyYTqdkaiU2tYcWUtpajwG4QMJlLLRan2YJ4SQmtvW19gmpEqK0B0ro1CJY2wg9pF2LJercExEUMdb1S9fldS2AuExh4az40PmJ8fsXbjMhQsXw5ypdexM9tm5eIXq5Zc4e+oJ9HIZXq+pvdFKo3WMjnS4sSE9whhsWYJzRAhcVVKWBZ6gqGPBLudweoPq4BqD+x6C/X3cICPWYZZUqygkNnuPsA6hNc5u3u+WKEK4QdF23bbnShtYdGtdS6uOtjc1wK2rYNrH23V3U3qtDQrx/OyULz/5RVaLGU985XkOjufoKMMDSZqRpAPy1RzjLeDxBiaTCW95y8M8/MhD3Hv/A9w8nPHqjQMmW7usCsu1669ijCFJYrTSJLFmMJwwGY3J85yiqhiPhxwd3uDmzVc5W8wQCGxRcHrwMtZ5SNLb/v3So0ePHl9ziNe/iXZwMnlDq5Jv8CJY/iW/cTcSyesu8+n/58+9oXV9O//8DS330E9+9nWX8XXfZ92jx53AHe0RbQlYkiRYa1AyAgfOWZwPF9lKBBIlRFDDrLFEWlOWZVAoG/LXDRM6HwwkESJYE+Ms5dKlu3HA9asvs1oumEy2uXj5CnGSUfgC5xxpmpIkCXmxoq7FOvF1uVwS6dDRKQTrABcdxXi3sUCug5Y6M6JtYmpbPdPuf7f3NCynOT06wVaGe+97iOeWK8ajCSfHhxSlYLUMzxsMIqSKkDpmVVYUpaWoakDjCeqqbudsnSFLNcMs4uLeiO1xzCCJqKoKWzmsqQFJkgwYjUMKr1QxZ9MFx6fH1DbHuyHSC2hswztbKWksmK9ijmclzgtM7VCiSTMWEi9DzUs4NA0p9k1Akw8ptlJ4htmAKI7x3mKcXXd5ZlmGdyC0OGd5ttZSFGWwvArBqT0jzwuW0xkvPvMkgyTh4bvvYx9NtVhQ37hOUuQ4IVBSEUUJSZyCCnHIztiQ1GwNzoVO1DWRbC80fPgxtsYZi8ejT6asrt3A7+6id3dI9vbwaYKaTJBJjBEalaSYqkZ2bNq3znCu64U6Nyi6M6RrVd05oihan1/tOXVrOFN73rXHzHuHNfDCc88zPzulNDWf+9JLWKmxziBExHJVkOdF6AVVnrvvvpvv/b6/xoMPPsh8UTCdnnBw4ybTheP4dMrehX3McsXZdIoWkssX9pECFvmK1aIgz3OqomAwzPAm5+oLz7BcTInSBOssp0c3qSqL1gPGW3u38ZulR48ePXr06PGNAOcF7jbNdt6u9fR4c7hjRFRJhRBgrUPJYCeNoxgaRc9aE7onvVtbPK214D1VHWJ9qroiEslGhZRqPQvqveuQCIEVkrvuvheUQuDQOqLKS16dvkhdFtz/0FsYDIcURYkXgjiOiHXMcrnEGUusNYMsYbVaYUxFkqUYY1iuVgyGEq2jNSloiUNLStvHu3Om7d+66bveebzzWOu5cPluVssl3/bd38M9997D7hcv8bGPf4p8dcBwGBPHEcu8ZlUsWBQF9ZqcWDyO2osm3TQQlp2dLS7vjsliQZZovLPkeY4zjijS4S6rD3dllVRoJdnZGoWKk7rA1DWuhqoukCToOGKYafbHEa/eFCxzqKnxSgASLUJNiBeNdVlIwDdqLUipcBiUlFR1iVQC7x1KSYraEEsdan2aEKmyrpEyVPNUdR1W1SitVW0w9ZLVKkcK2N3e4mx2RpkN0E6CqRBaM0wy4igCZzFVSZnn1FVJnZfUZYU1ZlPF0gT6KCVDirDakEG8p84rjCkRpyfow5tk2RCzNSYaj3DDEXI0QoxH6At7qGwEcYJtzk/poTYWpXUIOjI1Wgc7rmgCtzwO7xxKaVwzFx3pMLPc7dfV+ryaKoQ4F3TUEtLTo0POTg7ROuLZl29wPC0ZJgOM83hhUXGMTkZsTwa8/Zse4fu+5zsY7+zy/IvXeO4rLxMp2B6PELamWM64eXiDa9fP2BkO0bHm5PSYo6NjnBQkSUJZlqRxRF2seO7qS7h6RRxrTo9usFoV2BqkjLFI8rKvb+nRo0ePHj16vDm4pqHhdq2rx58/7mBYURPO4j3WOKqyBC/QOkZp1fSBhhoPON+/GYJsWuUnkJrQ6+lxazUSvLPgJRZPlA3RccpiuUTiMZVhazLm6GjBcjllPp8SpxlJmlJbizW2seGmLBYLnK7XKtVisUBpxWg0ojKGujYopc/VdbSpu8A6tKidDWyXaStmWmJRVxXeWCbb20H13JqgpeFsVXLjeMoDDz2AUo4iz1kuS/K8orI21If4oBTWtgIE1kukkGipiGKFswZT1xgpyUvwNpB3awxFWYAIaqUsCpJBShzHIARVVeNt1SjSBm9r8oUh2hqjlWIQK7Q0CBzWWawIdR9esA7yARDKN0E9oJTGGItaK32eogjqcjhGliSJG/VQBoVcCqQI9tQo1ggpKYqSolHxpAyGpShSLPKC0+WcVb5kkoxJ0wHegylK5vMZi/mU1WKBrQ11VWPrOpyH1gZC3hA6FWmUbiy8cYSOok2/pzNUZYW1Fl1VmNIQVSVpUZCWFckyh8MTqpdfRYwGqPEINRoSZwNsnKCHQ6rmTl6sNB5P7S06lNqs+0OtDTdUorb/tEmCbhV0IVpVNHS3tvOm3XPPGENZ5mitQCSsioqL+7vMlhWnNw/QccTFK5d597v+GjvbF7i0v4sl4Q8/9gVePThha2fEcDTi2eefZ3c04D1ve5Dnrt1EAd/8tgf4/ONPMF0ucVZiXUUqBZe2timKJVe/8iwRDmMrlqspi8USvAQi0sGoIcC9NbdHjx49evTo0eMbDXcurKjtYGzISBTF607NNhk3kE8BbCpabp2v9N4RWkoaMqIUxtjNbKn3KJ2wd/EK4/EErTV5vkIARyfHTUiM5+j4iMF4CxXFKKVYrVZIIciyjEhr8nzJYDBge3sbay1np2dEcUwUx5RlqDaJ43jdD9nO68VxDIT6mCiK1iShSypagq2jCCcVWiukUlSmZLksePs3v5vdnR1+7Vd+meVyQVXWCBSDNCWVGcsypzYGL0SjEoYCE6UEWkuSOALnWC6XCBmjnMV7sJWhKksakS88Zh12OiNJEgbDAUVZIZzl9GxBIjJGgwxbVzjj8EKhtEKpRoV2HotFykCnnHWNwB0SUwVtBU+YF1VSIBA0ocY456mqmkhrrLMoJ5rAJ71OLhZCQFkitcBYT1k5rA9JwAhBYhVRkjIvCmZlweXBFraymLLCVjVCCkbjMePREGc8i9mC6ekpy+US7ywKgRICT6jGMUJglELpQEZdmoZKH8LNkaqqsNYRRyneWHxVUy8XSBw6TjH5AjE/Rc0y4iSlLmusczCZoLe2sZMt1MWL5EoSRcGibjsklKai5dbPTft7VVXrkC/X2Jo3Nm+5/n139wL6UcF8WfCB3/8EzlaUVcHe7hbvedejvPtb30VFwgtXT/nkE08zGiguX75IqmFre0yUZjx011/lrgt7PPnE4+xvbXFw80WszcN7JYPToKxzLl+6l2pV8NIzX2aQaJw3nE3PWCwWIc1YJmTDAdnWTnAFVPXX4BumR48ePXr06PH1DO9vn6W2M9XX488RfwEUUXBN+m2Ys6yJkng9n+e9D+qn90RxeDz0TrazdTUQalxMXQf1zBq8aPKvPAxGW+g4ZVkU1KZmOBlR5itmZxlFPiPJIoaDEcvlijjJGI0nxFpTFQXWWrLBAKUki8Uc5xyj0QhjLdY5IiHIssE6iMh7z2q1Issy4jje1Gd0uiSBNUntWilVU/XhTVBMkyQjlprl2ZQvPf4YBwfXqU3d9KsK4jiltgZTVoBEiKCKAUTKE0eaJIqJlQLnqeuKooQkldSVpSrLJiEYfBskhCCO40CsbCCCZbmiqDS1SdBSY12Jqy2VBQjBP94bRDMfCgJrHV60qcHNl0TTPSmECIFCCIwJBLXtC23t11pApIMiGTVKZBv0A1CWNcZ6rIOQAxSGOJWW5FXN8XTK9TTjYjxgNxmik5gsTrCVIV8uwEM6GLJ/+QoCmE9nHB0fcHZ8QrUq8DbYuyWBnDtfY43BGUuapqhIE8dxSFT2HlNXaK1wZY2QEhFbZASJijFlCaVBKQfO4vIVvsyR0zN8pLBXh0QXLiHvuoJPBwi1CdiKdLSei21vYtxa1xI+C5vqoG5YVpvc7DFkw4Qvf+V5js6OSCLB/+W9D/P2b3org/GQVS146ukXuTkredej9/LWt97HyfEhn/yDT7C4OSLaucCDD9zLF7/8FJ/9wtPcdWmf06MbvHrjIrWB+XKBlo53vPUR7r54kU/80ceQzmBqOJ2ecrZYAAJFmD9OhlvobEA1m4X53B49evTo0aNHjx7fULhjRNTbcJGstCaJIiojsN5SVSVOOHQcbfo1LYCnrEyYWWxUnk1IEXjfpL0KiUehmjoUhGbnwpXGFuo5OztmPgv1EbsXdji4vqQsVsSRYjIaUeYFWkfEcYSUMgQURRGDwQDngi13aztl98LFhlhCHGvqusDaEKDUKnfWWsqiIEtS4iimLAqcFKgmDbVVhNvwItjU0oR9A4unqAsWswUaiCPJsjaUxpGXFZU1VLbGe9Uk5YbnxVFMrCMiFREpSaxDn2hdWepqhTGOqrYY5/DWIREkOsLjOZktmFjY37+AVIKzxQnHy5K9cUxpNCpSYUa3LMkt5BWhOBNJaAwNlNZZhXWetpkkUk1XKB4hHLYhSkIItPMoKYlUBM06jHHM5zOSOEFIGZRS76kqw3S+xNQO42yoGCFYd7VxlJUNQUpVzmE5w7uazAvsbEm9zKnmK2rnGO/uEqcpcZQw2dllsrdLXdfMZzNWyyXFYkG1yilXedN767B1TeU9kY+I4oTJeExRFpT1EqiRPgNXBSVYabLJNlrL5pyQeCEROkZHChlFKAE+L+CVq5gbNxCjIe7KJfzWBaLBGGsNzoeqo5BmuzlH2t831lzbJFDH4XnOo1SolSmLitnsjC8/9zSJNtx/+W6Wsxm/87u/x87OFo++/Z1829vvBZWwt7/DdLHg5PgAZS2+XPLEF6/zJ3/yJ6TRgOFggBA1o3GKccFWv50qvuvbvp0o0jz19NOU+ZIszXjp2rUwEy0ipNaMB9vsXLjChXvuZmtrwsnxGQc3rv45fOP06NGjR48ePb6e4Pztq2+5Xevp8eZw58KK4ghbVXgBpiEjUkqyLMJ4t1YOAbSKmzAiCV6i1Xl7bvt726u4SREV6DghzYboKCZOYvb3LrCYzyiLFaenp6GXUUvyPGdkLcPheB1Ik6Zhdq0sS4qiIE1T5vM5RVEwybbXaiawVuyUUhRFjnMudJNmGRKBbNJyfWf72nk+KSVFUYRZwCik2VZVRZrGeIKt9x3f/M3cuP48x0+fYa3CWYu1LhDJzoC1EBKtJVIIZNPk0naUeiRlUQfl0XnK2mCsxXtLLFSYQzQWAZwtl5wsFjx49z1MBpd4+eAmoyyi9oadYUwkChCwKD1FbXBeNcpbY9OV3ZoRj25mervKcft3KSWVMyRxjPNByQUZSKwP5NY5h1SK3Z1dlIoxTnB6Nme1XFLXRUO8m/fdOgSe0loOVwvUSFILRV3nlGfHSCsYbW8zGA+xxmCEQjgPCqRSbO3ssLWzE1Jy65pisWI+PWE1n4XEXBc6beMoIRmllFXFcjnDO0ddG0xtG4uHII0ikmHW7LsBAVEaI6QM9nNjMFVFmVecnp5x9doB2295mMt/5duJ3vowCBW6dL3HGkscxWtHQFchDcfSrm9qtATf2qB8V+WKmzcPufHqAeNsyOe+8BTXbp5hhcJZz0c++Qx/52/+db7jO95DPp8yPZny8rNfwRrL8ekZr778KrsXLvGe97wLZwzaFQzSlLOjKd/+nrcxHsa88uJL4D2R0ljrefX6DaIoIkkStNbc9cBD7F+4m7wMhDkTnpEWnMg+IKBHjx49evTo0eMbDXduRlQrYhnSNRW+SdHdBBPZptrEO48Vdt23qbXC2Y3l8NaqFNmxwFoH0SCmrGtWVUVcRQgPFy9eYj47Y3Z6jJCBAJ+enjLZucBkaxulNMvVklJKkiRBCMFqtcJay9bWFnWTdhvHcTMDGkhmURRNkqneVGkA+Spf20vLusJ10nXbBN0oitaEOkmS9exhkiRIKfn85z/PqjA8+NDbeOXlaxT5CbXza3uqb46bUqIJdGr6RoQPIUI+pMAKGVGbmqo21NZT1w4lDOPxkCSKkWOJ1YIXrk9Z5pbl869w/8WLGCRPvXLEyXbMXTsZd+0NwTvmhaAyHpqEWec8QqmQPibabrQwK9qmuHZrSdrAqkhFKEBJ0BK0DuRVykCq2/dUCMH29jaV9RyezZBxhLOWyjiU9iBVaGXxltJazqqCzGb4SJJMhmQIJlHGaLyFlmptYV0HK8n2BkhIzlVaMdweM94aYaqKYpVTVxVKSpIkDXOdzlJVqyb4yOCNCWqvtZT5imSYhDmGhpQb4zF1wWqxpFguOD0749WjI24c3CRTKTUh5fkeLRg98BCVlNjaEUXxujO0teG26P6+mTENycjWWW6eHPN//P6HeeKprzCOFbP5krpR3aUSrPIlX3r8S7zt4Ycgijg7O+PVF1/mwoVdirLi8sU9kiyhLhaMx1vcvHZAiiTSlr2thCe+9CynZ2e85aF7+eLjT/LKtVfDzHJjK97d3eXixYugBMev3gBvuWaX7I8zHrjyxnr2evTo0aNHjx49WvT1LX/5cUd7RIFNwA8eqRSsCZwPKmIS4R0hgMh7qrJcE5RWXdt0Jwa7q9IK50FIxf7+BUajCfPFHIDVaslyMQXnGI/GlKs5QkomkzGr1ZKqLBmNE5I4oa5KnHOMx2OElBwdHrK/v89oPMLREj9FXdfU9UadjeOIqqqxtibSijgJgUWeYCV2DanSTR1HS0TzPF+HHimlMHXNbDWlKAu+67u/m8c+n/CxP/ok0+kUS41FYFxIqaVRXAMRDTO4FoNWEoulqFz4wDpwTmCtoKzCnOsgjdgeDtFakW1N+NQTzzOvA5lcWMOqmLM1TrlxvCAvDLUR6ChBesu0kFgviKJwI0EJkCpUrwRy2tiFJThr1nOLQsgQJCUdsY4Yp4pBFrM1HhAnGuscVWWbLxmHNYayKABQUURdVyHgqqjJC4dWAmkcAwRKSqw1LJt6Fo1EbG2jo5S9K1fYiUcooZHeh2Rl13TQiraehaBAervpK1eaKEmI0wyBaOpVwnseIUmG2SbxqTm/PR6pFba2WGeo6pLlcsVstmAxW1LkOSdnZ5zOpqzygu3hhPFggF3NOXvhOdJhhE4yxMVLqCgNNS7OonXUWG9d07laIZsbKnVdE0WhSqjdl8V8ySc++Vk+9dgT4CS7wzGllTghkN6hBXjhyMslN268ghxuMZ0tGQwyptMzsjTBm2BNvvfuK2zv7FEsZ+xLzbWjI0bpgHuuXKIycz796U9x7ZUDpIqRWqFlUOlPj084uXmdJBuwN04YJBFSWIaJ5uKFu/8cv3l69OjRo0ePHj16/EXAHSOiwFrhAkLwT6RC8I0J823eNomoQuCdWZNX412TdrtJyw2ktKlt8eAIFlilQwDQzvZ2SNi1hnxZsVjMWJ6eYJ1DKU+cxGxtTTDWkBc5cRQhRcJiscA06ixNmmpLfFs10zmPEB69nv20620SjUJbliXah2VMWWKtJUmSdcqpECEkqO0WlVIipCTLMh584H7+6IPP8IXPfYrVfIrHUdmaygk8ISFYSYXWG0VZtOm7UoTOTmepqpqyqqmdwLmQZKuVJ27mSK0zOG8oHYy3JhT5CqxjMBpQ5zPSKMZYx8FpSRyVpFoyz2uQiqjpxPQuvK8y9LcgBUG1dk2NS2PBls4x1J4H7tlldzRkkCmyJCZSEucdNZqTWY4VMd6LZsY3EEyhINaaLM44KGcIqShKQ2Vy4ihmazQgjjUCQVHW3JzNSdOMYZRilaQWhKRX78EYfG1C56ryTQ2PxlmHNTXeGSCovEgJSJI4xTc3SxQhqMmysYqH8lYFzlGWFUW54mQ64/TkjMV8wdliyaJYsSzCjY9YJ6RRjPWOVb5ClRKqgvrzJasi5y1/++/itMaYmjhJKMtifd6XZbG2elvb9I46Hz4/zlMVFcc3j/nUp77AIq+IBeAaVdpZnFTUXuK9pLaSm4fHmNkKKRNUrJHO4rVgXld809veRpomCBxb2xNODm9iXcVyseTqKy/y+c8/TlFUZGmGsR6lYHd7wigbsFiW1KsVe5MhaaK5uL/LzmTEIE5Img7VHj169PhLhdsYs5mIO3o59j+F9e51l1Hi9s3XqTfY5/gH/4//5Q0t9//6X374dZexZ28wvb2PVv0LBX8be0T9bVpPjzeHO5ea26iZG4UsJKK2RKVN/+zOFSqlztWdwKZCQ6ztmwalw1ypsTakrJYly/mcKEnI0gHj0QiJZHV2un6eqU2wrgLLxQIxGpEmCePxmMViwXyxYDgchpm7qkbH0TpsKEmS9T6025qmaej1rIOduF3Ge3+uYqPbCdnuW0tw0yylKgqOD4+4+er10Fvp60ZZ9Fgb7LdSCrr/DwhEmSZwiWAZtYGoIySeUI8zSGLSSKAbIlNVFbtZRhzF3Lx5iHWGu3ZHbI2HHK1mZDrCaEltHIenNaMsQUrNMEuBULVjCGqnl2I92xlkRX9u+1INj9y9wyP3T1UOagABAABJREFU7BALh441CBEIrVScLSsGsWa6LChqS5oNQ9CRtVCB8B5vDRA6S2vrsRWcyCVaejxjokjjnMEsPdePFVVZcapn7KVDotLiF0u0tQgb+jeTWDBIBgyHI5JBQl17rAnH2zeVN96BFKqlnUgpiOIYFevmvbMh0KgsycuCRb7ieFkwmxsu7u9z3wMPczEvODo74tWbh5z6Gbt7u/jaUOcl0lucsbi6wlqDeuFlpi+9SPrwI+gko6qqdcBVt4+2ruv1ZyTPc7TWLJdLFvM53jvuuecenn7pJYyUzPIlpSmDdVZqQCGFRQrF0cmCeCvimWefw9maVGsshmWZo+OI5SpnVZThHI81+1tjPvLRj/DkM88zLysipRgknmGkyIZDLl7YY5AkZFnBcrXi3rsusL83Zjo94ejwgDLPqYrya/lV06NHjx49evT4OkRvzf3LjztGRI0xeO+J43g9H9mtN+kS0va/u+E2bSCLlHId8ANNaJBUODz7+/vs7e1hUZxVFXVVspifESlFWeXBFmwKvIfFYsG+dwxHI5wPAUG+sT62M6JXrlxpqlMsUiYdcumoqgqtdZhrXafeivXvSqm1DVd3UnPbbtFbialrejmllJxOz1hWJVtbFzg7W+J8jRQJUvigiBICdIKVNBy3QaLIYgneISMgEjgvSSw4p8AJ4ihCSYdCrkm/KXLe89b7ef7qDZw13H1lD1uXDIdj0qiiqB2rssI6T+09g1gjpULiAzkTAq0j6tpinQAkztNYgjd9sNuTAVvDGI1DahkIszWUeY11sMxrloXFGFiuclAaKRRShkoXpCDOEtIsYboqsd6TZDFIxXRZU1UnaC1JE8XWcMAiipFaU0ee0juGTnBhMubS3gWUEzz77DN86HOfAyF56MIuD13cI4lScA5rKpyx633Oq4p8VVAbC5LQMSoFSgb1vTKevLYsi5xlWTBfGnb39vihH34fVDlHr1zl4Sv7vPdt38TKeib725xNp3z2c59lfnQMPvTqXrh4GalTTq5e5/5H346UmspVazt7a+MuGssyhGCt0JWbk+c53ntOT0+5/567ec/b38aLL76AlI5BppHGY50I7gEhSJKIg8Nj9qKMrd19jk6OOZgtePDhh6lPb/L8V17kwXvvxxqHLVeME8VnvvQk14+OESoikhqFI8009977AINsSKoVD9x3Nzu7ezzz/PPkRc5nPvs8SgryVYU1lrqZ8+7Ro0ePHj169OjxjYM7OiOqlCb4OQV1bZBSARs1dBP4IxpSGDcBP2H+zVmLNXZN9LwP4TtCeVSUoKKY2nqMKdne3gYJi5mgyJeYqmKV50EZHGRsb22zWC6IkoQ0ydBSsFzOSdOUOI5I0jDnmSQpUm0qVtqwpCiK1p2OQdkNZCKK9DpgJo7jtXIax/FayW233bnQBbkOKzIW7wWTnV2+/bv+Cqcnh/iXFEIopBAo73HCgxehuQVPohSTQczlnYy0qT0xtsYai9IRWsdUxrKY59S1x3lLWa6Y5Z6drQl1mbO/nXLxHQ9xcjZjtVoxn87Y3dkmGQ85mS5w3mKcZ5CmDGKJam4oGGeIdCCeSoGSvlFuBQKJk+CMYTuL2R9pxqnE25raQ1V6qtrjhcULQZYMcRJOjxbMC4eRJWkSI3DkJhTESK2RKhz3JNaApaxqbA2mlgyzhPEwQ+sYayyL+RIx1gipmEy2GE/2GG7vEUnNt168RL1zgd/84B/y4PgSVZIxn04p5wtqY1hVFcui5my1xIiaR+57gG9660N8/olneenggCKvNjcgfAiLcqJRg73gm9/5KNkgQaQJyfaCo+uvcHY65eLly1AlbE/GfNd3fTePP/4FDl6+ynhri9o76uWc+PAQMV+Qp44kTbC2RuCIo5i6apR8CcaGzwge8qIMIUTLGTduXOP5r7zCxb09Hrz3CtPTKXt7Fzk8PeXw5IS8MgyyIWVdcePwkLe865t4cHuPV2/s8fkvfInVqmQ0mHB4cAjGgxUcHR6SxpLDwzMkMhDZQYLznsuX7uWtb3k7WRpTVytu3Dzk4OgI6z2rxYLFfNV8phsbOX1keo8ePXr06NHjzcFx+5TM1zeg9/ha4I4R0bZbMSSVCgaDId57lsslztkmgbUJIyLUUDhbrO25WmmSOAFYp9WG+hBBbQ3j7SG1scznC+I4ZrVaYZwhTWImwwGvFgVChovhUNWSkA6HOO8wdc1wMCCOI05OTkjTlMEga8izw1iLI8yphoAgjRBirXjGcdQof35t1xVik6zbdj22QUetKlrXNdbadcKutUFx293b5dpLzzI9PUYrSRTHRIkgqiqKCjwShAhKVyK4+8KQu/cnaBVTW4fzlrq2FJXBWY+zNUJ4pATpJXo0CDUixrC/u89QxwgP89oQWc8wihnEEUorxqOUygY1dzxMGKXR+kaAkDFVbaiMASTeOZSUaKkoqxrhPZFWDBJNGitqU2OjCLxvUmU91lckSTjWRycLDk4KloUjr3ImY0EcxUhvEMAqrylLE+Y5m/AerQQ60iRxhAeWeYn3YXZyMMhYLgpmswVVUTCQmkE6ZJKN0FLzne9+J+965EH8Kmc4GuKt58tPPsWffPbTKBlCdd66v89gGPHCS19hb3fC93/XX+WTjz/B0dkpZVk287GAgNJWrOoCYz1VWeC9Q2rN3Q89yF333sPRjRu8/Pwz7OZL9i5dRgnNIw88hDKeQZYxHA4YDod4oaheeYXBO9+J17oJeRLUNqjxUXNeGQyRjphN5wjvqcuSa9deReqUK/few4svvMLVa9d59dUblFXB3v42Dz1wBSUFxnieevpFzpY5f/jhj/AP/sHfI4k8wyyjLFbs3XWJulzy5aefo8hrhJAoBWkUofFEUZhJvveBB3jg/kfwzvPcs89wenrUUfsl+FA7gwcV6/XntkePHj169OjRo8c3Fu6cIupMY1+VjU01ENMsyyiKfJ0Cam2YZ2yJnGvqIFqC161x8d4TxRohInZ390izLYbjLcqy5OTkBKklxWqJaK20UYTHUJQl1obeTxXHzKcLnLUkabImiTqKWCyWgCDLMixubSmumzoWIcT6p1VKtdbrECJg/btvgota9XcTOLOx10olgm1RKqwT3PPAQ9z30Nuw3jEepTz91JM89dSLrPIS50Ej2N8acO+lLSZZTFEavDOUtiaKY4rS4dG4Ju1VCo9QIszOJhOEdcxmM3xpGKYZl3Z3KE0dEnClpKhKlJIIZxkmEbuTjPFkvN4fZx1RpJFlRV0bahMSU42zCCGRTeCBEo66KllJRaQgijVaSLJBRJSMqEqojaQwguNZwap0xFpirCeJDdvbg2ArVpbahNcKNmdNlkRESpClMVorIh1hnePobMbpdMnWcIQTwcY8TFKE0twvJLujrRC0tJiTxjFaaEQseM+738Ojb3krvq5QQoT0JeE4ODnkk49/kf/rt/9Vvve938LVo5sYYyiLkoPDQ6aLAqqKWkju2tvhvnvuoS4KrJTByqs06XjChbvuZnZ8RHR8wng0JlWKd7z7XRzcPEBZcNajvWH10sukD95PHcehxqW2WBNU4rwyiCgC55nnBbUQWDxHswUv3TjkbL7i8OgmL37lFZargqJ0lE4yvz7FXjvCYamrGmcMEsHJySl//Ed/zN/+mz/SqO2a3e1tHrn/PlbfvGQ6m3N6csrW9oRUaQQerRU7uzsMRiNWq4qyKLj/gft49NFH1jdprAs3LEaDAWmSkiRxcwOlJ6I9evTo0aNHjzcHfxtnRH0/I3pHcAdj2vy6GiMk3fqmJzSojO3flFKIppLDGLMmnm1YkVJq/by2P7Np36AsK1QU1NKLFy9ibM1quWA5m1IUOfkqRyrPcDhksViQDEdspSl7e7sUq5zFcok1oZMxSmJGozGI0C8pvFhbbtt50DiOKcsS2IQqtWphq+7eSlSFCGpvS0A3fakaoSQ6irFe8s3v/FYeuP9hnnz8ca5ffYFnnvwCV69eDyqbdEg8o0HCxd0JkyzBVAXlqiROU7yOqKzHGBf6QytPXQUCrKPNHG5Ie5WsbIXNHcNB1mxTsACvyhJjDJcv77IzHhEpTRQlaNV0oHqPkBIpV1SRZbnKsdZjO/O/AoP3Ek+McZ6irEFKpNZ441jlS06nFWel52iWN/btirKqOJ3WbG9tI0TMcDjCsiCOJc5ZQlhSU4sTR2ilGA4y0igENiVpxsnJlJdu3EBpzV0Xdsm9ZVrlnC0XpDKiznPMsiCNIuaHB4AnSmKUlMTDIUiBqUpc7bn3nvv4/T/8I77lkSmXLl/kkdG9zOZz6rpmvDXk+GzFdL5iZ2/CA/ffS7mcc3pwwGq5wpsKGSVcvHIXOzt7DLMBwli00lS1QQ9S7nnoQaY3DkmkRLgas5pjvvxl1PYWLkmQaUqcpSyqkmevvcpzL77MV557kdo6yrpmOByynM3YnUxI44S3P/ood1+5G/P/Z+/PliVZ8+tO7PdNPsa0553zGWpCDUAVUE2AQNPQQENGNbvV6ms9gO5kMusH0AvoSg+gG93I+k4mGUnR2EaBDTTBRoPEwELNdYY8Oe05Rh+/QRefR2SeAls4JTusZKFilZ3KyNi+Izw8PCJ9fWv913KWj58+59WrKy5v7qi7QEAiQpxvdt4iNPSNIDjBt37tK/gQyLOcru2omylSGoSQ5EWKQg4LMRKlZQzD8gKlNQSHkmJ3fnU2nuPBOQSgjdgronvssccee+yxxx6/pHiLqbnRpulcGGpX4p/OxZTZLakIIRAYUm2JtkvB64vb7YwlEHtEnUMnI7J8RN10rFYrjNaslkukluR5yuHRAavFDQiPD5KinHJ4eEzTtixv50ymB2iT0FlLUY6GfRCoxMRU1CEd1XtPmqYkiRnUWYCw63fczoBKKXc23O3+bsOKtuQ0BjMxzMkOFShCIJXCCY9JDa9efcyzpz/g4tkz1osNfedBBLSSKOV4cDzi/sEIZy2bdY3SBoEgNQmdq+ldz6Lq6HuJd4N/1IFvWyQwKkuyNCUZ7MViIPwpguV6xcFsxKgckRUZcugBDcO+WmtjbY2Kaan9agV+SBEWAmRABUVne+rWM990rCqJDIEsaSmyhCQT1J1l1Sg2HWR5ybvTEfN1xd1ygQBs37LZrNA6LjRoZWK9TOdit6dQBKHpnWC1WbNygdQklIUnT1P0cVxkqOuOl9d34MF2lqarmaA5GBex/zNN0UKSZllU34nzyjpN8Ebz4Oycrz5+TNPUXL+8ZHZ6SpmXLPolh7MDjo+OYQiaevaTH0abMtDUNcLHgKbV/JbT0zOODg9IxiNs70lFjQa8TihPTqhubqJSaVv6H/4AYRQiMbi85CMl+aMffcCPnj4nOEsiYnXL0cERsyThx08/4XvrirrqwMNkWpCmkncePeA3vvpFvFBcXt/y4fNn/PjDD9lsWibTMd/+9W/yK7/yNYpRzt18TlXXSLmKvbetJUvz3QLMaFSQZCnW9ljrEXiUVMjgcc5jHTtnQ1vXaGMwgxOg69zO+bDHHnvssccee+zxs2CfmvuLj7dGRLuuRwpFkmQ453e2W+B1j+ZwsfvppFkH2xCjN5TFnV03BIpiRJbmSJWQ5wVN01BVFZ3rqas1XbOhG2YznYf1ZsN0dsioLFltKtRmTZJmGJOQZRnee7q+IxFR/fHek2UZwGARdrtZT2PMTrl904b7ZiDRdp+3F+g7RRJ2JNY5cHaYrdOKl8+vePqjn/DJBx/z8sUl66qh7R1KCUZZyr3DgvNZQVtX9LbH+0CSJfQeFos5jXV0FlxQIAJSReLufECEqEhHm6TC+UCS5rtKEOcc0+mMLMvRSSTRyBhA1LftLmApy6Ii5oJnsVyijMLjES6gVEApSWslF4uW+RokASkCWaY5GAvE0tF0geXGk2Q5o7Eg14qDgxlBxHqZ7YxtXdc0bYe1DtvDalUTgqPWgtVGMCnH5LlCi6gQ9t5hlGIyHjE+PCDPUhbLOZvVnPb8nCwxzI7PabxDIClGI5ASryRSSJQU+ODxXoCUTKZTfv3bv0EiHDiQRiOFIB8V6CQhCMHNzQ3BO9559x3yLAMfaOqGxXzN7c0lq/mKer7iVWaYHc04PD6hGI+RSuKVQOYJyckJ9WbNanFHvV4RkFTlmL96+pLvXlzxwU+eIq3F0ZMVJZ3zvHz+HIdgUTW0ltix2g/WdRlY3qx5/vQOnShkIjmeTvnGf/EHXN/OWW9WCAE3tzfMDo8pigKEYL1eY4xhVJaAGJR4yWazwRhDURR03ZAubN1u0WXrCth+VsIbdvrEJLv799hjjz322GOPPfb45cJbI6JZmsbQEqK9dls7sVUOtwE/b86C7pTD4e9vEjjnHEIKfICu7ambbqiFiNbX8/NzNvWGulrTVGv6vqPvOpSKoUCb9Zrp9D6jyZS7uwWbzYbRaLQLWumGbcuyBNiR4KiKJjtVZ5v0u9237fzq9mJ7F8D0BgndqqI/becFcN4RguPq+orVpubr3/g1lPw+3/3e9zFKcnw44sm9GQe5pm8r1uuaECRJYuj6Dus8aZJQd+udBdi7QJFnGGOo6xoh4r7avsclCXlWULcdAgi+J0sSssQQXEdXS3yICwcCdvU2MaQppe176qphPB4zHgnWmzVs1rRtS9/ZqD72jspbnOvpbEdAo8QKLUCbhNFowsh7Wufo5neMJzOMMbRt7K/0w6xpt+tHlTEtVliSJIbmCKnQOiNPAe/p2g6ZZqw3Nc46kkRzcnyMDJ6gJVebBcJ7Cuc5TEvuHSfkeY5EDEQ9qtVCxPfWK8H0/BThLDhPvVkCgsnBAY5A0zYcHB6QpRnOdbjeorTi4OiQ43v3eNQ+4Uc//CEXnzyjnq9YrhZcvLwgLwsOj844vn+PdDLCGU02GpGPx4Tg6BrLyxevuH76nGK14h+9+4gv3LvPxeKGRqbM246//PgjfnB5QRXAEVOWlQlUmwUPz885OTjAWbherHm1WOCcZTo2PDx/wPvvvk8+Tbm+veP6+oqj4xPyXKJUPP7a6OHzJ/D+9Xnd9z1aKZQ2u/N++/mVb9jqd/3Ag2V3Oye9xx577LHHHnvs8bMg+rA+r9Tc/aL428BbTM01MQjFe6RUn5qj9N5jtEEqRfCeMNhdgw+7WcTdzKUUCAQIQQiQ5SOOT07J85x+tWK+mJMkCYvlAiGhyDNm0ymbxR0WMEnCk8dPuL655eLikulsRlkWaJPQNHEmMs9zJskEhAQCbdvFUB4pYxLoAGvtTr3dqonbEKMtidpeeG+33ZJsrePjeb+dlY2Eqmktm/WKg4MD/v7v/gP++J//M549/wghLKfHU+6fTJlmiuBamrYi0COFRiBRUoL3kTyMDFqKWEvTWJxrSYzk4GBCliQYrTDGDAnAASUgNZpxnpPnKdokKKnjYLj3OO/oXVRUIaqr682KJM0oyzIuCrgAogQluLtzdK1DS8k4Swk643Zxi5cKk4zJTEKeJiRaYoxivlqz3GxI04ym9aRZgvMeHzzOerIsJ0lzVvUrhOhJsxSEwuhYJSIIuL5HpglplqOQeOexgCwMm6qh61oOJxO0MXTes+waUpNydHzMeFyyWa1RQpIVOcJD38a0WLQCIWMAkzFsqhVN05GXBZ21CKMxSQL6tRoYz1lH1zTQ1ghl+MqvfJVH77zL97//HT7+4Me46zl5VrBa9bigOFMGkUHA42xAGUleZnzri+/w9ZMpy+tb8BYp4H55is4yTJLzX33rq/y7V1f8yQ9+zI9vbvnBi1cgBDaA0QbbOzrvybKEsja0vcQ6yQfPX/KTF5c8OD/kG7/6VQKerm8pyykCQdt0OBfIsgylBFpLpFDDbHY831zvcM4OIWQOMdjS5S6UKH5mgagwD4swe+yxxx6/cPgM312//e4HP4cd+TQs7jNtJz9jdZb8DBfoLvz8yy/GwyjT34bv/Z/f/1u3+dL//s8/47N+xte5X2D9uWAfVvSLj7dGRIMDiPOfzsXZQqVUJGPWxRRUGLpB+x2JE7CbXZRSIqTAeY/r+xiqUo5jGm7wlGXB9GDCer1mU1kSZVjc3bJZLiB4tDb0Xc96U3F6ek7bdyyWK8ZjmOUF43GyU0SdD3RdTZZlaK12c59b662Uckcytwm/WwVyazXekk54HVL02or76du7WhelGJclSlj+6F/8CS+efojzFpNIsjSQm/iPiQ8BgkMQbbeIODOZZ4YiSziYJnR9T9VYEAneC5QyZFkR7cTOD3UggSw1HE0nFIlBCHBC0nmBi47i+NzK0HWOTdPhvY3qmPS4YCEo8izbpegG4PbmDhkMeSJjF6YPrOWGfFxgg+FmvqKxKyZFxjQzjEYli8Udm6ohhAlyUIiTNEErQ2+H4CcC1vasm5oQLKk2GCXJEkUiA1UVLdubqkYrxWx2iO17vAWlUhrrEFWNLgtSYzg/OmOU5riu4ZMPfoLreybTKS8uXiC8ZzyaMj45QghDMR4xmkz44Y9/iOwteVlycHzE7GCK9QHbt3RNg+tdfM7eDueNwRiLTxx5kfHtv/ebHJ/d56OPPuarX/4ihwcHtF1HEAJjEpq6Bh8QXuB0nKfODyaYNKVarcEHkjSh6itWXUVVew5Lzd//lXcof+JxzYoXm45NZ/EiYH3P3WpFCIIyL5hMDdZZ6qah7TueP7vm5vZPmB1O+cY3fo3z80CaJcwOZlxf3fDDH/8InWiePHzEwcEskkwRL0Ss9ygd7elIhXWxc1YphRJgtEbK+Ln21iMCccFkjz322GOPPfbYY49fKrzF1Fw+lRJrfUyKxQekksP8YuyIBP4GkdvaX72LAUVb5cl7S1WtMGkS1bomEt3zsxOc86zwLG6v6fuerrfkxYi729toBx2NadsWpTTBB4SKltyu6yhG5a7K5c3goe3s6pvhStba3f5t7bfbebktKd1aF7d1NG9Wubz5Wo3WLJZ3fPev/oJquURIjRSKIkmZ5CW5STFaUnWvV0DDMPMpRKyaGRUFQngOplG1ypOcNM3AC3rraFpL2zu0TgnAuMyROsGYDCc0TiqmRYEP0FYtbbPBdrEyJUkSmiam1ibGABLnA85anPVY71BSIhG0bYOSkvV6hTYZaVIwOjji+eUckRb0/QqhNVXT0PctZVnQNBWbqoopq1qSpHpH8rMsYzabcr2oqLoGNwTjJGlKosVwzAVSaE5OTlkullxd33B8dEiaGOq6QuDRskBKQaY1Cliv1igkZw8ecHdzy3y94S9/8AOcs5wdniI/fkqZjbj3+AGn9yxJbkjKgiIvKMqCar3B9R0ieLSM9Tik6W6xwbaWzWbDpqpIKkOS55yfn/Hw3Xd29mydpHj/Ok1ZaomSKr4H1tIETz6dMCtK8JbOdsxf3fLiZs7L1ZpP5nOeX16xWt3xa1//Bl/SOX/+/R+xXi5IDg6iyq1TmranqjZY53DD3KdWCiESnn9yy6tX/wPvvPOQr339y4zHY5LU8OTd9/no46f84b/8I7705S/zpS9+gTxLdufcdvGG3WcyfmZ77+iHntwt+dxa7vfYY4899thjjz1+FuzDin7x8fZ6RIeLVa1jyAshIEJACoF1lpjHGrHrvRzI3ZbIhRBwIRK5aKEtODm/R1aUKJ1we3dLW60oy5LF/I627SmLPKozSmEQnJ6dsVisuby85PT8PsfHp/R9R9005Hm+CyuSSpG9kYS7xfZ2eMNiuO0VfXO29c3bW0KyJa9ba+6WlCdJEmciu47gPa7vcH3N86cfcHV9SQiOk8MJszKP1R59TBveIoYhBYo8ByFIkhRjNFpGB7yzjo4ao1O87/FYdKJRWpPlI7LRmMnJA4rZKTrJUKlGa4VtWpY3l1xdvKBarCAIWmcpipyui0FR1jr61tG2DUYnmIE0FnnBatmhpIxWYaGRJuVuXfHy9pZiNEbqgDaBaVng+khe0izDdjGgCDSjcZzR7bueQHRG5WWKWK1IVBpnRr1HSDPM4Xr6zpKnOWdn97i+ueX2bs7R4QGHh0fU1WKYFVZkWQISsmJEnuZIIZjODumdxeUZ/+Sf/3Ounz7jaHbE8UHK7Q9/Qjke8+477yG1QglJVzesNxX1ekNiNGWRk2cKkyQEKRBKwZCQbG2PDICQ9ASEip2rzlq8j92ciTGkJgEfCN5FVbuq+fHzZ3TB8dXHT6hXCz56/gnrTU8vJcuqZlX1VF0gKUeIxJC6lvODMR9f3GG9p+8ddb0moPA+LoAgY+2KkpLZrGB6MObi1Q0//MFP+PGPf8C3fv2bfPFLX+aT5y8JQTOdHfHH//pPWVc1v/kb3yQ1htq1uzAuay1icAq8vq/D+wCEgfCGHVHdY4899thjjz322OOXB2/PmjuoJdtZyVj1EAmB57XqGVVTdsX3UolINiASEQJ4y2azRqc5ddOhTIoQmul4QnZ8yHq14vb2FiUlz55+yGp+O1TECG6ur7n34BFCJZEk+qhOtn3st1RagfOfChwCPnVxvX09W2VTIvA2KqESgRjCWrbW4+0MqRxImfcBHwLJUA/TO4tWW2LqWC7mrO7maCkBz7jQzKYpo9xEktb3eBfwXhCCHHzPAaUNUirarkMIhxMhKotKIZzAhx6CQGpFko8YH92nmJ0yOrrH9OCUpJygEwPB0VVrbuYLur4leIeQIBVoofBeopSm7zu8a1BKAB7resrMoHVGMy5YzGuUVAgZuLxZ4HRCR2DT9qy6W84Ox5wej/FVhXKKTV0RCGip8R6CF3RNTznL46yos2yqimqzoWs7tFYYqXG2hyCJ08WCu9USoQQQODw8YD6/4+rmFqVhMioJ3jJfrvDHxyRlgUoSrPd0dY1te+q2JUsKvvaVX+X83n3u3buPt5a/+su/4JPnl9zezJlOyni+pSknZ2e4k7g4wjDHzDADjBR4KcAHVPAE6fEB+s6TG0Poevq2ot1U5NkIEhM7OqUCAUpJiizj/PiYP/v+D1iuK8rcsO57+gA+gPCx57ZIS4pMElY1ZZ7z5OCQbt2yWi6oWo8sc1wI9C5QO4u3nsSDUxnrqol2aKkoyhLw/PVf/4CqbXn06DHf+d4HaG3Iiyn/5s//HadHRzx5eB7nht3rpGitFHKY45ZKD+FSw2ffWYQUaPXZ5nz22GOPPfbYY489tvDh81My/X6s963grRHRLSnbpmnGWfhI2vAe791um21XpfcOISRCyhhgFGIaals39J1jOjsiBFjMF5RlTCmt6w3eOWbTGV3XMC5L1vMb2rYhz2NH6N3dLef3H6O0ous6yrKkHI1w3iF8rO1QQ6DSmxbcN22FW1XH+9e5W9vXFwS7C3MpJW3b7lJ2hRBIJXazrkormqaJJDXR1G3LerXh8tUll5dXdG3L0bQgTTQ+uNjBigAkWhkI0QJJCDjnaZqWvm3xRYHWEudisE9qBEbGrtAsyTDlFDM64vjR+xSzI7QySJMgTYJtKj754Cds5ld470BqdBKtz7a1SOnp+9evD3ryPCN4ie0to1HB0cGMm5sNWVaQZpokzXj64oJpOeL9+6c0tuXBySHnByUXmzk6Sble1oCKNtt4dpAkPlbvJArrPSZJSdMMY2q6rkVogZYxKEdKhXWBpmu5urkd0nIrsjzDB8eryyuaZsTBZMRiteHPf/hD7GPP1++/y9ikqDTh+u6O1nlO75/zzhe+gFBx5lV6x+np7xGcx3YdWguSJIlqc9vS1jVSCtI0Qw/JwiEEpA/IrmVVNdzc3fLxq+fUbc9oVPLt3/gmEsGr509p64Y0GeGdI00S0iwjSVNUqrFCMh2P+S//8z9g09b86Ec/IlUZiUrprONo3PGlB4Yiy5hNcqbjgsIYqran/k1P1TQ8u1nwRz9+yp9+/JRN0+KFRwkFSIQweC9BenrfIVXOdDZjMhlzt9iQ6Asenh7w3R9/gtIpWTbie9/7IScHU4IQNG0LQJ4kePFGaJeK4UTb0KK2a3aLTXvssccee+yxxx57/HLhrRHRrbV2mxb7N/oG37CtDncO/YMBeD1/2bYtVVUjteH09BSV5LuKlfliznq9JM9ylvM5SonYdTh0fBpjKMoCD4zHIwJyZ52VStEMqmWWZZ+qY3mzWqZt292F9Ha/Ba9fS1SVXtt3t7bcrQ1XoUnT9FN9o0mS7Mh537c8e/aMxWLFZrPZVad473EW3lzA8UPCcDx2Aes89BYNbEJFUeSRnFkPwtH2NUmSkpdTzPSU08dfoJwcUFVrBDCeniBMwmp+w2p+Q5ZkHJ6cUG82XL58Tm892nmcCwgxkN4hWApAKxFThb1lVKQcn0ypW0vXNozzlPcf32PTdkwyTVbmSBF4/uGHaCXpQ4e1niACQsQEYesjuV6tVozHJR7BarXmdr6g7S0ipjTF9wJiqq2I6vmmbpEyqofbTk3rYV1tSZOmcR3f+fgDpId3Dk5IAxwdHpBkGTIxBBwxIFahjIyhWM5BNqidCHQKeZ6RZinr9Zqmf32+3d3dsVwsaPuev/7Bj5lv1tgQZ5w3myV5bkhFYD1fsFpuGE8PeP9LX+L46HhwDUS1fNP11F3HD/7iz9hUFb/29W9wOjskCXJ3/sW5XLC2xfYt3nkOp7Ebtet7vnJyyLfffcx/9z/9Ff/dn/0bFqFHAKk2CCURKPI8QWiF7QV5kVPVFW3T853vfJ/f+51fZzYquFk22L7l1aua1WpFUZYE57C2J/QdUjAENGl0Cs75GPLlLNb1O0v6Hnvssccee+yxx8+CwOeYmruvb3kreLthRVLAEKzzutIkzn8as53xc7HigS15BYY6l77v6fuW3jZkiaHrWozUbDYbtNZkWcZoXFKvN7RDWuf19TXW9mgT7bFKa85P70VlMM3ZbDY478iLkqIoAD5ly30zdAhij+aboURd2yKFIE0iuUQIgg8kSfI3KlyIL4UQ4s+3CbtbMuyCJeAZjUYoZXj44CGr5ZwsMxitI8Gw25CiOKMqh1oRiEQr2EhOR3k6kCiB94G+t2idgFDkR/e4/95XKSYz2s2afrOkaSuE9aTFiGp+zagsODq9DwJWqzUeiQ8gJZFk6Kj0bo+VMRqFZzIuETLQtC1aB4QDFRRtHXtjR2lCS8f8+pqm6SjyEWma8vHLK3xQIBy969E2Epa27zFG0/U9zsNqvcE6T9c7tBJIk2B07BUNw8xxUZQsXMWmbkmMoe1bhJA4b2madvfVY7RiY3u+f/EJvet4WJQkRkIPXVcTlCRJM9K8QCkd2a4HnH8d1R7AB0uepuRpugvUEkJw/8EDirLk1eUt3/z6r1LmCQjPq9sbfvThj/ngxz/meDIiVSld6zg9PWcyneKCRxDnR30IKK24eHHF/+u//0Nc31OYjNNf/zZBhRjGHxzCR1KupUZn8WNuu462ikotOmGWCP53v/MbzMZj/m9//Edc9i3CaLzraFqB1IIkS8mMoKvXXFy+oto0NNbzwx99n0cP3uNm/hQpYVOtWG+WECzdQL7XbTcEWMXPjUkKQMQwqTQhTVIQsNls/gN9w+yxxx577LHHHnvs8R8r3hoRbdo1Whuk1BAEQgQCDjcooNZazGBp9C4g5UACGa75g6erKqrVCicFZw+fUI4PabuW9Xodw2fynPVmQ5amHJ+es5rfUBQZle/pe0s5nlG3PR5B37VIozGZoe972q4Z9k/uqlq2hDkqs69V3OA9RsYqFykE4Oi6Jia2KoV4IwVXakU/BAtlRR5nO12IypFQdH2H0YY0ybC2wyhD0zScnt3jJ9+9wnWWdJIgREzm9bgYziMkSgmEkCRC0jQbnHUIpZFGo01UgH0ApEIZg1IJ+cEZo4MzjElZvnqG71qUhlQZbLOmW9/RrOYoIVnP7xASXNfgbYeSAT8E0qRaY5SkrjeDyp2RZSlgyTODFoJRqul6aHwPAtqmBSlYb1YIGUgzTUCxqjqqzmNdtDkLNfTEIkAout7jg6X1gUXdA5pUxxRjgidLc/KB9LRdg5KCVGu61lLVFTpJqNuWMkspcsN6vUFJRZYYeu9YthUf3FySmIecaEXfNVjv41CstWB7lFEkOkVKjRIKQXx/vfXIeILiXY8UoGQkrUF4Dg+nHM0OQMTArqZvkKnG4bmbLwg+UB4cIpMRQiiq1Zp8WBABQde1rNuGp88vuVtUPHl4xtF0hCYghUdJSQhxMUIg8C6GeUkBJklR2lBtNth1hS4zRkLwX//aV5jmBf+XP/z/UPUdvnc470AGXEhwfU/d1PR9XBjRwvPR0wuePHmP2ThjU1mqrqXtWowQNE2zm29u6uZ1t24XSNKUIsvAeVxw1G3LX3/3ez/fL5899thjjz322OMXHiGIz63/c98j+nbw1ojoNuwny5LBLtrtkmeVUq9n6gYiKKWkaZpISAS4tqWuN9i+JyknjEZThBQYYxiPx3jvub69iVZc57hcXpIlKs5UCkGaZEiVcH7vEWU5JYSosGqtSZIEIdQbYUli97MtCYXXgUti0NSUUuBCDNYhRDIwWHC3ryUMNs3t46hhjnSXlqtfK0jew/X1zZDc6+j6Fimj6kkI0faqA3aww27/k0KSJjkOhQ8OiMQzBOito243JHnG/Yf3OL7/LgeHZ7SbNau7S/B9nHdMC6rlHGujUu2dR8mKtEhxfUOiBanOqXm9cFCMSsbjMW3XorRCy4BRAhUCaZ4xLjpuli3aKLwLWBuPudSKumnp+zgDbF0gSxN652OgjQj0zpGkBh881kViWjU9602Dd353jhgdFy/KURlVZW9xeBJj4mtBxe7Yqub6bsn52SHnRwcEb9k0HeDxeUJiEp4tbpmmGccmRbrXNnHnHME6rHUEoChLDo9OmI2nCBFiWI/rwTt88CDj60JE64dAoYxGK0WpMrL793n06DEeEZOjief+s6dPGZclVVujdXx9iMCzZy/44McfcDY74XR2zL3jYxIlCG+q9luF3ChkUMSopnhfMRpRhSW2bxBJhhTw2195xJ988ph/+tc/JJEGKQJNvaKqHHFuVGKtx1lLagw4R71eczBOuXr1kqNZwbQshkUjOajiW3UepFS7GeW6qeLnzUdV+0c//OHP5Ttnjz322ONzhfjbO5Bnpv5MD2Vxf/tGnxFNsH/7Rj8DMvG3XyrG8rO/HeozHLOc5DM91jq0n2m7Pf7uwiPwn5Ol9vN6nD1+NrzV1NztjKXRkWBsSR+8MW8pBFKKXb9mby0IT1WvadoaEEynRzRNz2azYjs/aq3lYHaA0opqsyGUnvVyzmK+irOLWtC0HSZNUVrHoJs3qla2s2tb5XM7x7ndL2Bno/XWYd5IwjWJwVqPVjrOiPrXpBopPmXDlWIgsLAjp9s0XmMSptMDuH+fu8vnBO+GjlWGlGFHGAjSm92hcf/k7nbbNbSdJRhFEBJlEiaHR+TjGaoYIZXi9uI59d0VTddhvcWolPVyw3LVkKU5CjBKkY8z0sSQGU1R5GQq/qPSdR13iwVFZlDCYyTkqSRLJEYJtIBUByQOL8ATkFrRNhVBxGMipcP2nhB6tBIYrem8i6FI3mGdw6QjijSh64f7nUOE12FR24UGqeDgYEbb1njnKEc51gfmyzXHxzPGZcnVfMHV7RzbW45nE7QxoAxV1+PmC4zQ3NQVI5NgfKDZNPTDokFXN2zqirqvMSZhdnHFO0+e8OD8HkYldJ0n9B7nPZpYvRKcQyBwSIKzKKPp+jYmJmd5XOiILwAkLKoVH778hBc3V5yfnqGBqm64u53zj/6z30UIzYcf/4RRnu3mkHefHUT0TTNcKwUIzhMEeAFJltJt1ggCbQg8my9j8nCI5zRiu7ARybbtPNb6mFLtLGWeUa9XfPLyFVp5Hj14gO16nH+9eBTnhaNlvapbBApjDM5ZTGLi+9/19N0+qm6PPfbYY4899tjjlw1vL6xIxBoP72MnpEnNjuD5gVx1fR8voMXrhFmlJNbZN0KMFI8eP+Hg5Ix8lHF3u2Q+n5PnOVVV0fUdaZpRjkqq1ZLRaITtW8rRiNHsAKkEq82SoihwPoYfaa1xziJlXJXbBsVsSeKWrAohouXQBwIBqRQhxPAeOfzMD+TQJLEeprf9MAsbqywQYrht6fueJElimIu1aC2ZTCcsby+ZL5fU3Ya+aTgcx9m6aBN2eO8+pSYDSA3We5yNhLeqG4TI0DplMp0xmZ0wPjxkdnzMerUguA6jFC9u7rhdrqlWNav5mjQtKfOCUZYxKgraTYXWilFZ0GUNaW6YTieEwpMZRSoDTVNhjCZPRLQNB1ASZNRo8ULStA1d72i7LpIXAqlJcX2NBLLE4IKgD4HgA8FD23esN2uUHCGVoW7rXVqTcx5rHYnS9L2lrioeP3rAYrnkdn6HEDAexRCrerPh7OSER/eO6G3PYrHkk5cvUEJx7/SI48MSHxzXyyUfGUOZJUyCoW17nLU0bYPrHFXTsK43WL9mtWkJUpKmKYeHByRlifcO1/dgLbbrCS4eC2QACba1tE3N9fUNKkk4vndOmRcEJbmbL3j67AVkBZd3Gy4uf0Qi4fE7j/jt3/77TNOERCUYUeOcxYXIRbeLOZFMvl7QwQ+z2BLmzYbFfAFInr284q9fvWT2pa/xD/+3/w2/tdpwMb/l+vaa9WbNYnFH31e0TUffezob54td3/Py+XNWqxUmy/jkk+dcvbrF+UheBQEXLASPGJTqPBtxeHhEURSUZUkxHtN3jrIY/by+dvbYY4899thjj78jCOFzDCvaW3PfCt5eWJGL3YJaCIQRWPum5RW8H8iqUISBbYQo++F6h+1d/B2VYvFU1ZKgPGmWcnh4iHOO6+trXAj40PLq5oLQ9/S2ixUSScK9B493VRNNU5Ok2a7n09pIdN+0CDvndkRvR5qHypW+7+P2gOT1PKnSmr7r8H6w0gaBsx4lwWiDcwFrY9WIENvApvgcve0QwOHJGU/efZ/v/sWf0Aboek+q1a7WBjxKa0AOxFYRgo1BOkLgIFbDJDllOSIbjVEmxQtJ2zSs53e4pgJguVzx3b/+MZkpyNISKQxZkmN0iuuJ+6s8rq9BVGRZQtdYxpOMIs8I0zF+lEBwKOHpuh7vA857pBJo4fFBY51nUzds6iYqgkqD9AQhMEmKThK06rHO07QWiCFLbd/FgCIPy9WabbELbJOIA0rGJOKqqhmNJ9wtFnF2V3mUAY/h+atXvPP4jFRJpvdOqJuWm8Way5s7qqZiNklp8vjejoqcdyZHCBXwTReJv/AgPSDo2sCmWhCE5OzwiEwp8kkJQw+nxyNNinUQuh4loWsalus1QilOjk/QSYJKEpyAru/RQjItMiDwD3/z7zEaT1Bac3Fzw7/44z+mbmp+42vf4MuPH7G6mzObHu06d4WINUhv2rUJAWckH11d8c//pz9FCY1KCs7e+wK//3v/OQ/efYJMEhwCTzwnu65ntVzTdS2L5TV3tys666mqDbe3t3Rdz3y14MWrCxbLNZuqoW1auq7FuR6hHMYopNRIFH3bsVossW0f7eN5jsByeFD+B/yi2WOPPfbYY4899tjjP0a8NSL66VnLT9+3tbxu50h9iP2Dr62xABKtE2bHpxSjCev1GqETlNSs/RrnHMcnJ7RdR9O0TKZTrl++oK4qTJLgXAz4yfMiWhKNwZhkSKyNabhx317XyGyrV7YzcNv7tvZdAIQY6i8EjqiMJkmyq40xxnzq9958vEik+l1qcN1UMBCJPC8oygNurtdcX68wJ2OUDKAUEomUaqckCyGGYxZv53kORuEFOAG96xFNQ9401Os5+B5jDDd3cz5++grvFCpNCB663rGpOnCSg+mM0SiJ86pdQ9e1eN+hVAxZGk8y0ixDiRQpPNa2OF/R1k18f4NHSofysbOybS2tdbjgkEKitzG8CLyPIUczcq7tapgV1VjrqEOLkDoGWwmJFHLXYep9wAfHZFoAIaazpml8a6JESDka4b3n4vKGybhAC8+4KBmXI+7mK1bVBmSOCIrb+YqPg+DApByaNCqPXoAUKJWiFUBD21asNxvqtqFtGkxi0FlKVzfY3oGUJGmKDSKey0pz9OABwQe6dcVqueYgzwlCxWRfE/jSb/0Dmq7nRz/8EdX1M1rrePbqJZuqx3aef/zH/yPp7/1n5Hj+8jt/yfnpfcpROcweC6SSJEkCAZyCj5Yr/t3Vkq/+/n/J6fkZ9x89oDgYE5SMyr1U4EHjSFNNWeZMJlu18vFg/45ElRCXh/q+Z73Z0PWOpm25vLpiva64u13w0Ucf8vLVC25vbqnrBmc9TdXQNX1cQFGgtWEyyf9Df93ssccee+yxxx5/x7APK/rFx1vtEd2SLx/83yB8223gtfr4Wo0k1pQgOTg6xmQ5D6YHpHnG3e0dNzcx4Ge1WoGUTCZTKutABIoiA6FR0lDXLZPJhMSkWBdVye1zbLGd+9zu05aAbvdre/vNZF1p9I5Ed32H1gat46HeEsWu60iShDRN6Hu7C0MCTdu2JEkMzGmaZleLkuUlQmo66+mdR8oh2Anw1u06PK2zsX8TGZOD1yuctxweHNG1Lb3zKDNCeMni5gYJKAS3txtW647gA8vVktQk9LYfVOieNEko8hRjDFoLdKep64a67um6AMIhRI4IsRdTGzHU8MTXpBDMRgbnJb3LmS/ntE7Sd562tyB6tFZDx6tEicBxPqZ3lvlqQwgeUPjgqasNvXNRZX7jfNJaEOgoypwyO+Dl5cvXiceCYX7Xkhc5TdNyt2yZlAX1zZyToyNOjg6xruf25obJg/tMxmNyYxBB4DygU5rGcbdacXF5xc3dHX3fU2QZ5aSgbhq6tqWtDc7HcJ+q6VFpgjCGZDIiCEkqZewgtQ5tUqrNDaPWonSCDxZzdsLsK1/F5QUnv/f7dE2PDI6bq0uCdcyvbvjxd/6Kj6s1X3t4j1MlaeuW22cvuLu7425+R9O2PHjwgG9+65vcSUV/co9/+Pv/BemoJIiAx4McLN0BQCFV1PQZFnuUYjjucXTVhxDnWAekqSIrUmJ7Kbz7zgNAEHx0NTR1x2I55+XL51xdz3nx4iVPnz5lvlyyXK5RSu7ewz322GOPPfbYY489fnnw9sKKZLxwlVLgvcB7h3U2WlRlTNvcEsCud0O/KPS9xTqPtRZHwHpP17S4rmcMJGnC48ePWa/XrNZrdKK5vnrJ/OYS4SPhy8qc8eyQqmkoRqOoUtqeEDxC6NhT2XW4N9JYtwR52wHqvENpTTr0iMohsdc6FxUo6wARLbEhRNWw63adoWma0nUdIFBK0Pcx4VVIiZSxTsYFT9+2tNWGi4uXLBZLmrYlT0BIGWcDrcXZjr5rcN4jEPTWoVVCWY5o6hYhFMakBCHovKdIc0xW4rykqzq869Ayod70BCtYbzaI4NmIQF6WhGDpTEaaGrq2QUrBdDodEmEF66rGuQ4bcrJcMxkb+s6gzbb6Ju4rITApC5IkIcsDm6ZD3K4RVUPfdfTe4R34pkKKhHJcIqXg/HCEFoHGQtM1MZV4WIwY9FMApBAYrQBP37eoiUZIhdYJUgq6vidRBpSi6XqKIqGuNlR1w6jImc8XzGZjHpyf8fTpU569fIkMcP/9h1xVG/7qkw9YX99S3Syp2xpvHULF9z0TGZPJGJMlrOo6dqxWcbb46P45Mk3wNmC7nmimjqZirSQi0VRtTV1tGGcZEoFbrag+/DHJV76Emo0o+4ATjgenM4RQPPTwtd/9Heha7p4/R/eWsfOYq0um9YZHzZp2vWaxqriygcPf+DXu3b+Pk8PCj1AIoQnbhRQlCT4AQyoz4H3YWX1hCMPyHq2i8hxCVLCNFoOd3iPDQCpVIARIk4zZ7Iwnj89wLv5eN7gU7hYrLi9f8ZOf/OTn9r2zxx577LHHHnv83YAPfG4zon6fm/hW8NaIqFBblTEgpEAEiRzsldv+TQCTJDG10/Y453HbYB4JZT7i8ZMnaJOyWCx2RPH25hatNaenZ9RNTaoVrq2Z31zRtR2Tg5TJbEZvLXVTo7SiKEuapiGEFmNihUvbtruZUYiKrJASGTzBxgoapeJ8aNf3yKEvsa6bOAOXpsgkpocqtU0Mda8TdAHvHUoZhIjqqzEakySsFnfUVUW9WHL5/BNWdze4rqXIEoo82YVMW2tpmjqmz8Yji5IalZgY3pMkUcEaPqw6SUmzgiwvorrlHN45ur7j+uqatm4QPpKIddNQ9Q5nA3lq6TpLIqPdc7HZkBf5jmS2XRPvGxlGI00+SvE2pg9r3Q2W52jBTZTiaCJ4eJyzWC2hzNgQ8ERLshYeKQQSGBUpRniMmnG7rNE6EsqqbQYiKnZEVBAQIpClKX3fUdWrIVE4oHUCCDabmiRNkXiUUkxHI5pmg5KCxCTDewXnZyfUVUffW6q249mLl3znO9+nRPFkdsRklMVvLQFJkvLo3n2ePHqMFJLWWbrNmqZpmEwmZMsF+WiEVoa2aXAuVvgoKXHB423PzXKOSg1mVKDzHN/3cHtH88FTsi98Ea80Ssbwq3j+C0SqCUZx+OUvoYbzKXcWicduNtR3Cx7oBFXkpNMJdggOEujdF65UcWHFh7D7zMVwqNfWeR/sLgjLDzPTWxXTeY8fknKV1lHlBYR4PaO6va2VBAVGZ5RFysFByXvvnPNb/8mv/Qf6ltljjz322GOPPfbY4z9WvDUiaq3dqYxbu2skDHqYRYv1KG37uieqbbtIV2RUFYtiNKg48ODBA0II3Nzc7EKFnn78McYY8lRTravdxX/btvR9T14U9H2/Cxoyg7rZNA1pmu56Q51z6MQMc56R3EmlaNuWEAJZltH3/c6+m6Up1jqstZ+yF79ZWZOmKVmW0TQNYpgjFX1P38e0XGMSrpfP+egHP+CD73+Xp09/hO1qZuOM6SgnNRrvPG5QsLQ2ICRaafK8JPjtzKnFOUftPc56dLKtfwnYvsG5juAdbd1xd3eHdw6CoO0Dd2uLDR2Qsmk8qarJs4QkTVj3LVmTo6REa4mQYDvH8xd3mMTj+g15lpOmKb13dLaPqhmetuvIEs37D46oNg2fXK7RecKmtdiuw+EhxGqXVDkmo5LZWJOnCc+ubtHGcLuM1T1bQi5lDOoJxMWKvreE4HDOUlUVciDQxiTUdYu1gcQoEqPJkhFts2E0PaJta7SOwVF5lpAmBpOkHB2ecu/0llC15GnO45MjVIhzmOPJhMePHtN3HatNhe0t6/UKIQSbtmO12jAuCqbTCeVoRGKSnd08hIAj0Lqem9WC03CfVERnrLSWcHVNVxQk7zzBhk+rk9LHBZ0gBX2INTFSKYTXyLGmnE4RIeC9xRIQKgZcvdlx+7oi6fV5KoVADMo/BLTSu8/nT5/PUsoYBBXvRGqNfOPzvH2OGJi0DZYa9sHH17l9TXvssccee+yxxx6fFfsZ0V98vD1F9I2LTyHEjgRuFZStJXZLVIWQeG/o+pjIue3YrKqaQqpBEfWUZcnJyTHr9Wbo67RcvnpBXa0xWtB1PZnJWCzmjMbjHTFUw4wnDORgILNbBdZ5v7PD9rZHK70LHtoSzu2fMbHU0nUdaZqSpinNTgkz0cJrYxLsthpmm7ArvSP4gLMWgce5FudalArkZcKonJAZifBDwqzRSBkJoQ+xe7TvLQKJkhKpFcGC7T0giT2rchdstKk2NFWN7cKuVqZe19xtWu42FmTgBEXwkdD0WIztkI0kqTYkJsMYRZYnGKW5WSzJLgNG9Vjn6K2Nx3CgQM45hA/UzjIaKb7xhXOm4zs+uVpzcbdh01isjbO3WZpyOJtACBgtkeOcZV1wu2njSkWIFuXtY0diKnAWui7gXRjSihVd3yOEpCxHWLehbmq61pMlGUoIOgJaS1arNs4tytj1mecjitGELrQ8uP+YZl3R1x0Hh8fcPz6hLEt6Z7m5vWG92RAC9HheXVywWa/RWlOkJaeTKUfTKaenJzx8+JCyLIfzOqr749mUm7tbfPC0bUOS5QgCvqvpn78gmUwxpydRrdzOaMq40CBCDGwKQ0BWUAEZfzsGZimDJIYkBREtzQxfuD89h70jurxO3/XexhAuEYl/iL8Qz93wen9iZ+7rwKw4//2a6AYv4nsVhpxj+XohYY899thjjz322ONnQeBzrG/ZX5G8FbxVIvpmMNCbCkqcGx3I2UDUtqqlEhKhDCbNOTk/4+jsAZtNzXK5JEkSlssF19eXFEVBkmZYZzk9P6NtKmxbIaTk6PiEar2hWm+YTiYkSlNXNUJKsiyj6zq6vsOIBJMkbDYbTDCYJIEQYoCRGJJt65qmaciyjCRJsNbSh9gHuk3TtdajlBlSd0EpQ983COHRWsXO1K4jlwqjExbzG5Y3L3n59EOefvwBV9eXSCRFlpMogfAO53p8cCBizU2a5vCG+iRCDDMKwVO3Dd0QCGRCJKJd2+Ctpa0tddUggsdkCr/wjPIsBi4lEgho4QlBYBEIH/BtS0DRyh6hGlKtGbkRo3GBrXrqVlHMRvR9gBAVV2/doIoBUlDXNULAaDzm8fkMaQJ5qri8rdk0Ld5ZFHERwijB4WzMYr3h8dkh8598ghseyguBCJHQaQVGgneBquq4ur4mEKtitJH01qJTg1QAjk3dkySKIkvRumBT1ZSjKTc3d4zHI8Azncx4/P5XaaqWk8cN1aZm/fIVl82CB+acvmvpeje8LEVjLS9ubpFC8bu/+duczI65uLjk2csXLFYVebphUd4hhnNbaQlakhY59sbxb7/zHb7zgx/w7a//Go/u3WNycECmDc0HH1COSqzJEVogBbgQ7eFCSsL288NrWyyoYZ7TD8QyqpdvqplbN8Jrq7iPZFXKGFIVAgg1KJseH4g2YKV2qczb89x7tyOg23Nx+LDHP4fx0TB0w35KLd1jjz32+DuI/9PZH37GLZPP7Tn7IWDu80L2Ga7Plfj8Quc8n+3fhMq7z7Tde//3z/HfmP2/V3vs8bni7YUVvWFNBHbVKPECOaqNxhiapomzmQL6vkMqCUJikozbuzlJMUEIxaNHj+j7jvV6SQietm25vLxkNBph24amrsgSjW170ixD6MBysSAxhrIsKYoCiHOaSZLQvWHZ3ZJTV9dkWbZTQYUQu9ChrYIaL7I9TsR6lqZpMCbaQkMItG1LlmVkWUbbtjhnybIUKSVVXQGeplrxw+99hw++/z2uLl6x2azA9QTbYouU3GisjeFEWkY1TEqJNrHeJISA7+0wa6tJs5zOdiyWFeX0AOctbVvjrMX7eNy9tYxGI25eLcnSDJ0FpJKx/1NKXAAQKBRIwfWqom1rxplmOipRSpEXhsxoRuMRRickRtI2NX5QfLf/eecHW3KHUg1FWfD43jFGzjmYjGisY12tcNYTXI8LkQTlWUbrWsblCCHmxKLUrYIeUCpaZQmBqm5Q2rCpNvTWYhLo+paU+LqkFLhe0NQWwqBmK4GQgSzL6bqO6WTE/S98ha//p79HmuZoqag2NRcff8xHf/kn/PUHH/Dk+JhcKJI0QUpFu1iwWq04nE3I0pTSaL759V/hW7/6Ndq6Yb1c4p2nbzuSsiT4gASmowmL1YZROcGrjH/8R/+aw0nB3/vG1/jWN79JgaB9+jHZl38FO6ibcRFiuEIYCOCbFvBPuQ6k2lUBwesE6k/XKIVPE8s3HiemQ4vdbGgY/r7d9rWT4XWa9E9bfoUcPMeAkFGZ3RPRPfbYY4899tjj/x9sR/k+r8fa4+ePt0ZEt9heyG5DgZRSOzL1ZmVKmqaE4Og6h/OBh48f41F0XY/3HavViiRJyPOE6XRK13XMZjPqasPHr57jnaVrLW1rWS4WnJw/pKpq1ut1VDPTlE1dDc8Tycp2fnNbxbIln0mSsF6vYw1IniOlpK7rWLmSJPSdpa5r0jQlSVJCgLZtd/bjbahSfG3s7MfWWcDhrEWFwGa5oFqtcbYHHL2PSiLeRBNmCPTOY7SOpNFZnIvkUkoVnztmsJLIjGrZ0lZNnPlTluAcQsSZQOtdVFAJtLbHBYdWkYh4YqiSkBovBVXT8tHLSzIjKZIRzrk4z9u1nJ8ccnhwRKoUtq+iVTi0Md14sCq7vqfIx2idRKt0BkmScO94xmJVs65adDD01mNdi9b5jgx1XcdqVRFCJDMxoCiacr33SGNACNarDVIYWusIApq2i/bp3g9Ku0CmCVobeudYbTYoNabtNrtKEY/ina98g3Q6xWiN8IIyTfnS8YxHX3jCv/5n/5i//ss/51glHI9H+ACdDxycnPDi4oLvPvuE9rzjnnCcHZ0wm00xqcFZR55nQJxplR4IkuWmYf3xJ3z1/fdIv5zxJ3/xF/zlDz5gMjvkK+++h3jxCX42RZ7dj6nTbxC+LbZkcnt7N9c5bPumCrolim+qotvzc+tYeNOy+2Zf7psK6pt/B/833A7bfdl25woRE7P/fYR5jz322GOPPfbYY49fDrxdIhrY2fu2s5oQS+4BnLMDQXPDxbFCm5QgHNY6JgdTptNDFosVTRNrRW5ubuj6loODGV3f4vue48NDLtuKgKcocu7ubpkeHpPnGUpL6qbGpAnj8XjX25lmGUVe0DRNrHxJU3oZK1gEUOR5tOF2HUkauzW3ybDbqhbnHGkaU3Sd8ygl0VqxXm/QWpNlKU0Trb0xfKmg2qxp6xrvIE1SpAgYJen6jr7zKKNxRpElSaz+ALxzNG0LIs6WShXDjoSIFRzeelbziiSDalWTJAafKEQQ4D1KK6SN+bPaKKq2jxUfEoIQEATBB7SOFljfdxyXBba3GJ2glQYCWWK4f/98FyGklMYrR+sbQNK1LXmW0jmHHciO845NvaGQoxi6JGGziu9nkqUgQQiHtf1Q2+Oom3ZYuYqqYCQ+Em9jnU2SZbRNz+3dAp0mKK1iB6gA39uBVCkCgbqtyYoMlRhuF0um4xEmVYxHI87P7vPo8RNSo3GEuGqgFI6AmR7yn/5X/w35eMQP/uzf8vLmDhEEN/MV3lr6JOc7z56jyhRhNEYnHM5mFJMxwbshrGtQz11AS8V0POHl5Q1//Cf/hnvHR3z7m1/nn/3h/8jVP/lnPDk758tffMJ7dcf9P5hBViACIGQMaRpI506V/18IFoKBPG7ttyKm5G4Tc9lu98asqJQCiYznBK8Xj3hDQY2EdOty+KnKl+H2a8WWnYV49wt77LHHHnvssccePwM8Av85zXZ+Xo+zx8+Gt0ZEvXW7MBRBDJ1JkuRTSZ5CCHprQSjY1n8oRWZS2rbh7uYG2zuyvODo6ICqqpgdHACB+fyW9WZFkWa8evkC17UkaYLHIbzl5uIFT957H5MkdK7Hh4AiXjj3fb9LWe233Z/aIIBqvcFnjvF4TN/11HVNOSpJ0pT1eg1AluafsuymaYq1lq5rSZIkpsyKgLX9jrR67wk+UK1XLOY3LJYL+t6RZimJybFdVBKlCEgUQigSFR8L8Tq5t+87EJ4g3WBplVjb01tHgmZxt2Y0KRFaIZXAdg7b90PQkeL45JBXr25o+n5IEBM46/AuIOPKAeOiINc5TdOC9cgAmZbcOz1iNs3BtTTNhq5tMVqTpgXBMShvjizP8MSFBusdXbXBBse0GAOCqquo6gYX/M6WHfAEopLW9lHxhR31GWZEJXhHwKOMoneevq5I05zUBKyPCnA85j29s1Gpq+pYDRSg7VqyzOACfOXrX+Pk7Awv4jkqzXYWMiCUJJmM+fXf+wO+9M1fZ3F9y2K+5PDomNViwb/9V/+KmxfP+OHzF2ihKFSK1pKxcCiphhloCMGDlBzOpvzBb/19nr58xbOXr7C25WRU8htf+TJt22P7nr/67g959uqaf/jeO8y++o0dwfPuNbF8U8F8bakFqePxcs7F6hch8MRZTTkEPu1qYbapt4AY+n59eN03CtGaG6uMXs+Cbi0yWzIc92+7P4oQ/O4+pRQMRJz9l/8ee+yxxx577LHHLx3eqiLqvUcSAzzDT90Pry+mGapcopoiODo44uz8Hl1vMUnC7e0t3sduxlFZUtU1x4ennJ6ccXP1CogX0N45rPWMJiPubu+YHS64/+gRqdR0XUfbthRFEec1qwrnHFmWReWz7zHGkGXZpypY2raF4cJdKUVd1+RZQQiB5XIZezG1pu97VqsVeZ4zHo9ZrWLHZVEU5HnOer2m7zoInrvbG26vr2jaGttbvPOkiUZLSfCRSGwThUOIQTxba7P37rXtUQ5JqT7OQyok60XF5ctbTu6fYDJJkIqus3Rth7OQmITpeIRbLukGa7R3lixJkAKMjCTDGMh0QZoqikxz/9EZ9x6cIWRUaNuuYTFfYLQmz3MA0ixHaYOUmvWmIgioqw0AnQsImbDc1EiVkCSvzwEpFJtNRedlDMzxPh4Doeh7i8MhkpQ0TQjO0rYdWVYgjaCqarquoUuSnR16qwzavt+9d1JKRmVJnmqCEKg05f2vfh059NgqFWtvtu+zFArrLCovORpPOHj4aFACJcEH7r33Hs9+/AP+9L//f/P06oqz8ZRRk5FIgR7qW+JCjECEgJSC6bjgV8tH/Mp7j1lvakSA93/vPZAS63o2rqc1KcXhCdsM5N1cp3p9bCJ5F5/6HP37VNLtsdgq+Vtiu7XEv6mubrfdWXq3P+PTxPenrbs//bxbsiyG3/FvzJHusccee+yxxx57fFbs61t+8fHWiKhSMS02Ki9D/+FwIRurTV6Hnng0WZbhXLQ0bqqK+XzJ4eEROkmYzeSul/TF86extqXvYmUHgTzP6GqH7Tu6tkMCeZYxXyw4PjujGGV0dbO7IN/OfDrnKIpoz53P54xGI0ajEfP5HKXUjkQu5gukkhRFQZZlbDYbsixjOp0OSmi3I6Tb1621pqoqvPcYY2jbFq0k1XpFU20G9TNepCsldlZI7x1+CHmRSsX+zqE2RikVyWgQr0OIPDhrwfvYGeo8n3zwjCIvKA9KhAzY3g376VEyYToZ0TvLYrMBBF5LEq0Ajw8B5ywSSNOEyTjj9PSQg+NJDJTqLE1V09bdbnYUiERWR6VRSoX1gaqu6PouWj91yqvrW+4WS8KwQLGbJwzQtz1dgLbrcN5t21oAcD4m5apUo3WCG2yio1FJnmcsFyus7RAStNKRdI5G+OCxQ0iWVorUGLI0QSeaL3/tazx6/wtIE5OSg4+KrFLR0mt9j5ICITXOOoSKP/MuECQcPrjH7PgI11u+/2d/wov1kkmeMk7MTs0VUiJCTCb2rifg6a3jZrHm4vqGBw/v04eOLiSkBwdM33lEfv8hajYl7GpS5I4ghuCHHt5PhwVtP1NbIr4lqP+/Aot+2lr7plNhe3+E2FmDt9ja0+G1RffNedT4APH/9jOie+yxxx577LHHHr+ceGtEtGkaIF4Mizc6OON9ejdzZowBqXcX2nlRcHxyjxACL18+ZzKbkWYZZRmtou++/0XapmW9XhN8R1Utubu7JjUKHxxGaxaLOx6/9yW8NDRNR5L0jMqStuuw1mKMYTKZ7C7Yt7Oj6TALOhqNuLu7YzweR2XSWoKX6MFme3tzQ57nHB4dUlcbur6nyHNG5Yi7+R2b9ZqiyMmzjMViQZqmHB4e0nctvhuR5+WQggrOx9nSJEmp1yuCd0jh8V7hhECLOOvpXaxricFKAes7pACQWOuRSlLXLW1fczI7RLSB+as7JgdjpIwpvL53KOPw3pKnCXjPzd0So02sAQkeH3oSHSs8Dg7HnJwcMp6UCCHpuh4J1E3Pet0gpUYAXe/QOhBQ1G1H0wfWdU3btoPiLWh6x3pTUdcN3nvSxKClxoYQK3ukQLiohCIk3vUIoUAEgpA4F9NniyJlenAIUnJ8esYXv/wl/vRP/4yPP/wQ6/qYNGw0QkjK8Yiua9FCxnPOWaQPpMbw7he+TDaaYp1HSQUIvHNorRBA7y2BaAcWSscqnVjSibc9SIHOMr71D34XF1q++0f/A9f1huOyJEkZlFABIVqrnbfcrla8uLxGZQXHX/oS6t45k3ffJx1PUWWBVxKpNN55nLVo/Xouefu5IURF+s0Qoq1KCp+e2XyzMunNAKMt3qx4eZNovrmt2IYmbbdFxHoXXqfuxkipT8+Bhm2ZqJC4z7lqYI899thjjz32+LuPED7HHtG9IvpW8FZ7RLcXuEarWAvCVq2JKpjcWlB/Sn1JswSBYjIZEwSsVisuLy+ZTCas1ytCgMlkQmI0bb0iz3Lwjt71CGRM37WWR0/eoa6j/XVrBb2+viZN0x353Gw2Ozvt3d0di8WCsiwZj8fM5/Od8tk0DatltN4eHMx2BLUsS+rr62H+NZKEuq0oihwILBYLxuMxZVmyqSo2VRWDY6RASIUPgfWmwtloj8VLnG1pmpqgHSovUEISxBtW1kEh04nBO4UYAm3qtseYBIXm7mLOq5tLxocTDs8PkdJgEoUSApUVCJngvUCKJeNRjpSQpDlCeDSCYlRSTiZkWYp1FiEVXdsi8CilKUdjtFa0dRXnNkMsC257x2J1R9s0KCFIswwpFavVmvVmsyNCvXWYQTkWStLW0UbbO4cQgwLowqAum5h0KwSTcsSjBw/44te+hleSb//OP+DLv/ob/PN/8k/5y3/zP+NsT9NUJIlBak2Z53FeWUlAYZ3HAVkxipUnQNf3GK1RKtbjxG5TMyj0fugD1bu6H5MkUQ32Hp2mfPFXv8XV02dcv3zBvG4o0pwkyUAINk3NzXrBze0cPZ5Svv8lTr/8ZQ7f/yJqNAJcLKmRAhHAD8nNWpu/YZndksO4kON2fvc3lcjX26id3f3fp4a+STx/uhLmp9XWbUjSmxZfIeRu5no3AvqGzff14/C6Z3SPPfbYY4899tjjM2Jf3/KLj7de3wK8DkbZXQSzu1ju+x4vAlIojEkwJuHu9pbxeIZSCpNqptMpp6enbDYbNpsNaZry8uVLmroCZwkBnI2qkbMOlZpYtzLMfEYrbkNeFpRl7MRUSpHnObe3t5RluesPbZqG0WiEMYZXr16Rpimnp6c0TcPd3R3T6ZTpdMr19TVSSsbjMbPZjJubG5IkYTabsdlsqOsarTVnZ2d0XcdyucQ5x2KxYL3e4F20wUptUARciBbcREtMnqGlIFHR7qulxAW7C5QRQmJ0AiESAikUwVuaziFESrCw3KzpWsfN9YoPn73i/v17nBwfglEgPH1w2OAZT0rKIkGpmEyrpSAxBpUYpAyE4FBakqZmNzubpZEk1XWN7TqKLEUqhQvw8tUFq82GIs9Ik4QgBHXbUdX1jqRorT+VQOxDwPmAkJKm7XfnzZYcWdujkozp9IAvfflrvPeVL3P++DG6yEnKKV/62kNOju/z+Mk7/OG/+Kcs7+Z4Z1FCINVQoSIlmASZaL72G7/Ou1/84nAst7UxPcmQjLytodmen73tMULtgqe2NmnbdvR4Du7d52t/77f4V//P/wevNhsyI2jvHB+/umBjPfnZKaff+FXe/cY3mNx7SDCK1nVkWBCCru9AShKdAGGn2ksp6fs+EmNjsNbuqoGkfG0zf7OWJc4R+0/t55sVLEII+r7/G8TyzcTdn57/fJPkbu//9819btXTN+dO9z2ie+yxxx577LHHHr+ceGtEdHvRGy+UFc67nUIDAveG1dQFi3MerQUnJ+dMZ0csFgsuLp9TlCO0MXRDuu2DB4+GIKGcar3i1YuPqao1iVEMhkDcYPFsu5bxeBK7Kddrmq6lLEvW6zWLxYIsy3cE9+rqiuPjY5qm2c2wnp6e7lSw6XSKFJI0S9FaM5vNuLu7I0kSsizj1atX1HVNURR473n27DmT6YTT0zMWiwXeeyaT8S4sKUkS8qJgNBoBAds1uHrNZr1ASY/MUohhwsOx1DsiulXCrI12Ue88Wis62yGxtFnHZDZGFikffvKc+bqDcMvt9QqTSISJQUBaG/LEkKdZJGwhoHxAeDHYISK5UEoO3aWCvu9wtiM4F+8bFhSsc3zy7DnL1QahJFIbUJKqbuhtVDu1NsOSlNiR0TzPkdogGk/b9XS9jUnK29cpQEoQMnB2do/f+f3/FdOzU/JyzHg6BaXou57p0SH/6//6f8M77z/mj//wX/Lj7/+QdrNCClCJISlyHj56n2988xv8yre+zvTwgLZtkDLO3TrraNuWJDGkaTrUBclYheP9LswqTZI4x2otWiqC92yalkfvv8fRwwdcvXjO5fM7qrpjfO8BX/r2f8J7v/YtknKKDZ7KWTIh0VpS9w2pSkhMggvRjquG/bE7ZVTjnKPrut2McLTKvg4berO/c0sskyTBe7/7+66zl9eK+nZBZnu+bwOytjPO8Dq8SQiB7S2IOIe7fd5PEdpP7VP8fO/nQ/fYY49fVAjzH8Va/t9A9Tkv7o0/10f7/PDb/+S//Uzbfflf/tu/dZuwHxH5hUQM5/ycrLmfy6Ps8bPi7RHR4UJ2W8sSQoi9iG+Qi77vBsXE4b0gqITVakma5ySp4smTd/DBc/HqgtVgmV2v1gglmIxK+maNljAdT2nblt45pMk4vfeIyeERSkU7ZVEUrNarnRUYoKpqiqLEmIT5/AV938U6l77nk6dPOZzNOD455XZxw83VFbPpAePxhLu7azbaMJpMODg4YLleUbcNJ2eRtFrfk5cF9+7fR+lIHI6Pj1ks5mgtSRKFUjIGERFtyGmaYBtDI3rwOTiLRiKCx3mHUAopBFlWolWKcyDaNdBiux4BGKUwUtD0cX7x/OyYpxcvaboWHwQCje08bdMjZKTso3FJ03rSImOaZhghET52gCrEUHXjyPMU7y0+9ITtosHQESmEoOk6Fsslz169YjqdDkm1Adu5oZLHIBVD/2isR/FOkqYpaVIijUHJnqpesVx1uCAGN2ecLxUEEp0yOz6iOD7g+P4D2saiTIJJEqpQ0fU9o/GUb/3Wf8rjd7/E5ctXfPiD79I2G84f3ufJe+9Rzg7wIVCUJUZJ2raLf89zpBQsVityn1EWJQhH3WwQssQYTbOq8L0hzzNk8DRNS5HnGGOoNzU1gi/++rf5q7ojzXO+9vWv886vfJUuQIdAeYsIYNuG1huyNMN1HVWoyLLBxjuo/Wma0vUt1lqKvEAqSdu0SKdIkwTvHV3bk6YJSqkdaU7TNFqGB0KrVLRTa60xw7ntnUMbg5CCpmlRUmISswsDk0OVTd9G4quExLvYvmW2JHggvkpIgvMgBHKo3vH+zVTd8DdswHvssccee+yxxx57/HLgrRHREPzrC9AQewnftFryZnKn90hpePT4EYvVmvndLUma7sJWTk9PMcawXq9RfYcn8Pz5MzaLOXhLVVU7BafrOo6OTzg6O6OtW5bLJaPRiOl0St02LBYL8rzg3r1o2e26jocPH9I2DQIYj8acnZ2RpwlaKU5PTnn+4hlZlsa5Q6G4u72jHI3QieHmRbTk3rt3D+89H330EdPZjKPDU54//4TFXHF68pDpZMrd3QVNtYlKsBkslG23UweFFBRlAYOtVJuENMuRysQgHgYrbqIp0xm6rrBuQ5AidlgWOXerGu88q6s73KLGSAOiRykBwYMIMdU1ZswgUFxczgl+wnRckMhIgKVwCHT8PWLYjq17hAARoHc9TdugtaFtO27v7sjyEilNVE5tjxPxaSQOgsSHMCQLRxU1Hd7jrq2p2oZV2zPfbLB2O0c8nD8iIBCMxmOU1lhnKcqCuq6p22aXuLypNuhUc3h2wmg64eE7j0lSQ+d6hFJkJkMrRdu2WBurddq2pWkajDFMpxM2mw1VXaG1JM9z2raBkAy3WzabTVTBB9W073vyJMM6x9k77/Ob4wnZaESalzhlyIzG9j1t05AkCWkSyeK2Hsg5G4n/YBXf/ixJEqSUsUIGhTGGEDxdHxOaTVB468AHjDb44HfKaFAKOSwUbBVVaS1aKWyIba0CgRRg7fCeImiHwK43VdgsywghxDnaN+zCAGmaxp8Nau124QfADOf3mwm7e+yxxx577LHHHp8V+/qWX3y8NSJqbbcjmkrG5NwtlNC7WTQpJUEKnIeu7Tg6OmJ6cMjNzQ3L5ZIsy7i8vEQIQVHkZFlK1dScn5/yomtYLxe7C12lNGzn92yP0q9th0mScHVzzXpdcXp6SlmWfPzxx4QQOD4+pm8ari4vSbOMk5NjLi4vENwwmx1wenLGcrmgtx2TyXQgMB1Iwb1793Yzc7PZjNXqjvFoPCTlHjG/uUEGyLKMi6bbWYy39sct2TBCkGYlrutQJqCkQJuUrCgxSRq7WIPftmLEbkptsF4i9A2igyIxrHVL31s669BpipQKKXuECDEgalCodr2PSlLXHR8+veDgYMz902PSVNJ7hxzCo9brFXmR0NuYbJwoTW8tUiuW6zUvX16S5wV5VgDQdZZ11aCkoMgSvLW8TrSJx2Jr4YRY99I6we2mZ9MFPK9rQRAxwTcEmM/nA/mzpElMZq6ampOTE5RSPH/xnPF0zNHhIZumpqlrTsoTUmW4vrmGEYzHI6y1LJcrDg8P0VqzXq+x1jIajVBKDbPA8T3cbDZ0bct0PEEIwWq12s0Ud123m3UmwLpuyGcHlOWI1WpFqBvyokBrxXq9jj24oxFN0+w6bE2iWS6X6KGPdUsAt8rocrncnfsheKoqKqh5mlOtNyAgz3MCULUVxhgSk1APM7lbQrtarSKBNYZ2+FmWpUglsU2H0oosSXfEPE3T3UKNlBJjokXZWrt779q23fXbArH2RwpC8Dtr8U8n9e6xxx577LHHHnvs8cuBt5eaKyP1iIlX/lMkCD49j6akRBtD07Z03hOEJMsyjo6OaNo2XtT7QFU3PHv+jPFkRIVnsZiTJQbvox0xAF/44pcJIVBVFYezQ87OzmialvV6w9HREUVRUhQFQsC9e/fiRXeSUGY5i/mcsigo8pzZdMarV5ccH2uKouCTT55ye3fL+dl9TGL4+OOPyMuS8/NzNps1l68uKMuSk+NzLi4uWC0rDg4OODu9x/XNJaPRmOOjMy5etQghI0GRgiQxJEmKQmC7Bt+3BGtREoTSaGOQSiOUIuy8zR7pBd4bvAedGvpNixKaRCvavkWNcmb5If6Tl0gpdnZo56IaRnitikkBISiub9es1jX3zmccHk7IlCaEmHxq+55AnAvsQiAQWK0qXrx8hXdDd6i1eB/oOktVtUghCR7KIkVJEWcGpcCHQKIUcqiMqWrLuupZ156uiy9xS/AEoGTsNH316iXeWfqupZIbirLAect6tSRJEo4OD7C2Z7mYU+YZwTuatiZNEmbTKW1TsV57kiRFAN5ZHDAZj+n7Hmd7sjSF8Xj4ueNwNqOqa+qmJk0zRuMREGcnR6OSuq7pbYdRhrLIsdZGUjsuaZqGQZhkNB5h+/iz2F3bogYlvCzLnfq/tYfHbtBYC9T1HcFHNTkxCRC7Y9MsoW0avHcooxE9BO8I3mG0inZehpAwQly8EPENX65WaCUwJqHpO6RIkFpjjGZT1btFkmqzQStFWZb0bRst7WVBkhjapkbIqBx3XUc3qLxaKZq2RUo1dP7an+M3zx577LHHHnvs8XcB/nOsb/m8HmePnw1vjYhqrXBDR2PwASn9EJgSiI2QYkjRBY9iMp5y78EDrm5uqduWxXLJcr2OvZ7j8S7NdDqb0lQbnn/yIcE5utbRNhVKGUaTGUfHZ9wu5th6g5KS6eSA+fwO5wPj6YSyLHn+/DlSwMHhIT4Eri4vKMoRZ+f3uLu55tXLl6R5yfn5GX3fsVwuuH///k7FkkZzPKhwiTZk0wM2ixVlllOkJVoZ5nc3zKZThBTc3N3RWct4NGK13LBZraJSpA0qyRhND2IKruvomg22bZAi7OpFXiO8tjxLIAh0miC0QqUpWhomWVSqArBabZCDLdraHqMUSgr8MLsnPBih8CYSotY6ms7z9PkNV3cLptMR906PKPIE5wNpktJYx7ptqDYVNzd3SKlIM0MYZj+tcwQRCbbtLW3XkWQJ0ijCwCq3IVY6UegkIfSBvg/YLqbCBrF1DsepUhkC41FOnqfcXF2R5AWX15ecnp5jjObi4oLJZMzs4IDFYh7trpMRUgief/KUshxxcHBE3dRUVcVoNKHIc66vLkmTjKIY0bY115s14/GY8XjM5cUrlFLMZjOUEKw2azrbk+c585tbjI4LFFII5rd3lGVJnuUsVxV1E1VLYwxX11dkWUZZFvSux9U9SZKSJAnLxRJtBFmaYfuezWZNYhLKsmSxXKBltC8rIVku5jFcKs9Zr9bUBNI0pirP5zekaUaaZjRVRSdqjNIkWrNeLnahUM451os5SaIZFQWbQaXVWtG2FX3fkGQFRZFje4sQMCpyural3qwxJqHMcyQBbzsSrXDe4/oOoxQojUKAh2SowpFDgNEee+yxxx577LHHHr9ceGtE1FmPc5675ZLDwyOUNigdZzi1knRdT9dFddAkkrqquL29ZTKZUE5ip6d1DqkEry5e4r0jSVJcb1ECiqygr2uECKR5jnWCLC8RUvLgwQOW6xVpGufbZrMDVus1+RAukyQJShLDihLLi0+e4gNM792j6ztubq65//Aho7LkJx/8BK0THj54SNd1PHv+jKwomM1mLJdL2qYhTzPu379PXdfc3t1wdnbGbDaJilYx4p133qXrOtqmIQx9kFprhIxErLMxYEhJUGmCVIB3iPBaQY52XocQQ/DsG8ryaDxmvlxhlKEwguACF5eXHJ3fI8syNs5GImkUSigIjgBIPFpKvE4IwuGFQ/iAkGBd4PmLG9arjvGoIDWSIkt5+eIlxiQQAlKLYaYwKpvbIBsxpAJrpQjB07YtxkiUjt2WwhikFgQBddvQ9Y6262j7FodDeAg+EHTsmjWJom1rZgcTVqsFXz88JKtqjNbkeclsGucSg4Oz43Ouri/IkixaVn08dlpLTo7PuLi4RGuDEJLJeErbdggRGI1GtG2zs51Op1M2Q+9pliSsN+th3hUODg5YzOe7FNttmnCeZ4zHJXd3853NN0tTgnMIIEsSLi8umE2n5IVACk+9qckSQ5oY1qtFJHe2JzWGpornlpQSKSRGSZSEyahksZiTpqPdPljryHNJURTM53PSUbIjgLvZ0RBomgatC5Ikoes6rLVMJtF2vFwuya2jHI2o6zXOOQ4mM4wxVFWF954kMczncxCe0WiKc4HVckmSpBR5znI5Rwg5WHuj7VlKycHP+ftnjz322GOPPfb4Bcfn2CO6j819O3iLM6I+zip2ltF4QpqmCCl39ty+6+jajqZtyYuCLI/zhZeXl0y7DqkUB5MpddNweHiMHObz6nYDznFzdYWzHWmWYF3g5PwBs9kBd3d3zIa5udvbWwiCg4MjDg8PWG02dF3HwcEBXdtweXmBNgnvvf8e1abm7m7OwcEhWZqS5yVaJ9y79xA/KKHbxyzyIqbDAh9+8CFPHj0iSRJevnyJ9Z5HSYp38OFHP+FgdsTx8Qnz+R3eOfI8jwEwXpGXE0xiEDIqnViH85YgJEH4QQ8cSmn+Fz5AQghOjo64+ckVQmnyMgUDKtEkSRotwE2LGFToPMtQwsWKGyFiumoQaBRIjw5xX7TR+D4gQkrfgkLSS0FicrzvENKTJpHsROvvMHc6vL9aa1A6zqYSey2VTIfcXIHQmiTNWa1brudLbpYVfR8wQiFEABHQSpImhoODkoODCd739F3HcrEky3OePX1KMSo5Ojri5vqGtq2YjEZMRiWXL19i0ozpbMZ8fsd6vaIoRsxmU25uLtFaMRvP8KHn8uolWZoxHk+4uroiz3NGZYHWmsvLS7IsYzwecXt7S7XeUBRFnPm9uCBNU8oyH+ZXa/I8J01Trq6uMMaQZxlVW1O/iom4B7Mp87tbqs0iKqoErq8uybOMUZGzXMxp62roMQ0s5tdkWUaWaOq6otqsybKUsihYLpdIGW3sfW9Zr9ekacpkPMH2Pd57sizDWrtL5J1MJkCg73tms1lcIBmSdSeTybCYYCmyqNJuO03zPB/migXj8ZSmqVBSYbSiqWqUEDhnSRIzhD/F4KqqinOre+yxxx577LHHHj8L9mFFv/j4m63zPyckg2omCNi+o6rWLFdL7pYrlssNddvjpSYvRvTOU5QlBweHHB8dkWcp1XrF82dPqTZrurrm7vaG1BgeP3xIYmKNRZqk9L3FO8/B9AiT5tRtRdc1GJ2gTYpQijRPSfOM1WrFcrmMwSsh8PLVBavlAkLg+vqKDz/8kM1mQ5YXfPThx3z88VOEVDhn+eSTj3n54gUP7j/Au46Lly+QCN595x1MmtB0HQ8fP+b07BQpoSgL7t9/yGg0Ik0SptMp3jnqusIBSElvO5qmpt5UuN4SiGFEQkoEAj/8L7ajxv/izK3YrRAJIem7gAgKGTwnj074wte/hEZw8+ICfEAqidAK6z1t25GqlDKLRMtojVYSLSVGGzKtyYwhUZpUmxiipA2JTlBKE0KPdTVpKkjSBCkFPvhhVtKhlSbWdgSMliRGU+TZUDsSX0nVxG2FkGidcr1suV2uY4iSkEgRE2TLLGGcG+6dHPM7/+B3+dVvfpvRaIwPjqIsdgqrUorpbMrt7Q0QKIqCEAKb9RoRPJNRyWq5QBJIjEIQqNYbpJBMxxM26xWEgJLxvLq7vQOgKAqqqqK3PVorsjRnMV8AniQzdH1H33QIJGmacXt7R99b8jSjrmpWyyVKSfIsLmD0fUtiNH3XslouIUCaZMxv56xXG4KLivL11TW260mMYbG4ZT6/QSAIPrCY39A2NSF4losl69US72Ly7d3dLU1TIyQsl0tubm5ohhnSu7tbNps1xkQr783NNX1vdyFQq9WKEALr1ZL5zTVN3YBQ3Nxcc3NzjfeO9XrFYnlHwGMSw8XFSy5fvSDPNF1T4fo2zvMO6u5mvSQrMrzf97ftsccee+yxxx57/LLh7fWICkk2XJADuCDwwRHCdmA4EJxFS8V4Oub65oqmaSlHI7Is4+DgAK01fd9xdXWFUorlcs7Vq4q+rXHe09uevMxwQeC84/zojKIcsVrOubu9ZnpwjHOOu/kcKSXn5+d0XRcrONKM9959F6Pj3OTjR4+5vr5mNB6TJSlHswM61zEdj+lSw/XVJaPRJM60es+rVxc8eviY45MT/vp730FKzZMn7+Bsz4fPX3B4dMR0OuXDD3/C/OaG4+NjyiLj7rqm63qcazFDJUaWpmip8Vhil4YgvJk0GsIQVDQEzwgBQcYelSAIXmCUwmM5OJmBMlSbis5F+qqQ2CHlNghB21u88PSuZzIdDzO7xPk+MYQiDfZao1Ukq1rhQ8+mnlMWGVpLwOO8R0ox9FfG2WA5+Ie1VpRlgVSRRCm1JY4C5y1IMFmKTjOc88NLHdJ9ERwdzjg6nPLeF7/A0b0HPH7nC/gAdV0zXyw4Oz/n9vaO29s7sizj7Oyc6+trnHOcnJyyXq9Yr1Zkec7J8SnL5YKua5mMJ1SqGmYrcx49eMJisWS9WjEejREIFosFxhjOzs64u7vl1cvnjMopJ8dHrFdLQvAcHx/TrCuqukZrw9nZOV3bsQ5rTo6PqaqKzXpNURQ8ePCQpq3YrFccHhzSNA111TAajbl3fg9r43zseDQm+MBqtcI6y2x2QNu2rIfamNnBQfzseM/hwQFd3+C9x+iEw4MDBNA1DdPpdKhViSnSk8l4sPD2TKdTqirakJMk2VnWlVKMR2M2mzVaK4zRZHmGknLo4BWDxT3BGE1Z5KRJQpIYlssFV9eXnJycRCX54hVlPmJ2cEi7qX9eXzt77LHHHn87Psck7//r/Jufabv/9vD7n9tzFp9zEnkqfr6ulf/ji9/5TNt9+f/wF59pu+DcZ9ho78v8RcReEf3Fx1sjoviAFhLhAzIEfLBAoGk6uj7QWUuaaCZFTlHkHB8f03U9FxeX1E2srxBCAYJHjx7FVM6uY+k96+Wcuq5RCrq+58Gj92n7ntvbO8aTMYvlCoEkKydoY7i7u0NrQ1nG6o7Ly0uyPOfw8JCPP/6Yrm04OjwiyzI++ugjsixjVJSoIPjgRz9iOpvx5Mm79H2HtR2HB0ekaU6aGpzveffd92Ka6Laqo9ogjSLJU8oi5+7ujuPjo5guai2d7bH9tntRkaUZSgoECi8+PRe6hRACqdSu9gYRK2CQcdZSpwm5C2yeXSBMRp4kuNaRp5rSOqqupu1ayjynx9PZDtt3yBBIlQY8LkgCASEFQimc6od9HCpxXEOaafI8WnL7frB1moTXolcAITADCZ0dTCjLHO/B9pYkTWiaBhs8dWe5uJmzruqhJzNaeoP3SC3IspTj0xPuP3kXU5QkozF5mvG9732PTdXy6Mk7KKX45JNPuH//PsfHR1xdvKLvex48eIBzgefPP+Hk5JSzszOur6+o65gIq7Xi4uVLZrNDypNz1usNt7e3nJ+fk6YpH370AQcHB+R5jlKKxeKWLCnIi4Jnn3zMarXm9PweTddx/eIFp2exEuji4gKjNQ8ePMBaG5OXT044PDjk2bNrNsZweHhICIFXr15wcHDE8fExL168QErB0dEhWZbx4sULmqbi+OSEpm6Zz+ccH52SZSlPP/mYLM04v3efum64uHjFydEpk8k0zm8C5WhECI6rq1vSNGEymbJarQAYjUYkScp8fodSkqIoWS6XrNdriqIkL8csl0sSrcnLMnarVhVpkjAqCua3d2itKYo8VuhUFXmex4WLwYZ7fHIMPhC8pSiyn8tXzh577LHHHnvsscce//HgLRLRrcLlIQRa2/Ov/uc/59/+u+9T1xZjFO88usc/+oPf5+b6Do4UxiS89+4XQEiurq5YrzbkRcrt3S1+sGCuVkucc5RlSdvWpPmY4+P7zNcrAh7vLe88eY+u80ilMMZwfn4PAKVi16dzLs6lpin379/n6vKCo6NjlFJcXl7StC0n56ckvebi8hKTpkymUy4vL5jPbzm/94jpdMzHH39Ekibcf/AQIQQf/OQnjCdjjo+Ouby4pFpXHB6dYtKcxWpDnqUcHB6yXNyhjBnqa6L6NRmPQYTXHYzBvUFE44Tnbg4zHliECFjrI8EzKUbCZrEhyyFXhpAnrKuORBmqUBGEoO5aNrbHq8A4zWJXqBSEIHEoPMBQs6JE7HkVIhJRhKHIC4xJAUEIDtA0lY19n4rBvhtDlcpRQVkWHBzMMEbhfbQXr9cVHz+/ovNwu6xYrqLaN7wwlFGkiSbLEg4OZkymB0ymh/S9J9GBL7z/BearFX1nOTw43NWeNHXD/QcPWC2XtG3LdDrdHcO+tzx88JDVehUXOYDT03tIKWjaDQ8e3mMxjzUwSZLw5MmTXY3K7GCG/v+y96+xsm35dR/2m3Oud616137vfZ73NtndVMskJcWmosiS4Zg2IFgmpMhGDER2EPiDDASgISB+wQZow3I+CAkgG5Bg2AJkCA7C2AqE2KBEhKRtKi3LosUmu/vee96P/a73c73mnPkw165zL0WpL6nue7rJGkDhnLOrau1VtVatU2OO8R/DV/hhhEVw/959sjzH9z0G+3t4nnKVJn7A/Qf3KbIcpRT9fh8podFo4Hkep6cnbBYuNMvzXJduHDew1nJ2dsZ8PnPzoHVHqedLlPTZ3z9gOLomikJ8P+Tg4ICqLFFSsjcYAJY4inG25Ngt0niCyI/ZbNaEkUvpbTQSFoslIPF9z9XOBG5fwihkPlvg+T5BGCIWrgpGKUUURYzHY9I0xZMwvL11zwkC1us169Wa4+Pj7aJAHMf0+31urq6osoK03eHse3292WGHHXbYYYcdfkfhbjDtu7WtHb54vD9rrnK2Wt/3KG3J//dv/c/8jV/8OlI4i19pLd1Ok4f3HyCUZT5f4Hk+m01OGMd0ui2SJGWTFVgkUhhub66YTkaEgc96swIhiJMmlTG0221ev3rGZBIw2DtC+QGXb98QhhF7h0ds1hvOx2/xPJ9Ws83o5paqLEjTlF6v71SmKGJvb4+qKsnWa8Ig5MHDh2hrKE3J0fEJSZLS6/cIgoCzs/usVmviRkqUNFiMp3hSEgYBuixZzmb0egPCMOLNm0/o9/scHJ6xXCy5unjDer0EA40oYrNeIoTBVBVGV9v+zjviaayrM6HuZ8WCNAKkR1FWmLLCej7Ne8c0mimjyZR8tcTzAiIl8JTCUz4CQYlBVLA2BaU2pF6AxFIKQWnY2oO9ux5PIfGEj+dbMj+i0pqiyKkqt9hQ6RJtNGHkI6XF90J836cqNZ4K0NoQhBGeEni+xAtjnrwe8vJiyGSxwugSsGhrkErieRD6AWnUwvMjwjih0RpwcfEWqeDk9JSiKLm9fsbe3h79fp+nTz/BDz26vQFlVfL0yce0O10OD494+vQZw+Ete4MDPM/n9csXhGHAwcExl5eXlGVJr9cjCEJevnpBkiQcHR9zfX3N7c0N3U6XRqPB86dPSRoNBv0e0+mcq6sbOt0OaTPl9fMnRHFCf/+Q1XrJZHpDkjRppU3evnmF73ns7+8jFJyfvyVJUtqtNre313hBQJTEeJ7g8vw1cSOl1em7Chm5Ik1T0qTJ+dvXNNMW7XaX8WLF1flbGo0mSaPFzeiGMHQ1MdKTjMdjwiAiTdvMF7NtkFLaTNhsVpSlR7fTI883zOZjojCh1xuwWi0pig3NVouq0mSZs/B2u66j1QrY39/fEvw0TRFWkK03CCk43D9w56vW7O3vs5gv6Pd2mbk77LDDDjvssMMO4EbM/qP/6D/iv/qv/itev35Nr9fjJ3/yJ/mZn/kZTk5Ofsvbe/nyJX/uz/05fu7nfo6LiwuazSYffvghP/VTP8Wf/bN/9nvwCj4/3ltYkbW2tpNKNpsNf/d/+VWMsVuFTQpJr9NhNptijeXhwwe02y3y3KXp3t5e8ebtM66vL1zQTVUR1+mlAL7v0+n0qCrDcjHHU5JOtw8ooiimESW0mm1arbZ7XquJtS6AJk4ioigkz3PiOCaOIrJsyXg8JAwDLJZnzz7i8uoCISS3F5e8evIJw5srojjk5cuXvH79GoEgiSKGV9dk6w33P3yE8lxlxdnZGadnZ5RlTrPT4sHjxyTNJkVZbpNMwYU6WaybsdQaYzTGGLSu6n/XvaHYmoG+W9Vxc6SCfL2hKAsqo8FY0n6HxmGXtNnAF67TcdDssNfs0E2axMojUgp0ha5KfM8n8P16HtRVhEgBAlsvHLjuT4QlLzOm8wmT2Yj1ZkVVlXieS0N2vaEugbXZbKKUIi9yqsoluhZF7mYjN2uSOMDonDROSMIAYUt0uUGi8etKlEbaYLB/gEUgheXRo8cMBnskScLxyTHNVpsoChFCcHp6RhwltFtt7p09oN3p0253CIKA09MTpJSEYUCzmRJFMa1WG8/zOD4+ptVqkaYp7XaLTqdDmqZYLPfv3yeKItrtFmmaMBj0SOIQz/M5PT11/aBJgzAI6XT3CIKY0A/Z39tDV5Y4iggCn067vVUWe90exljCMCSKQ5K0wXq9wve8OogJp0ZLwd5gQFmfL+12hyhsoCuL7wW02x3W642z1kYhSZxSFiVRmJCmLTYbV40UxxFh6M51pVwv6Wq1qit1fIyxzGbz2hatyDYbNusNQgjKsmI0GpHnOUIIrq+vGY/HWGsZDoe8fv2WoqiQCl6/ec7t8JosX3N1fcH5xVuyPCcIQy4uLr7oy88OO+ywww477PADDovYzon+I9/4/pgRzbKMP/pH/yg/8zM/w3K55J//5/95zs7O+C/+i/+CH/3RH+X58+e/pe39d//df8dXv/pV/tJf+kv0+31+6qd+ih/7sR/j5cuX/MW/+Be/R6/i8+O9KaLGaKgVvaoqyfPCdRlisVbgS4+HZ/dQnmQynZDnBWna4uT0BOV5TKeSzWaNJz0uz99ibUVVZsxns3p+VNDpdOkNjpktJozHQzr9PeJGynQyRghJrzugqEqur6+I4tgFxmQbZrMpvu9x7949iqLAWsvpyUPyPHepp3HM8fE94qhBGMYcHB8xHN7S7Q+IGw0WC9exmCQJRmsunpxjK00jSbi9uaHIck7Oziirkou3b+n2+xyenDAajdBFUc+rNiiVdFbc0hAGMVpXLpjI2m3SqLW2rkiRn/kIWcAI0LoiX64RQFVpLt+eU+mCx1/7MsPLIRcfv0SSE9cLA2CJGk0ASl3RCCN8pRDGUhmNxHKXhXRHeUVNRnOtuR0OkUo4u64USAlB4ON5qn70u/0Ow5DNeoPnSfwgdO81mlazxf6gw/31ARdXC2wjRWDJCh+LxZceUehTmYogSijLivHtDf39Q3SpefH0GUmzyeBgj6vz12RZRrPZRgnFm5cvSNKUwcEB15eXZFlWk8w24/Etq1VIt9vj6vKK+XxJmqbEcczFxQVxHNePG7PJXYdnr+vmiOMkpN1usZgvGI+H+H5Iv99nNptRlTlJM2UxX3Bzc0EYRRweHjOdTcizjSO21nJ9fU0rbXFwcMh0OqascvwgpNfrM5/OiKKYg6NjppMRi+mYIEro9XpcX1+Ttzu0mm2ybMNwdEOjmXJ0cspyNqXSFVHSRAjFbDYniiKODo/ZbNbM5zMajYQoCijLkiAIODw8RGtNURSkaZMgDMGCMRWDvT5F7j4TjUYDsNvP28HBAUq4RaDBYMBqlREGMSIMabe6JGmDRtKgqjTj8dj1jvoBSaPxRVxydthhhx122GGHHb6v8R/8B/8BX//61/kn/ol/gr/xN/4GaeoEtj//5/88/8a/8W/wr/6r/yq/+Iu/+Lm29dFHH/FTP/VTNJtN/ubf/Jv8xE/8xPY+Ywy/8iu/8r14Cb8lvDdFFKEoigKw5NmGZiMCZL0yYUmjkA8f3UcKQdpwZKAoCs7Pz7m5vkJrQ9poE4Yhp6fHNJKYfLPG91StGBq0sWhrKcqSxXJJlm0IwoDxZEyerxFKEkQhk8mE1XIJFmazOS9fvsQAQinenp/z9vwc8EAInjz7iIuLNzSbLW5urnj69CNm8zntTo/Xr17z9uVrkjghSRKuLi9Z5zmnD+4ThD6bzYrTszMODg/wfI9Op0e30yGOIrAQRTHjycQl2Napd+v1Gm1qNVRrbE1AhZBIqQBcouxvNLcLixWOEIZRiLYVpS7JVmuu3pxz8+YtQeDzIz/x+zh6cEgaeyQeJL5gv9lm0GpzenBIr9XBEwqBQFinggpcaq2UTtEWUiCFwBiLMS5USQqFkKCUmxUNAp9mM3VhP/VMYtJI6PY6GGMYDm9r9dRDeZKTwwFHB32SMKDMCgIvqCtjAgLfp91uEUQRlbE0mi2Ucp2Ve4MBRjtVMG2kJI0G1zc3aK3p9rrM5lPKMicKfJSUXF1dYYyh1WwyHo1ZLhdUVUUYRdze3mKMJk3TbRiW77s+zdFw5FTUKEJ5Xk3iXC/r9fU1CIiTmCzPmE7HdehPVC+CCIIwpCxLZvM5yvNotdqMRmNW6w2+H1AUJcPRGM/38T2P29tbVqsVqg6kury6xBhNGAas1ytW6yVREoK0jMZDirIgjEJmiwVXV9do7Y7BeOwWdXw/IM8L91nIMjzlMRyOuLm5xRjDer3m9nbIcrlESsVwOOTm5gZdOVX74uKS0WiE73vc3Fxxe3uNsDAZj3jz+iVFniOE5fziFdPpmHany2Qy482bc4RQtNtdNqsVk/GQosi/oIvODjvssMMOO+zwOwb2u3x7zyiKgr/wF/4CAP/Jf/KfbEkowE//9E/zta99jV/6pV/i7/7dv/u5tvfTP/3TZFnGX/7Lf/kzJBScEPj7ft/v++7t/G8T7y+sSCiqsiJbrynNht/zQycM//YTKqOwwrC/1yVNQsLQ9QyWZUnaapMI15E5Ho9Yra+JwhBbVczGt0hhCT1FaaC/f0xWaoSC+w8eMxqNwTgC9eiDL6GNYbac0263uXf/vlNmdcVgz6maYRQipeLs/n10pQnjmCiJ6PUPiKIA3wvo9/e4vLyk3xvQbrdZLpZs8oz9g33KsuTl5SUIwcHRIa+u3rC5KhgMDjC65O35K3q9fXqHB87eeHtNq9Xi8PCQty+eo8uSqiqQWJTyEcK6PkgECoHnhxhrthZnUavJQF3fAhKJ8hRHj894OptSliWhH1OVmqe/9hFR2qR/cMDB4zM6/Ta3b84pswxTaKyxFJsKX6htryrUn1PhkniFAKlACOvqWjKN1RaURXnKdYY6dzBhGNLutOh0WkRhzHQypZk2aHecFXYymbBcLdnr77HZrFkspsShoNUOGE9drYzvK4qywA8kvh/Q6fXR2uAFASiPy8tzmmmTo6NjhsMRuijp9Pr4fshmsyIM2jx6/AGb1ZrJcES/2yEMPJaLOaQpDx48YLVeUpYFnXYH3/dZrVZIqbh3dp/xeMjNzTWtVpez01PW6zXGGA4ODpmMx0wmE+KowYOHD5lOJvi+4uTkiNVyRb7JaDQanN17iLVu8eXo6Ij1as1qvaGRNLh3/wFYjbaak7P7zGczFtMZzWaTDz74gNV6SaVL9g8OSJtNgtBHeZIHD++7KpjNkma7RZQ00FWJsYb7Dx+TZTlKSaIoZH9/UNe0FAwGfbIsw/cDlPI52D90CrunaLd7gCQIA5Sv6HS7GK3xvYBuJ0YKZ+P1/ZA4jBBYQt9ZgmfzMY2kiUUwm02J4pgoibe9rkkjochzXr18ydHJEa12/z1dhHbYYYcddthhhx9UWPvdq135fmjw+eVf/mVmsxmPHz/mR3/0R/+++//En/gTfOMb3+Cv//W/zo//+I//Q7f15s0bfu7nfo5Hjx7xz/1z/9z3apf/kfHeiKhSikYjZT4boXXF49N98lLza98+Z77OOTk+Jmk2qfIcLwzwPI/VckncSLFImq0Oe/v7bFZLri7P0VpjTUWWrYmTDvsHJyxWG25ubrBWkCQJz549pd1p0987wAKj0Yj1es3p6SnD4YjRaESn0yFJG7x8+RKJ4GB/H210nYDrs7+/z831Dfn6lrBO1b28vGQ6ndLv96mqisViQaPR4OTkBM93dSMnpw8Yj8Y0Gy2iKKwTWGWddOpzfn6O53k0kmRrvbXWIpTE3NlxrUsqlZ7v7LCins0EwNQdm3Z7n7M+C9rdNu2DPqPzG0yZ4XkBQRhTrHPOn74kmy/x0pAHP/pVFosZ05sRIQGT6zFGG7TUGGucHfg3+H+lkEgEvvIQWGfD9R1JtQh0pQmCkCRJSZIEwFlTj46QHizXKyyCVquDlB6r1Ya8KLm4GbKoSRrSqcBJI8IvFZ7vE0UxQknSdoter4f0A779zV/DU4per0dVFZyf3xIlEWEY8c1Pfo2D/X3uPTjj+uqazWrN8fERcRzz9OlT0jTl4cNHDEe3vH3zltPTM9qdNs+efUKz2ebB/YdUWnN9ccXjRzHtTodXr14RBAGPHz3G8zzO377m+PiEXrfHfDpluVjw6OFDAF68eMHh4SF7gwHPnz1DKcX+/j4egvPXb2n1OuwfHPDi+RO01pye3iMMA5aLGVprOp0Ok/GE0fCWg4MDrLW8evWKZrNJr9djtVpydbXg4OCANG1yfnWBHwR1IrTh/PycdrtNv9/n6uoKgG63i1JO7QyCgG63y3Q6Zb6Y1scrYjwdE0YxURBSFgWXV1eEYUiapkynMzxvRRQGlHnGeDLE931CP+Ly8i1JI+Xg4ID5fM56vabb7bJauRqcIAg4PTsjzzcsZpMv5qKzww477LDDDjvs8H2KX/3VXwXgx37sx37T++9+/o1vfOM7busXf/EXMcbwEz/xE1RVxX/9X//X/PIv/zJaa37kR36EP/Wn/hTd7vsPi3xvRDQv1lhdkBdrp6wZjw9Pj9jv9JjOV/xjX/2AbqfDcrGkKis8P0AqxfD2ltAP8HxFvl6gTV3VslogPYGUTbwwZJ2tiZKI6fWYqqrodLo8ePCA7C6UJYocEfDcW9DrdRFC0Gy18AM342a1IY5jhBBMp1PStEGr1UFXhqeffMzZ2RmtVovr62uWS1e7IWu7Z7/fZ29vjzdvXnM7vKW/d0Sz2ebVq+ekaYt79x4ym0148+Yth4eHNBoNfN9HCUkURSjlbUmmFHUyLsKRUQTaWMC8CwpCOPJnLYJ3Bb9CWJQvOX54xnQ8pVjkVNoQhQleGFCVmvHNLebaYMoSvxXz8Ec+YH6z4KMnzzjrOdJujHnXS4qt60BdhYsnFWEQUFUV1mqk9FBKEPghYejhea56RUpJHMeUZYnWmkoXxEnAepNT5CWe71J758slby5HDG9nSC9A4KFNCbgFhSRJ6HQ7tPs94rTBcDik1e1x//4DiixjvV5xenrM9Y1HXhS0miEfPH5MURSsVmtOTk6YjMYA28WEqqooy3IbMhSGEUopfviHv0yeu07Xo6NDhICqqsiyDWdnZyyXS2azGa1WCynPUMrHAg8fP2a+mFMaTbvdRsq7ihvBvQcPXLdmo4FSkj1zRBSGGGO4f/8hi8WCOG5AHZ7leR6r1YrD/X2XTKsNQrqZTHCD7QcHhyRJgzRtEkcu1VfrCiGg2UoRAsIwIggCjo6OmM/n23OuKIrtnHG32+V2eEMcJURRhO8vKYqStJGSKrcYdFch4/tL1usl3fYxVmtGw2sODo7B8xmNhjTbTaIoqi28PlEUkec5o9GIo6Nj4jjm+voSX6kv6rKzww477LDDDjv8ToH9LiqZv2E7T58+/S1vYm9vj/39/d/2Lrx+/RqA09PT3/T+u5+/evXqO27rW9/6FuDaC/7QH/pDfP3rX//M/f/2v/1v87M/+7P8kT/yR37b+/vdwHsjolZvWC0XrNcrNusMgY82JcZWNGOPbquBriqshSAM695En26nTSNJmc9nZOsVZZExGt5SFTme7+GFMb3+HsvVjMN2yv3797m5uaGqSlrNNhbB1eUFYRwxGOyxWq24vb2h0WjQbHW4vb1FG0O71aESJaPxEGvgYP+YxXLK69cvSeIGH/7QDzGbTJjNphwdH1OWJXES4Qc+/X6fKI5BCLr9PrP5nDAMCAPfVb1UBRZLpksmozFxGNPr9Xj64olL2m026R4coi9LbP3YTZZhKo2nXN+mwSLvqlqwWzuum+K8+7kAARZL3Ex58MMf8vTXP6ZYlxTrBVIofM/Hkwpp4fLlWwpT8fqjF4xGY9bLDSfdPtJKhPM/YOopUWHvSKggVCFRGGFtQRAIpADf84nikMH+PlGcgIDKGvLC0EwjvMAnX5Zc34xQymO2zBiOphRVSRiFVKVkk5eIQhMlCb4MEBaXENvtc3h6n72DEzqdHi9evibLS05Pjnn18gXV2zccH58SRjEvnz+l2Wry4N4Dbm9vuB1dcXx8ih+GfPLJx6StJmf37jO5eMtodMPegUvbff7JJ67vcm+f0e0tb1+95vDkmGarxbMnz0ibKcfHx1hrubi8YG9vj267w0ff/nXiRsLxyRlFkfPi2RW9/X163R6vX74ijiMGe3uURcaTj9/QbHbY2z/gzatXeJ6g0+4iheBFrd6DO1+UlGS5mxE2xi1AmFr9ttaAcaFVL1+8pNVq0Rv0WC0XLKZzklaTTrfL6HrIerUiabaI45jb21viOKbZbDKfz7m9dSp/u9VjuVyw2WxoxE3KqmA5X+B5HoNBn+lkQp6t3SKNFcxnK6Io4uDwjEpXKCU5ODqhKHIWiwX9ft/1ia7XtFotoihCCImQitOzx1TlbkZ0hx12+IIgPoeNT3y++AzhffFfoT4bS/ibIxKfb3FPvoeYkNLq7/iY//7N48+1rVP78T/q7uywwz8Qf/yP//Hf8nP+vX/v3+Pf//f//d/271wulwBbB+FvRKMOd1wsFt9xW5OJc5v9Z//Zf0aapvzVv/pX+cmf/Elub2/5mZ/5Gf7L//K/5F/4F/4FvvnNb/62KmG+W3hvYUW+5xQilxCbkiQhfuCjtQZcCEwURnS6XcqyBCDPMrIsYzqbIZWk3emANSRRSBzHYKGZNhkM9thsNkwnk63Ss16vUFLSabcJ/MARMM+j2+3i+wFRFBOGIUopysL1hzbTlOlshpCSJInxPI/JZII2miiOWa3XXF1dIaxFScmTjz/hzatXdLpd5vM5FxcXWGuJk4g3b14wngy59+Ah7W6PymgOj464d3pG2kgJwoDDg0OaaZu9/SOCIMYatis02miEtVhjnA3ZWrQxbnbzrralJqKOgN6RUPczqwSdgwEPf/gxURoifYFFU5QZq82GZZ5TGI2QksXcKc1hHNQJuJ9y5NarT1prPOnhS0UUhAgLlc5pNCLiKKTVahIEHo00IW2mIATS97DKY7pYMJ/PsdoyW+S8uRhzeT1jOstYLAqePX/L5eXNtp4GIAxC4igmiiKStEmr22ewf0iv2+fx4w/YGwxoNFJ+z9e+xuHREd1uh8P9fQ4P9+l2Oni+x4OH991xbabs7Q84ODxw9T1xzKNHj4jjBp12lyhqcHxySnfQI2k2ePjBY/qDPu12m1arxcMHD0jTlDAMOTo6ot/vu2MYhDx+/CF7g33CIODs9Ixut0Oj4Spczs7OiKKYJGlwcHhEf7BPv98jCSOOj44Iw5BGI2HQ75MkMWEY0Ot1CQIfId0RCIJgewuDAN/ziKPYJdAGAb1+Fz/wCIOAvcE+WZYhhcAaQxiFbLKsroJJtxZwpdQ2kMnzPILAJy8yNhsXjqSkZDQaUZYlnlKUZcFkPEZXFcYYbm6uXW+s1lxeXrJarQmCiNFownA4xBjDZrPh/Pyc2WyGMYbXr19ydfkWpWC5+s4X1B122GGHHXbYYYdP47tW3WLfOQl/p+Du+3NVVfzFv/gX+Zf+pX+JbrfLl770Jf7KX/kr/P7f//uZzWb8p//pf/pe9/P9KaJWIKUiDCPWqzVgUUoShhFHp/dopimT6ZQ4aRDUtk+n+lkqXbBablgu5+giB6PJsowkSdDGIITg5PiE1WaD1ppHjx6xWCyYzScEecbx8Ql5UTAcjknTBgcHB1Ras1gsSFM3yziZjvCUx4P7j1itlgxH1/h+wOHhMWEYYIzh+PiY6WSMH4a0GgmbYkMQ+CRJTKfTYTQaEYYhzbjJxcUFSklO0ibDqxtWiwWHx8c0Gilv3ryi3e3SbLVpNHyuLi7JlktMVWFNRVm6GheB3JKHd5bcOwJahxb9htVeIUQdNCSwQtM76OF5kuuLa0c4SwOFwhpbV+doPE8Sy5gwjFFSOQJcb+PdyoXAUxJfuRRbgUEKSBspfhDSSFL80K+7ORsYa3h9foFG0mm3qDYZk8mU8WzJeLogDGMC3yNtxERhxGS6wPN8pFD1eRKSxBFhGLC3v0d/b4+Xr14xmc148OAhL1684Pr6mt6gT6PZ4unTp3RaLTq9HucXb8nznEajQafd4/mz5zTSBr1Bnzev37BZL2m1O+zt7/HmzXN8L2B//5Dx8JbxzS3tTpdWp8O3vvktOp0OR8dHZLOMp0+f1h2jDV6/ekIQhuwfHLLZrBmNhrRabXoDVxOTrzYkDRcidPX2FWEUkcQx11eXTMMJrWaHMIi4urp21uNOh+l0AjgF1Pd9iiwnz13vahAESCnRWrtjbAyTyYRut8toNOLVy1e0Wk0GB/tkde9n0kgRSjEejYjjiE6nQ5ZlTCYT12Ha66G1Js83rtqoKCjKDCkkR0dHzr5clezt7VEUBWHowsTC0Mf3nf365OS4nlU2HB0dcVfvcnh4WBNt93ne3z/E94L6vuMv4Iqzww477LDDDjvs8P2Lu5Tc9Xr9m96/Wq0AaDabn3tbaZryJ//kn/z77v9X/pV/hb/zd/4Ov/RLv/Tb3d3vCt4bERVCEkUx3U4foy15ntNMUw4aTVrdHta6rtE8zzFGb5UbYzRBFOKpBKxmURVkWYFSkk63SxA3GI/HDPb6LFarrSoZRREXF+ckjZIgjFFewHK5QmvN8ckJi/GYy8srTk6O3WMv3+B5IWenD6iqiuurc/r9Q/b29nnx4qmb2TvYJ45j3rx+TRTHDAY9JpMxz5+/oNPp0G63mYzHpK0Wjx99QKUr1nnG8f0zRte3xI2EtNkkikJWqyXtTg/Pg9Vqzmh4RZa5jk2jDdYarARbU0GtNUrI7YqHRGDvQorgUyTVzXNKW6dTS0uz3yJIY8qiZDVfka9zjNaUVYGuSrCWKE7ptPuY0Zpykbltynfbt4CSHr4XIIXEGk271UEG7rhWlcbzBZWuGI/HrDcZeWH59otnoC1xGGOspao0lQGKijBUSGlopg2yvKIsNFIpgiAgjiOarRZppwlC0mw18ZPE1bb4Pmf3znjz+g1RktBIEi7VG4oiJwojBv09nj77hA8ef0Cz0+Xq6pI82xAfHtHrdnn+7Clpq0Pa6jAZjREIvDBicLDP04+/jfI9t91Ggq00UkkOjw558skTZxVutlg1W6zzDVJ6DAYDnnzyMX5/QBTFeFKxnC9oNpt0ez3evHxOlDSIGk282YzVckkjaaLNpxTKplMoF4sFYRhuF2KEEKjaDnbXIWut3c7fau1mUm+urpFS0Wy1XErweEyUNEgaDd7evsZaQ6PRQBvDYj5HCEGr1eby8gJtKvYGh67v8+aGZtMpwcPhkKosONjfI89zzs/P6XQ6xHHMy5fP6z7WFje3Nygp2d8/ZDKZsVqt6PV6BEHAmzdviKKIbrfn6mKGl7SabR58YVeeHXbYYYcddtjhdwSscLfv1rY+hb/21/4aH3zwwW9pE3t7e/9Iu3Dv3j0A3r59+5vef/fz+/fvf8dt3T3m3r17f59IBfDgwQMAbm5ufju7+l3De1REPbQ2+EFEu9MDBJXRXFzesn98H+mFyNoSWxQFWEtZlhhjqPKMItuwnE7RRUkcBWzyHK09lBexWCywBs5O7nMzmiCEj+/H3HvwAXlZUOiKyPc5PjlBSkVZVqRpi4cPky3ZfXDfzSgIYbahRlEYIazm7OSU0XhEq9UiDEPG4wmelDTiBpvVmuvpDc1OhziJefb8I1rLDqcn97m+PCevMg4Pz2h3u7x9+5q4MaG/v896vWaxmDiCZjRv3r4lbaS0+z2MLtBFRmVwc6F1Z6fQ76ZBkSAsaEz9Bn/qva4LkgQCg8JYTRAG+EFAlMRbgmNr62+pS7Q2mLKiGJdY49RWz0IpnVVXIYl9nzgMUQqkJ9k/PsEPXa/oZrNiupxjS02eFyxXGcPpAmMkWVYynk4IwgBPCTAGP/G2Kq8fKDqdlIvLEaKuGhECWr0e/eMjlBdxO5pwcnrKyxdPmYxH9Pb26A36XJ+f02o1OTk5ZTQaMRoO8QOf+6cPmE3nKOlzenzKfD5nMh4RRAH3HjxkvVpxfXFJf7DHaDTkzSuXpHt0csZofEOWbzg4OOD65obzt+e02k0ePLjH+HaELnIazQ4GyXw6I45i7p09YDqdgjEMBj1WqxXZZk2cxJzdv89yuWK9XDDoD1gELmhLVNBqN1mtF6w3K3rdLnG8h7UukMgLAkc+746qNS6VuZ7UV0qxXC5JkoT9wwOKsmK1yegMDsg3S6qiIPA87t0/oyoNCEm31ycII5SQVJVmMNinLAt83ycIAoQQxLH7LA4GAzbrFUJK0mZKWVW0Om2kVAzqRZkgCOjqHlpXKN+j2+1tE7KDwNvuX6ORAJbZbPq5VvZ22GGHHXbYYYcdvih88MEHfPWrX/1Cf+fv/b2/F4Bf+ZVf+U3vv/v51772te+4rbv6l7tZ0d+I8diFdn66q/R94L3NiK43G7RxX6QbjQaLxZJP6oCYKI63NSSbzKlxFlCeh6jtiHmes8k2aKtZbTYoL6CoSnq9Hqenp0ynM/I8ZzAYMBqNHOOv02Xfvn3L5eUlICiKgjdv3jKbzYiiiKurK65qNWm5XPLkyRPevHlDo9Hg+vqKZ8+esZjP6XU6vH75iicff0K300EpxeXlJUmScHS0T7ZZsdlkPHr4Jfr9AUII7t9/QLPRotvpkKYNlPLqHkeXKPr69VvG4wkXF28pK00QRERRjJQeIKm0xlhb35xlUwi2BM5Zaz97A7Zc9dPWXWPsdgazqir3fm425EVBpSu0qbDWYGoSqyz4SuELiS8VceD6I+MgJPR8vDAgbnWImh3CRhM/TtmUhuvRhMubW1YbZ1tWUhAGPmEYUpaaqjKAs5YqKfF9H60r4iQgCAWWiiiJafcGpO0OB8cndA8OSdtNokbM0ckJ8/kcz/dpt9usVktGoxFJkhCGIS9fvkRXmn6/z2Q84eryAs/3MEbz4vlzdFHRbXe4vb5mNLrFWk0ch0wnIzzp0e/uk28yppMRcZIwGAy4vr4mX2cEyiPLNkzGk5pcpZyfn1OVJe1WiyxbcX1zjkWgPMWr188Y3l7iKY/ZdMrb12+2s52j0cid59aQpinG6Pq4CWaz2daSrbVGa01ZVhhryPN8O0N9Z+G9s+uOhkOuLq6w1hKGCaPhhPFoBlYym8158eIFo9EIJRW3t7cMh8Pttq+urhiPx3iex8XFBVdXV1RVhdaay4sLxqMxYRBwdX3NcDRECMHl5SWvX7/CYpnOpjx79jHT6RghJK9eP+X8/C1pmjKbzXj71tmlwzB676txO+ywww477LDDDx4sd12i34Xb+34xwB/8g3+QdrvNs2fP+Ht/7+/9fff/7M/+LAB/7I/9se+4rZ/4iZ/YVvZ9/PHfH+x1Z8n9zfpKv0i8NyKqjcEA49mUb338EW/evKHZbLkexIYLgrFAHEdURqONYbVZI6SgKh1xCnwfIUEqj8PjU5BwfX0NwGazYbVauQCXXs8RrrKi0WhwdHREp9PBr8lLp9Oh13PKTbfbpdPpEIYhg8GAdrvNYDAgiiKOjo5Qnken2yFtNWl3OwjP2R+jKOLNmzdMJlOUH3J9ec7lxVuCKGYxX/HmzRumkzmDwQE3N9dcXV1weHhEo+GsxL7v8+D+fVrNFsPRTd1p2kNJD2MsFoEBKqOpdEVV6bo79R1Buese/TQJNcZsP12f/vmWpML2uXeEVgqFUj5K+QRRDMYSCEXkB8RBROyHJGFEEkZ4CHwhCRoxzf6AIGnRaPcJGi0ajS5ZVuL5bhawEUekSUy30yLwFVHkU5Y5Qlg8z6OqQ5iMqa3azYS9vT4PH3+Jg+NTVBAzW25o9rosFnPO37xGG8vJyQmz2YzFcsHjxx/QaDS4ubmh1WxyenaG7/tUVcWjR4/p9QboynJycsa9+w8QvsII+KGvfIVedw8pPDqdHkfHZ6yzDaPpiPsPPyBptBkOh3iex5c+/BAhIC9L7j96SLvfZbFYEAQBjx49YpNlDIdDzk4fcLB/htHQbHa4f+8RjbSFxnJ6/x6dQY9NlqG1ptfrbY9XVVV10uwGY8xnFMM7Qup5nqshCkOEEJ8JdrLWkq03HOztM+j30VVFoybRzWYDIQWHhwfs7+/TbredgrrvFM0oimi1Wnh1oFEcxwwGA4IgcLOrvS5+FNLudWm22wjqJON2m3a7RbPZpNPpcXx8j057QL+/z6C/T6PRrudpU9I0RWtXu9Rqtf6BsxA77LDDDjvssMMO/0DY7/LtPSMIAv71f/1fB+DP/Jk/s50JBfjzf/7P841vfIM//If/MD/+4z++/flf+At/gR/+4R/m3/w3/83PbMvzPH76p38aay1/5s/8Gebz+fa+n//5n+cv/+W/jBCCf+1f+9e+x6/qH473Zs0dTSdsNkvKskBrzdHxEUmjgRcETgmqw2CLvCAKQ8qyJKln4PIypyhybFWhrZsx1NYw2B+QZxXK83n0+DFX1zfM5zM6nTZKSW6GN/hhSLfbo6oqzs/fEIQR7ZYjGUVRkCQJUkpevXqFEIJer8vV1RVaa9JGg/39Pa5vblChT7PbQfgew/GQNHFVMa5SRXB0eExZFUgF+weHnL89p9PtkTQSrm+uKcuKXm+PIA958+oVWZZxfHzK5dsLijxnOp5QlW6+sqoKMJV7U3CqpsBicK8bU79h1tarOnZLKoWQdYiuS9J1lR8SS524W7t7BRLlqXozon6ORbUUZrgmFD74AXG9QKCEIlAeoXKWWj8K8aKQ9SxHW4sViiCI8FWAVJJG0kAvl6SNBgKPoiipTIWk4q4Ptaw0eVHheR5ZUSCV4uDwiL29PZJmC+H5pM0mrWYbZSwff/sjvvq1r9HZb/P1v/M/02rOeHDvPsZYbm4uAej1+/zaN75BmjY4PjlhdD3k6bNPOD45od/t8vFHH9FstTg5u8d4s+TFy6ecnZ3S73f5xjf+HmEUcnryECk8Xrx4yfHxEQcHB7x48oTh9Q33HtzHVJqnL19xdHRMr9vjxc1TJr7i0cMPyfOc588/4eDggL29PT755BOCwOf4+AQPxWqxJIpDsmxDr9cjW2/Ii5yyKFksFuRZTtJoUFUlvu/j1Z2b1lrKsnIdu56H5/su0EoIN1etNWVZYhHkkwnZZkm73WY8HgKCRtIiSiJubq/xPJ9W2sRiGY1u8P2AVrvJcjlntV4ShQFB4HN9c43ve7Q6bWaLGZ4X0N/bY7VcMplMCIKQ9XrF5cUVYZgQBDHD0Q2e59Fud1jMpyxXK+LYzUaPhkOSJOH4+P3Fhu+www477LDDDjt8v+Df+Xf+HX7+53+ev/W3/hYffvghf+gP/SFevXrF3/7bf5u9vT3+8//8P//M44fDIR9//HHt9Pws/uyf/bP8wi/8Aj//8z/Pl770Jf7xf/wfZzgc8vWvfx2tNf/hf/gf8gf+wB/4ol7ab4r3aM3NGI9mKOFzdHhMf2+fOG2RtlrOHlqVKKUo8pzFfEFVVkgh2WzWFGWOsI7waS3Z3z8mL0oarRZaCCaLOZUF6XuMp2PyMqfT7xI3YoQUhGFIp93B83zCIHAdos0mUkr6/T69Xg/PC2i3ezTTLnt7R1ghabU79PcGeL7PZrUh8AI6rQ7Xt7dc3dyQNBpcXV/w5ONvAgKtLc+efcJsOePg6JDh+JbxZMy9ew/pdPqs1muXwnp4TK/TAyHodDs0khiL5eb2muVyQbZeU+QFVaHJNwVFVqIrQ2UsRVlRlBVaG7Sp1VBj0driBDKXTmwRICRSeSAl9q4PVCmEVAilkNJHKh+lJFIIpKfw2zHBoIn0JWHg0wgCIqmIlEegfAIpt6p0VRmkkgRRTLvTR3oejTSlkTYcqfWUS+GVgkYSE3k+7WYLi6CoKrQRbArDcp2zKSq0lazWK66uLxiPbijzNcvxiGff+hbz+Zrje/e5ublmNpvz5S9/hb1+nzAIOD09YzDYJ0lSrJI8ePiQtNXEiwIef/Ahh8dHdDo9mu0eh4dHCCxaVxyfnNDr9QnDCCF8Hj/6MoP+MWEYcXh0wOHRIYHvYXXJg0cPieKIsihopm4RIo5jPN/j0aMP6ff2Mcawt9fn8PCYVquD5wU8ePCAXq9DHId0Om3AYnRFWeRcX11SVdrV9iBdDUwUYExFGAV4ngJrEYAUAiUUQiisdTPDYRjieZ5TtZXCDwOC0CdJQppphzhqsL93iFKKKImJ4wjfU1ir8YOQZtpktVwAlqCuUtpsVkglkMrN/XqeTyNqkK8z8nVBGLggpeVyie8HhEFMlm1oNBokaYPFaom2hihx3aGr1Ro/iIgbKYv5nM1qTRCE7+sytMMOO+ywww47/IDid2J9SxRF/MIv/AL/7r/775IkCX/tr/01Xr16xZ/+03+aX/mVX+HRo0efe1u+7/Pf/rf/Lf/xf/wfMxgM+Lmf+zl+7dd+jT/8h/8wf/2v/3X+rX/r3/oevpLPB2E/7dH8AvEn/+gfIIkj7p2eEicRfhSwNzjA9wMXYuN5VFWFXyeEKs+jLEuEFIxvrynzAqM1Vnocn95jMp1wfHpK0mqzXq9dVUenw2q9ZrPZ4Ps+nU6H5XLFfLZAKo/+4BAhXEqtEIIoilmtVtuU3aIot7OqSSNhMZ+6OcbARwhJURQEQUCW5VRVSeB75Js1o9tbjo6OKKuSF69fcXp2j7TR5MmTj0FIzk7vURQFV1dXpO0m+3t7XF1cUpUlSRTwyUff4Jd+6b+nkbS5d3qMNRopXJeoUgrf85BKopTA9yS+p/CkRErhqlys3c6OBkFQBzCZz9zKstymrRpTUVWGIAhRUjm1dGvpFajMsHx1S4MQP4qoKkMcJXVHq6UMBMFJHxohVkjarRY3Nze8fvUcoSv8yGc5nVFUFdc3Y7QBT7lOVoNlvlxSlRXNNCX0PYw1CCGJk9j1h8YxrXaLTt3jqaKYwb179PcO+Oav/j3CJOHDH/oyN5dXLBZLBoM99vf3Xb1Kp83+wR7X19ds1mv63R5pmvL8+XPSdodet8f1xRtW8wl7B8e0Ox2ePXu2tWLfXt8wHY05ODqk2+/y8be/RZJEnJ7eZzyZMB6P2d8/YDAY8OzZE5JGxOH+EfPZnNlstrV6v3r1iiRJ2Bv0WczHbDYboigmTV3oT1ir/t1On81mw2KxII4jlCe3x0kiEQiqqkJKCVJuj61SirLKt1VHWuu6EzRAa81stiBtOFusVIr1JnPhQr7Per1yvbFBiOcpNusNnu9t502VEnj1568onDIrpXQdpVLi+159Prkk5aqqqHSF73uEYcBqtXKfrzCqLcQW3/fQZYUuS4Ig5Mt/8Cffx2Vohx12+B7gn5Z/f1XA9xy/SSrkb/6477z+LqPPtzj26qf/se/4mP/P/+n/+rm21avdLt8JiQi+42NKqz/Xtj4vfPH59u3zwNwFKv5D8ON/+09/rm2d/qknn+txtiw+1+N2+N7gb5r/53dtW9/85jf5kR/5ke2/j//c/5ng9OC7su3i7TUX/5f/+/bfv/7rv/6FhxX9bsR7s+a+uLpFSsHHr96A1ZwcH/Iv/u/+RapC14PDdvtF2BgDdaprtt64xxiwToqjkTZcGM104mbwyoqbqyuKPCcMQ6bjsbN+5jkgGY9GeJ5PFLvE2Ddv3hCGEd1ul+l0WitZeywWC8bjMWma0shTxsNbTF2P4QcBt7e3SCnptDuMx7fkmxXtZodm2uTFy2e02m0ePfqA1WrDRuT80A99xc22BgHtdtslkjZj/DAgSSJeP33LxXLKdDgkUIpss2Y8nhBHIdZotNW1mpgQqcjNjVoBSHdpv1tSEAIlXc3HXRquEKImne/+E5A1kbHWoJSrZlFKYYVE1ITVGoONFclxj+p6jigLlPJQErQuWVUFhVLo2YhEtOn1+2hrmC8XCKnotNoYNLooMas1QRBQlIaq0giEIzNCkhcVfl7geQqjDVJCtsko8oJSlxhboauMIlvQbHeYXEiUsZydHlEJ9+LvP3jAm1evGQwGtFotBgPXhRnGMWf3znj1/Dn9QY8kbrK/v0Tbit6gS6/X4fWzT9g7OCBJEu7du8d6vaaZNmm325yfn9PvD+h02lhrmc9ntDpteoM+nzx5Qq/fpdls8qUv/TB5ntPpdOl2ujx//px+v0+n06lDgEqarTa9Xo8XL5/jeYpSOxLqCJ9iNBrS6/WJooiyLPB8VdtwS3Sl8T0XbGWtpayDi6Iowvc9RO5maz3PIwzDbbVLq9VCa711GYRhxHyxYL2yNPYOSGK4ub1AKUHc6DGfLyiKgsFgD2tyzs/fbmepR6MhSnqummUzpixLDg8Pt32khwfHpGmTt+eviKKARmOfLMsoy5L0uIWl4OryDd1ul3anz/X5W6pqwpe/6AvQDjvssMMOO+zwg43v5mzn98GM6O9GvDci+vrtZV1F4mYRs7yi1DU58iRWQBg1MMaw3qwoq4rlYkGRZ+jKqS/S9xns7zGdjAnDiPPzc1abgl5/j0bD1VqsV2v2+vuAU1WthccPH2OBXFfkecHR4QlxkuAHim4dGiOkotXpcXRyVquLcLB/QFEUSGHxlKTX65LnbqWt2U65vr4mDkIwlqIsGY1GVBqGwxFCSM5OT9nka+aLBc1mm7PTM65vbsizNVQVpiy5Pb9gPB1S5msWa5dgG4UhQkCv0yYKEoyxLlUVkEJSVs5oKxVIKVBKIurVVW0torZz3pFSow3WWKSSSKGQwiBkHWQkACHrzlDXTYoQeN0ELw4oR0v0uqDaVJhAUSYehbJUeUZs2yjpMRqNWG9WJEmCH0VOyfYjwtTDX+dkxQKLZZnnrLIcrV0vjRUCXVfFGCMoTOXUW63RVUFVbCg2G/LNmvViSblY0jvYJ9Oaxe2EzuCA/mDA008+ptFI2D8+oxwPOX/1miRtMtg/5OXLV7SaHVqtLtPJLU++/S2a7Tb7xye8eP6MOI45ODhgMZ/y5ONv0ex0SFstPn7yEUmccHJ0gjGaj3791xkcHHF4eMTzJx8TxTGn9+6TZTnf+ua36Q36HB4f8+blM2aTJq1OnyzLef3qJWmrxf7+AVVVouu0YM8LKMqCMIy2wUfGaLIsq2tUJEiL9Nwsb1mWhKGPsZY8dx2tvu9vSe3djHAcx7UboLtVKmVV0u/1KYuS9XpJGPj0un2XSF1W9Lp9iiKnqiqCMOTk9AyBQCDZHxxQn0111UsOQKPRIIpisBJjLAf7R1g0Rhv29w5YrzeURYXnBRweniAE6Kqi2x+wyTZf5KVnhx122GGHHXbYYYfvA7w3IootMfqdS2a53rDeZLTTFGsrhIDR6Jb+YM/Nm8URwlomRYZVTglUnsdqtXY2zrjBw0ePmC+WKClI0ga3o1viuHFXvsn5xQVpmpI0EjzPZ71euqCWdpOiLLm6HtbBKm3GoxHLxYJ79+8xn8+5Hd5weHgMwnJ58ZYkjNg/OGK5WrHerGl1unT6eyymY9Ik5Us//BWyPENJj16/x2aTkeUbyqok22zwlMfLly9YLles5jMCa5iPR8xnMxbzGbqqyLM1nh8gBIShT6lLQmtQysNiUDLAGMumzAk8hYdE3gUTgQsjqqs8PCG39TWl1pSVhqoilm5G1FTuPd+GFwmntDol2lCaChFJvJMuoQiwCCosVDmrxRJtLFobFosFk8kEpRSeH2AQKD/AC2MKs6HRaLJeb8iKDcv1hrxWRpX00UaQl4YwCFhuMjZFDtaShhZTaazWWG2wusQag7SGsljT2T/EYGmlDZrNBmmakmVrGo2EJDzi17/5TdJmm7SRcmMN0+mYXq/LYLDHkyefuDTZVpNer8dmvSaJYx48eMiTT56QJCmdVpf9vTVlWdJIGjSSB7x48ozAD+l3B6wO5uRFRhiGtI5bbNYrgigkiCIaaUpVGZqtNknS5OXzpyRxRJqmrFYrPGupqgoLNJstZzmv04/DMEKpO5utwlOCSrvaFsCRSikJI2fVUkqR5zm23qYQgrIs2Ww2eJ6z2l5dXTn1v9MnL9bMphM3+9wbcHV1hTGGXq+HtYbhcEycJKStJsObG5RU7A0GTGczNhsXrqS14fb2kk6nQxTFXF/dkCQJ3W6Xy8sLyqqs51J9rq4ut5b56+trJNDt9ZCe/wVffHbYYYcddthhhx90fDdnO79fZkR/t+H9EVHh+iNtPYq4yTIur64YfPUrFFmFtKBNxdXVOXEUMxtrqrJASWfHtdrNQfZ6XUCQ5xlSxlxfvsVUBd1qgC01o9U1VatAa4MUsFmvKYuiJlqWggxpwFjDZjXHD3yKfM10MkFrzfDmirzImY2H2KoiiWPy1YL1bIySzh785sVzDo9P8PyANy+fMxjs0Wp1uLi4pKoKTk6Ot12PrVabw6MT1uslAFHoI6uMFx99xO3lBcvVvK70sPh+yHg8IQgC9vcH+H6AH4Z4gY+n3AcmDENWq5XrFjUGbQRSGyorEIJ6bk9iJAhrMNpSFBXL9Rrf9/EDQ6DeWXY/7UwQQmAlYF36rquRMUgP8BSV0Vgk0vPqgKSC+WJKpSvuwpo8D3Slaxsx+GHEap0xW64pNJSVwUNRUZLlBRZBFEXkRUZelgRKEaAIfUeqMgkSuyXbVhj8JGL/+AGXF2+YLxf09w64vb3izcvntDo9t0AxmyGxnJ3eY7VaMZmMiaKYw6MTri6vKIqcdqtDWVQ8ffqcVrvF6b1TXr16xWo5p9FMybIVz198QtpocnB2xMsXz1ksp3S7PcpZxcXrczq9HkfHx9xcX2Krkt7+IZPxLS9ePKHX7XN0dsp6OXfqZFURRRGe56G1Zr1eo4Tcqpll6ay0vu9TliVB6LPZrLc/s9ZSFMX2WN3VugRB8Jlt3B3bPM85OjrCWvCUTy8Z4HsBURRtK2SKotjaeo0xxEmC8hWdTgdbq9WtVmu7D0Ho1wslIVLK7X1VVdHr91iv17VK7zMYDPB9HyEEnXabqixRnkcrSb6nl5oddthhhx122GGHHb7/8P6IaB3Ya4yrCsmLgv/+f/wfeXD/HkkYYbQmbbTQpsRUFVVVkm1WFHmO0Ya02SJKGixXCzxPMR6NCaOQZiPm9uaCqiyRKmCdz1nNJmw2GUYbwjBgk2VYa8Aa0kZKVWrysmSZrTDGUbHJ6BaBJQhCR1wVGO1CjKqqQFcVz5MGabPNZr1iePWWNG1SFRmr+YhG0iEIQpaLKUPPkYKb6xsOj07xPJ/NZsV6syT2A64u3jIa3bDaLFmtl+R5TlFosqzEGkmeaa6uhsznS9K0SafTppkmCCxJHDv7stGEgefsmmGA7/kInBUXz0cKsNpQlhV5UbLJcjZZThTF+E5GrXtGDVi5JXrizqoLIAwGSSW3mbv1vKjEV4qqKhBSAIai1LSbCWWpyYvckVghmc5nLFdrbodjVnmBQIKFihJtLZW2rPMcKZwymsYxjTjAD2rLsZUUuUGIEiVzlCeJVguKIme1XqONZXBwRqvT4cUnH6GCiJPjYy7Pz5nPZjx49BilFC9fveDs3kP29w6YTSeMh2P63QEnJyd88skTPOXTandJ0wm6zOm2uyRJgycff0y3M6DR7tBudyjynEazhVQezz5+Sq+/R6fb5ebqnNlsQqPdpdvf59WzJwwGB6Rpi9lkTFEUtNttsmyzDZQSwkPVFoG7ICxr7XZ+NM9yN+tclhRFgbV2SzyFcCFGxhjyPCeO4y05zLKMol58cedWwXJ5QbfTp9nsMh6PUErQbDbdeXpzQxRHxFHMaDTCYum02+R5xvV8TrPVIggCrq+vSdOEMArdc6KYZtpiNBqhtabTbSOlrB/XIk0Tbm5ukFI6NbWquLm+JoxjHn/xF6Addthhhx122OEHGbsZ0R94vEdrLttZM1uns/7qr/4a/8PD/5E/9pP/LGVmsMKglKQqLWHokW+s+3JuLGVZ4BsXGBRFIYv5nOXbBVVRkuc5z8pPMNZS6XL7JV1XJcZq1tmaUpc0k7vkV4mxoI1ASEkYBFSlS+Wl7uRUnocANgsQtdV3s14wvLlAa4O1Ypta6/ocFc20jQHOL16TxDG+VLx9sXGKkxSMJ0OW8znTyYTNal1X0xTkuSOhWrvtCQG+F4FV5FnJYr7GaIsfSCptCDyX4quMwuSw2qxpNBOs0QRK4QU+lbFoU5FXJct8w/VkTFVVtHttQhRCOQKkjYW6j1LUpMgpqwJTQWUrpPScKomg0O79AUu2yYjimCwvKMsK2fUY3d5iDAgU0/mYyWTIar1hsSkpqopASTxf4asAH7bqXRj4xH5AHEYEvkAIXQ8US4SSCOHImrdRLKczrsUTBkf38H2P+eSWbm+PD770ZYQSlGXGh1/6EtPpDGMNzXaTH/7yV8iyHG1LHn7wmNlsxmrjakfuP3jAZDJmMh4z6O8zm024uHxLmnZ49PiHGI2u8KeKvf1jhrc3vH7znE6ry+MPHzMcDRESDo/vsVwuKfOCOIo4O3tEXpRMp1P2Dw/J1ivW6xW+pzDapSIbYyiMxau7WYs8ByG2s71aV8RJRFE4+24jTinLknxTOLuucHOidxUudzZdIQRSKFzhkaLb6YMVxHGC50marZS8yFG+RxCGzJcLwijCC3wajQZGa+Ioxvd8tDEkSbKdP02SFr7v02paZ8eure15nhMGEWGQUJWCRiPB87xtcJPv+3jtFlme00obX/TVZ4cddthhhx122GGH94z3RkR/Y2mMxaIN/P++/j/x4z/6j3G4t09VOgJjTcb17TUCixISrQvyomC+mnBxdcl6uUKXFQK7/eJ916VYp+8gXPEoUtRVFLoERK06gUKAdjvleR6Br2oiytbmeAchpbvVdkhHRN+9Fq1LjLEsV5OaxcF64vbEVz6+5yEEVFXFYrpgvdmwXK1cuqgxFJUhLzVlZZ2d1ffJsowsM8Rx7BSxMidJI5TyCAM3Gzibz/F8RbvVYrlc43sK6UNZVEhpMVazyStubqZMp2saDRcuY2vl1AIYQy0K1ym6rocUXJCQFQKss91aayiKEm3qVFdj8ALL7e2YMIrYZBmz2YLpdEZZGSazKUWRscpK8qLEWOMsw8LDr8OVSmPQxrg0XQNxEGKtQAgFVtQLGI4wm6LA4o6r8iTNXkbc7vH0ySecnJbcf/iQX//m3wNrefjwMb7v8+1f/waDvT0e3HvEy8vXXF9f8eGHH5I2Uj7+6CMODw85OztjeHvLaDTi7PSMbq/Ltz/6JknSpNNuc3n5mvPzc37oS19lf3+fjz/5Js1Gm36vw9XNBePJDQ/uf0BVVTx/9ozT01P3uI8/BizHJ0dEYQgYdFVRFDlF4eytW8txrXZ++jwrypw8zzHGOKJauSqVSlcoz8PU4U6qfi+FEOi7ZN0wqZNzKzabDc1mi/F4TBiGRHGMUorZbEYYhuzt7bHZbByZDEOslIzHY+LY1c2MRiN83ydNU6bTCUp5NJtNptMJ83lFq9VCSsnV1SVx3KDZTLm5vcL3PLpdZ/+9vr4mSmK6vR7z6fS7dVnZYYcddthhhx1+10DUt+/Wtnb4ovEeiehnphFxM8KCy8tb/h8/+9/wf/iX/2WaSYwpKwA838OUJdYatK6YL2a8fvuW9XoDFqIgIApDlHq3bSkcwQWLkDiiisBTCiUVw+GI/f190jSl0gaFqe2pFuUpvHrW7Y6IfroKRchP21fl1tLr7vdd4AwuEVXhVFeMQRhDVWas1xlZlrNebVisVmRlQWUMFkmlLZU2dYKs3FZ/KCURou5uDHxW64zVcoOtyUYYBbTbLeRqSRInKCEpqfC8Cs+zrLKM69GE12+uqIyl2WwipaQylqqskMq95rKqyPPczS+i8GulV0iFBLQ2+L7PdDqj0i5Mx1hXyWJWGW8vrjg8OmY4mnAzGjOdTMnygtlyTZbl5FlZD5jbbWVMHIb4vsd6vWa5WmFxCrMxFqxAcqc2C7IiZ72p8D2JtRG+kmSbgNV8Sf/Q4/j4FCFguZxxdnbKzfUNeZ7T6/U5PjlFKkFWZHzw6BE3t9cspnM6nTYffPABq9WK8XjM8ckJy8WCxXJOI0l59OADxpMRxhiOjk6YTqdcX7uQng8e/xCL+ZLZbMLpyT0WiwWz2Yw4jrl///62F/Ts7MzNgUqFUpLZzIVSNZvN7cynoJ71lI5Mbmd3rSVJErSu3GyvHxAnDcqypNpoLM4tUBSFO6ZVhee55OggCLaVL8PhkKR2AoRhiDGGIPCRSnF1dUUQBARBwLru343j2C2qLJekabrdZhRFhGGE560JgpAgCEiSBmXpyKvv+2yyNc1mgzCMSNMEgSSO4629uNFoEIcRm9XqC7ji7LDDDjvssMMOv+Ows9T+QOM7Nzt/j2Bxqa7aGjTmnc1bwDd+7Vv8v/6b/zer9YqiWJNt1tt5RM/zCYKIbOPmCz0/REiPygpQPr4f4vsBnudtF0qEG0NFeQopBUI6VRQEy9UGbUz9ZT0kDAOUFHVdhVNHBa4Wxd3kVnGSdTenQLxTYaV0NlmpUDXJErX9VwinPlZak+cFq03GarMhLwvyqqQwmqLUFGVFpbWbq6yJsTGGsizYbDZUVcVkMuPVyzcsFkukcnZIozXz2Zw8y8mzgs0mp9SGLC9YLFdMZwtuboYYa2k0YuLI1cKUVUlW5JRVxSbPyfKSxXLD7WjCYrVhnRWuIkb6WCvwg4jZfMHVzS2j6YzZwhHHvNQ8ff6Sm+GY+WLFy9fnPHv+inVeUlSW5SqjKKwjtEri+wrPU26u1FYIDJ4n8ZR0aq6QTinXmqrSlGXFJsuoymr7nmitMbpCV5rVcsF4OGV//5j5bMbzZx8DsH9wyMtXL7m8vmL/8ITL62u+/fG3Wa6X7A0GXF5ccHV5Tdp06cmj8QSlfLqdPhcXFyyWK/YPDsnznOHtLb4X0Ov1ub66Zj5f0G53WCxnvD1/ifICWu0eF5cXzBdzev0+62zDzc0NAEHg8+zZM169ek0YhPT7fYrS1RFpbSiLcktGs9xVo4RRiKoDjcLQzW4uFguGwyEIQbfXxwqnXHteUJ837gPl5lgLxqMRm82GbrfLYj5nsZgTxRFhGLJYLtlsMgaDPYwxTGfTbddtlmUYY+j3+2yyDVmW02p3yIuC6WxCmqYUZc5weOss6wiur69YLBa0Wh1G4zGXV5dEcUJZaS4vr1ivM9K0yfB2yOXlJZ6/S83dYYcddthhhx12+N2G9zgj6oG1WJz9VCKx1mBw9s9f/vr/hJKWf/af+ifxlbOJOsICujI0m00m8ykWKGE7QyeDsFYoweKCd1w/pgElMdY4FU5IhPIQMkB5ASCd8ic1QoASd0mxBntXeAr1HKhFSRfgo4RwdL5OqQWBpE4Eto5Mga0duoYiz1gtVyzXKxYrZ3/MihxtDFZIjHavRcqaNNeVK1LWdSo4lazbbXJ4sM/x8THX19fcXN9grXbWx7wk20xpd5oYrKtJKSomkwm3tzdkeUGSBEgMuqxYZ0uKoiRNW0ipWG1yxpM5ZVliraQoXKWHVB5SeNzcjHl7dcWiVi6jMMAPI5ZZzvV4gvQDLm+GjEdTNlnOOqucEiZAU2G0xpqqVngtUhikNISBIgpTlJRYY4mCkCgIkcKipCCKApSnUICSjkBbqym1q56J4whPGbJszv1H97m6usQPY3zfo9Vpg3Cpv1/60pcZjW4IfImQsH90QKACiqLk9PQek8mEzWZDFIV88KUfZrWcs1zNefDgETc3V+T5hiRp8ODhA9arNcv1kgePP2A6mWCtIUliDg4P8X0fYw33Hz5gOnY27Xa7w+PHjzG6RAqneKdpE2MsUgk8q5H1THPSSAjC0KnySuKJACEUQRDR7fZYb3I8LyCIIuKqiSlK0qZTLa+vLpFCYQ2kaYvJZIIXhwRhyHK5oNIFYdjG+nB5dUm7o2g0XO1NtslpDBpYaxmPx/R6PdI05e3btzQaHr1Wl6oyGKtJGk2ssCyXKzqdnls0uDG02l2CMGST5XWPaZ84ckFG7U6vtphrwBDFu9TcHXbY4fsH5kc+X3zaX/k//t++42N69cL1dwsG8x0fo8Tnsxh6fL59M1+w5PSLv/8vfa7H/R/O/vef63HVyzff+UF1N/sOP2DYhRX9wOO9EVFtKjebWJM9TVnfIxAWrK74hf/h66yXG/7kH/9nEFDbNF0oSqvVojVvMpsvEMJZ/XStIvq+h5S1ymrstpdRCYFVCn3Xrak8pFBI6bnZQ+c/rTNxHOl1QTxwVy9ka6nJWSZrxRLhfpe9ewXvIHAkuipLiixntVyxXq9rpcld+DzPQxhnxUWAUO/mNu/+PzHSEWPHdS23t9fEScxsPkXrCqMNrWaTPC+YTmccHu6zXq9dbYlUlGXJfD4nCkNazRaddos4jMnziixz9TZFUeJ5bKtFhIAsyxyhMnVH6HTOfLnm2Ys3bIqCsio5OT5GenPenr9lNJqQZTmNJMX3PFqDfYSUVFWJEAnGGKqqoiwqdFkiEQR+gFeH7MRBSBrHICyelG7RAIExGmMqMAblhUgpiGMPpQRYTVmUZKsli8kN0lccP/phknafb3/7Y/b3+9y7d8bHH3+b+WLK8eF9Go0GL148o39wyMHxCRfPXjEcjjg9PSWNE54+e8Jgv8/+3gmXl2+YTEZ88PirdLuGT55+k4f3H3F4cMi3ht/i9es3HJ6eICQ8+fhbHBwdcXh0wieffMLNzQ2nZ6dIKXnz5jWtZquuNVkhBfi11VVK6ayynpsb9X2fbJORF8U2HKjRSBlPJgRRhPRc9c5wOKStXQ3MYrVmvV6TJAnNZpvZdILWhqjuM83XG6SxdHs9NusN69UG33e1KsZasmxNkjTwPB+tNVJK9vcP6pqYkr29fapKs9msiOLInVOLGUEQkKZNJpMhUnq02x3yPKOsSjqdDlmWMR6P8b2QTqfDYrFgs97QarUp8nwbirXDDjvssMMOO+yww+8evDci2kpDysqgraCqBNZItK3zgoxBCjcf+Kvf+CZnh13+V7/vxwDqrky3IthsNsmLkqpyxNIaS56V2zoLYwxiqyq6VT3xqZk7Rzolvhc4ddaYOvDFIqxF1Umytk6ItdwFIVFvo+5vvHtR9t2fd7OkAKbSlHlBtt6QZdk2cMbW1lspXRIslUEJ17epra3vr1+DMa7qRDj1uNFK8XyfLNsAIKXHYrHazvJZNLPpijt/spKCVpri+z5xFNbVHoqqDlpSysMagdZmS0KCwCeO4224jFKK4XTE7XDCdDEny0sEBms0l5cXbFYrWo2EfqeD7wcYberjoAn9AF1lLJYrEIIkijC+T1XmWG3ACqy2eFIReAqEcMFSUuF5EjcyaQHXhVppV+lTaUEYhOiyZDmb4ocBSd6jynLODo+RGLrdNmEYcv/+QybTCWma4PltlPRIm00UHg8++BLTyZhWt0MURXwl/hEELhn2K1/+PcwXc4LQY//ggChOkLhFhK98+SssV0s6vS6eOqDVaOEHAUmS8Ht+z+9hsVjQ6XbZ35PEUUSr1SZKYhppg9HtDZ7v4dWdnUVRIBHbrs1Go7FdiU6SBCE9mq0Oy9WGk5NjdFli7dD1fcYxSRjx+vVrt1DTbKErzXzuLLJJGnP++jW6KOnt7RFFDW5urmm1Ujq9Hre3N5RlTrc7QErF9fU1cRzT7fYYDScIKTg4OGC9GrNarzg6OkFJxdX1OYPBPlEYMhrd0O0OiMKY6WSCkNBoHFBVFcPhkP39Q+IoZrlcooUm8EOKvOD167d88GPf/WvMDjvssMMOO+zwOxiWd0rRd2NbO3zheG9E9J/+X3+NotKUlaYoNGVRkZUVm7xgMZ9Tle5LeRyGPH/2jB/64DHtVhOsxuoSYyrSOKRsNpgtligVsV5vUJ5ktV7TaCQg5LaKRCm1tV4IJNZKF4ZjK5RSjnwKCRI2mzXD4ZBet0OcRHieI4C2JpjWWjAWxJ0nwM2TUifPfjYR2BFXeTerWleh3FXWKE+6mhnzTk4VQqCse7AxxiWhSon0XXqtm1E1rFdLlB/g+R6zyZKqqAh8SbuZ0Igj0JqiKOl2O3ieJAhClHIqo+cpRxatC0iSUmGFm9sFSxQHKCkxRqMrjZSC9XqFMZo4Cbl/7witXbdqmedkG0eIrTXoqsSXksDzAIsRkrKq6HQ69Hs9xqMRZVkRBiFFnmGFoKw0laepdIWUwhFyBBqBKSr8QOH7Tr22yuBbSVmJ7SyuQIA2lHlOVeZcX71hk63o9Ae8ev6CIAxoNJsoEfDyxXPCIGLvYJ+b60swhma3j68Uz549wfc9Dg4OGN7ekhc5+3t7GKN5/eo5abNNu93n7dtXeJ5kf3+fzWbN+cVLmmmH/f1jLi/OuamDjKqqYjS8IYlTep0OF29f4wURvV6XXm+AxFLkG1CKMAgpqxJPuY9lUVWUlatLWW8KwkjieT6dTshyuSKJY1rtLqvVksl4ShKFHB8fu/TlqqI36JM2Uxd2VWmOT8/IixzPDwiTAOlJPKUQUnFwdMJ6syaJE6SQxEmDIAgAwfFx4uZOjaXT7dFoOguvVJL+3j55XrJYrmi2uqyXG5bzNWHoFNOriyuUUjTTJtPxiLny3AJIVXE9dOFI/b3B9/hqs8MOO+ywww477LDD9xveGxGNPYmHJbeGyuRIodnrNlFehyxrURYFRZZjjUWokPl8jqckSoKpKsAggWbD1VJkeUEUhfhS1tUTBVEYIu/6MbVGG12TUo/Ah6JyhBYc+UMIhFVMplOWqxV5kSOlJYpD4rhBGAQoz0Py2ZCiu5ReZx2Gd8sqAmMtwoJQys03Kg8/CDAIhDRo6+zE6FqNtS5oxpo68VcIpJJ1KA8IoTDWETaERBeGxWqJqUrSNOHs9JiT4wM8BY04IooifN9HSrG1JMNdsJLAVtr1kEo3N2uxBJ6Przy00Rijt1UzQkAah4S+h9aG+XzFdLUmzwvyPGexWlFUziYchxHtVpuqqoiikLSZEoUBAggODhgNh/h+QFlkjuj6HpV2YUTGaALlbStMPCXRWUlZOtu00W7xQGt37FStXFZVhRQengpJ0zatVgc/jHj4+DHL1Yq01UbJgMl4iJKwyTa02m3KPMf3FJ5yIUTKk1Rac3rvPlmW4SmPIPRpd7uuj9NC+ytfYblYYqyh1e4hlI9SiuV6Qbffo8xLhPAIo4CyLDBGM1vMCeOI9SpjPps71dmXVJVLunXvvVscCO7ONQ/8IKQsS0ajcZ1W6z4Pi8WCTqdDFMXc3Fxjmym9nqtHGY/HdLtdms0mFxcXjvD1+xhjefP6Ne12m8FgwO3tLXme0+608X2fq8sLQt/1gQ7XayqtiaKktucWhFGC8nys1QRSEgcRxrh9TNMUo51NOopCtDZkeVZbrz2yzRprIYpcBZGuCqRQKLULK9phhx122GGHHX5rcALRd29bO3zxeG9ENMs2rp5EKpqtJkjwpIeuDF4YYvyAIghZLpdOGTOaPM+RwuJJARgQ1s0hpg2sdf2J6+UKpRRFUeB7Xt2B+Y5o3k1wGmMQQlIUZd3LWNdlVLqurYhQSqKUJM9zppOrbS2FBfKqQmtDGIbEYUAQOCLiKQ83ViqQwoUbeUIglMQLfPwwIDAWLQSmKKly/Y541pUmZVlhjcAKN4eq6z5TXWiMrVxarwfK8ymLAk8pPnx0jwf3zmh32wgBuiyI45AgCGsV1tQ393u4m0GVkjsacFeXIoWbo/XwqEoLdVVNqEKUL9HavXe+CpDKZ7VaUUYhCFis1hRo8spwO55Q5LlLc1WC48M9et0euiw53D+oSYqkLHN0VRAEHlEcYrWmKIp36rNV+L50ZM1qN9crFEEUonWFLgvKssTzPNarFdPxmCjtclve0MgL2p0OjUbKZDSi3e2xv3/AzfU5cRzgeQFRFDObTmk3u6SNJvP5FKM0hZcTBSHD4ZBGMyVJIm5urhHW0un0UFKxWriQnoP9U5arGb6viMIIrTWr1ZogCDg+vk+WbfA8RRIndf+qI595nm1DfhCgS5eUW5a15Vy5KhXf9+n1eqzXa7TWDAYDNwM8n+N5nku1XS25urra9oyORiOmU5eAu9lsOD8/J0kSl3RbFFxdXSGlxOiK2dh1v6I1hAFeGNKJE3RVkTaaCCEoigKlfAyWssgpy4IiW6GAbLNimq+QSm5ft3tO/s6RgCXLcoIgdFbs+viGUfiFXHN22GGHHXbYYYcddvj+wXsjou1uHyEleVlRVhXGaPJ1xmaVYbUhjCMEECcNLJ5LMQ0DQt+ryZRxNlZpiMKASidkeU7heRRFged5ZHmGUjHK8xD1fOe2Y1S56pA8z9Fao2o7ZF4/N4oDfM+jKErCICJttNzz6jRbbQybTUGWFUw2C4zV25lPK+rHCYGnBO0kotlIKfKc1XrtQmU2OWXpniOERBuLsZayKKkq40ipcFbgqnIBPxbwVIDwJFZDWRYM+l2+8uUPODroE8cxCElelWAknhfUQUPaJRNLb5sorKu7WVmzDUVyRM9irQs3MtpglAEcEfU8p+Z6ng9CkuUaO53R7XRQSjHo97i6uSErNJPZgs1y5ayfdSjPYrEgCSPKvMBqU7/H4LdSyjJjtVyS5xlJHBFGIUWRu4UCXSGEh5TCqdLSLTBsu2iNxvM9N1tpK/LlhNH1G7wg4PnTT7j/+BGNRsqTb39Eu9/j/tkjZqNrnnzzkuPTB3R6Pa7fvmakrtnf30cqwUfPnnB6ekKn02Z4fcX524qTkxPQFc+fPuXh4x8ijCLGN9es5jPa3R5VVXF1dcHx8SFR5PPy5UviOOZgcExRuAqXe/ceIaRksVgQRRFpGjOd3JDnBUkcYxGURUHSaNT1Kg0mE1eTopR7zcvlkiSOieOI2cwR0TgKUbgU6iiKCYOA2Xz+zoJtdN3/6uH7jpgCTnnt9VguVzV5dEpxtto4kmoN49GQKIocoV2tEMJiTEVZFlhd4HtuMWKzXhOGLmTJGIPneRhrWC1X+L5PGAVs1ht8P0ApnyhqsNmsSJKIr/yBf+qLvPzssMMOO+ywww4/6Nil5v7A470RUU9KKl0RCPB9he/HrIUiUD5lUVEZve2JLE3Jq/O3+FEIIqISLnxHKoFEgfRoNHyKckLgu67R1XoDUpFrQ+i52UcDIAVWWISySC1RylLpkkimgFMN4zgiTRvORuvnWwLgiI8jZVIIkih2852VRmu9fUz9m+rHQaQkGE1ZVWR5Tl6UlNpijAsmstZitK730Sm3Fou1AlPfSgvSWozUCA1SeJwen/B7v/ZlBv0Wnq8ARyQ94aydvucjpHDqq3WKr1ISqwRGmXf7a8y2B9WRUounnALmxFOF5wdoYwkCn0bapNKGpJkgbqDIMoIoBuFxdnKP2+GQ8XhOoQU6L4ijkKqup5HCYnRBWWiEKPA8BUYRBwE2DMkLR+5lJAhDnzjymM3m5Lmuyb3Ei3zCMKjt1hXS9xBSUhqNqjQyy1iNbtBCsFiv+Hg5JAwTrBC8eXbNfHiJ7/uMJ0Nmkxs6nQ5lWbJcrLl4kxLFEcvFgvH1BWmaYEzFYr5k+PYVWEueZ8wntySNhKIoKIqSVrOL5ytW6yWzmzc0m02KsmB4vmY1unUEb7Pho9mQKHJ1JdkmY7A/wPN9NpsNSZIQhD6b9Yab64oojGi1u+R5wfjmLY1GShhGjK4uscWaKI7INms2izFhGIJ1KcdCCBppw/XOZhntVmsb8uVSeiEMQybTCUkSEQUh69WaPM9JkhhwqcxJHGFNxc3tFb7noTyP+XTi7NTaOHKbl3if+nwIaV08Vp02fXdOuVCuu7/XjgTUdv75j/yJL/b6s8MOO+ywww47/IDDiu9iWNF3aTs7/Jbw3ohoEFpEKRAotLYMb2+cDde4fkKERHohgR8iLQwnc/7O//JrfO2rX6HbStHGEFiFChVCuOChZtrEak1Zajcbeqf+abutiJBSIZVxs5fSpcMWRY5syjoFV9b1L65WJoriOvm1qrdxZ+91SyfWWmfX/VSQkbW6/l0SJQWiqijyDWVeOJXROPLq0l/dY13FjMtAMtbUqbqSuwwjKSXWQGVAeZKT02O+9tUvszfoEASy3huxVaIC6W8VTqUU2trajuxI9F2i7x0JlrX9FuzWoiuExPNB+SHK9+tQI9epGoQh4JKKXberRnke2WaFLgukhLwsMcaiNzkigziOyYqiVrIBjCM0yqOoauUVga60s9pKiVSCJEkoC02Rl0jh1HFj9HZu0aLRSmEBJRVSeU5pt4aqKphPxlghkJ5CKslscoM17hxQUnF7eVnX9BjGt2KbjCwQDJV7T4QQzKe3bi5YCjzhzhWlJMZYJkPX46qUs7p6nufeGyEYXr1y3aj1sbDWoDwPozVX5z4C4VR7IQgCn6qqtnZxzw+QyinUvu+TJAlZlnH+SrhgIaUQgO8HCFwFzHQ2o9GISRoJ8/mciefM18pT2xU/N/epuXg5JQ4jqkqzWq0QQpDlK/LNGqzBVBXWmLrzVdRJ084VIOtAqu25I0AIuw3cEr+hyMjW9UR3+2BqB8Gd9XyHHXbYYYcddthhh989eH9hRXEDJUuK0lBmm5okwXI1ByBpNPGUS0o1paU0gpvhjIubv8WXHt3n0f17tOKEhrQoz5FI3w9pNptMp3PCIMQiyLK8JpQhUnpoXbrkVatrxdN98Re1Eun6HKNtsuddlYa1d8XPd/OVd5ZWwTtyeodPFU4bl+SarTZkm/qWFeR5hTWfVY6EENsv6a5e5m7L7j4jLGkj5sPHD/nwg4cMei4NV9bzr6beH6WUU4xr8nO3nTsiirW4iT22itQdMQVqYmGRQuNHPtILkMojihOKqsIPI7SxbLKcfJNRFRVpy9WseApWyxnClHgCrBIgFKXW3I6mxJ4kiby6q1QThgFFUbBersjzHM/zCIOAsqzwlQQrUVKSmwJrBUWRIUJFUdZ+DOF6Wu/ShbVxScHS98FCWbi6lyDyUbVyKoVEeGKbYnx33AVmS7Rd/2tNrqgrdnALHo50OeIolXKJyFiE8N02bYCsSbsQAiR1jZDc/s5tT6347JmDLfEUWAlCGEy1xlbCKepaMFqOkcIFTy1G8q5DCLBY4VRPpZSj9NvFl5oU1iTQQt1JazHWKc2iPtPcPtezzVIgwwAr1GfPz7uZz08tagjhyPTdCwyDgCCIKPISIR3h16Yiz7O6gxdE5bqDPe+9XYZ22GGHHXbYYYcfUAjqAovv0rZ2+OLx3r4Bbjaa+WyBFILNZsNstkTrkna7i6d8hPSc2iNsbQ8cUckYrTV/99c/5smLt9w7Pub+6QH3TvZJk9i9IC8gThKQiiwvMNYgjGSzyQjDACk9nAUXpKhQ0qlEQriu0DAIkNLAltTdqaDUf979+111y6dP3+3f6k+GLgvXT6rfzYN6ShEFTq11ClMdUlTpLcG1tZXYNWe639WIfM6O9zk73qfbaqAUeMrNolocuRTSkQO1JUxujyRgpay3L7bEVEqJ/ZRCSk1grDUo3wX5CE9hEMSNBE8blss1WZ6TZRn9fp88y1mvNxTFBq1LiiLH8yRxKBHCw/N91psMjGU2XwAJvpIs5jNarSZxLNDGglDbmeE0ibbvphAQxwFSSTzPQ9XzrADGODXXD5xyaC2s1iuMcL20VWXxAw/PD7gTtKW6e70CIe8s1O/U3rtjv1VGpcCrU5LvyOXdTLHbj+2KgbsPPqM6I+/OFV3brmsSKutFDd4tNlCr+/X6AYH37vyy1kBwFzhlwEqnMtZbR9gtL7XGnT31y9m+j3e9tfVvBKHqBYt3Nlql/Lq/trah1/O4Ltn33eKJVK6HVypFEAR0e320UfiBXyc1e9uk5jtXQZ5tqLSmzHPy9YKiyPnMws0OO+yww/cQwv/OX3s+7/fapiw/x6Pk59qW+j7+Giw/x76Zz/2ufWd83vdi/6+OPtfjrv7J75zMbrKdM2eHHd4H3hsRLUuN1pb1ZkVRljSbzfpLb11VYgy6dBf5sipYFyWLLCeNGxgrGc2WjGaf8K1nLznZa/LP/G9+gl6nCUISxwkgqKqSMPQxxoX9ZFlep3dSq1EST3mus1I7VdSTEisVRZnXvZV3ZMg9z1r3Jf6dGvqboVaorN4SlLvuUGsEEoHvCaz1sMLD4qy61prapninwDlSGEpnAT3a6/DhBw84OjqsQ3sc6QRQSIQwuMheN6t3Zym9U7ss1s2kyjthqyYen0oWdgqq6xn14pAoTPCDkE2WgZR4QrLerHj54gVXF1dEfsTBwQGz2RRjLVdXN2wKF44ksLTTmDgKKWOPvNQURU5eVoS+SyW+s4MGYYwVBWhBWZYUlXaErT5WYRCAsEjp/pO6e91ePQcrhHAhOJ5CeBXjyYLVOiOKY/xAYSqDMaVTeIXHnQ1Y4Eioe590Pd+oagLqfv07lfTde+oUddfn6o64/NR2xPZcBmqy+071vjtvpJQIa7eK62fOIPnZc8ttV27JpP0UWd6GNvEucOrTz/v0v6217r2riacx1tXtfvp+KQG1fY6SckuyLXeLHZJ2u0Mj7aP8gCRN8cPQHfd6v3S90KEr7Z5jLWmzTVlV6KpiMRmz2SwpivU/4HO0ww477LDDDjvs8A/ALqzoBx7vjYgWZYUfumoRNhsq7b60KqUIw7CuMSnJc9fjCC48p1AuPbWqbX1FqXmymhP5/zP/2z/yE0Shs9JGUURRFuh6O3fKzF3NB3d2QuU6E7V2tkZTp/EiXErsZ8mmqAVGwT+chNZ/ExI8p8Yp3wepkJ6HJyUGKEuLNpZKV1RlBQIaUbi1KrpOUYNUiv29Pb70wQN6vS5RFDgF71OvQdz1j9pP9aJyRwoc6bojL8Y1lL6bGf1MsAygAve+BD5x0iSOGxg7ZT5ZEAQ+y+mMq/ML8qxA4bkeST9msVhwfXuLkIrlfIkCfE8RBQGhp2hYWG0coVNKEAR14JDWWF0RBj5Sho64WENZlo58KeEqbdCuVkc6Y/E2BEd4746Vxe17PedbFAWVLqmqMVJZkoYj1r4fcDdbLKVTQ6XS2/NBSokwnzrOjoEhapvqXbDVu17Wu6uhC7T67Hnw2ePx7r12j/+N1ujt398J2nfasNPkpeTTBPPTiyK/2Xn5G7f96enNO+u25R2J/ey23qmsn96GlJIi21CWV0ilGI1cErXFuQikkLVqLLZW3rs5U2vcrGwUeSgvYb3eXf132GGHHXbYYYcdfrfh/SmilXHqlhcQJ5KyVj+dcpltSSncfdF2z3OK4R0RAIlFW/jGxy+I4pg/+gd/3H0RlpJGIyUvS8qydIE9tdJaVRV3oT1GCKqqnl0TAoTGD3w8a7aE8NNfyD+N3+xL/6dtkMaA0cZ9OfcD/CBkvanYZAWlrjDG1WMoKWk1k7qC4x2xEFIilSSKIg4PDkjTxKXe1rbJOwJ19+XeGGct8Ty1tQZv5xTtOyIqeKd82U9ZRI0xKC/Aj5LtvJ8BssLN2VZFwXI2ZTIcOVUSS16sOL94w3qVs1yvEUKxyQuEVKRxhCcVoiYyvq+QIkKbCoEliiL3O5XCal1bYB2J9IREKIsRUFYl/nYBosIPJb7nuUTgSm8tpLYOZMqyHF1pwjDEWEtRZJRlSeQFGGvIsowsy+t5Sbs9lqLubX33/rMl+kpIlFQ1oVIoqVyVjVIoz9lbPU/VpP9uLaMmZepTCwY1iVPKPcdFJJlP7cOnSKC1W1UWnOq6NVCLf/BiyG9Kaj/997vP050d91Pb+/SixKfO6k/twzsyq6sKKqf627vxUetqiKT4rGXd2Dur8N2xMttt/f/Z+7dYWdY8qxf7fdeIyMw551prX6p27e7qW3EznG4u52AjP4BB2LxYIBkeeEEWFv2AZSGBMbKEBAIkHox4wMADNkK82PBkZBCW0LFoHvDpY7oRFIbjQzfurvu+rducmRmX7+aH/xeRmXOtvffa1btrUV0xquael4yMjIyIOVeMGOM/xppUt2LFihUrVqz4zFhTc7/v8dqI6PEgNRbetdJP6TQxSuqobxoykFPCNg2mjGwaj1Y9RcmFbqlWU7mY1cRS+G/+zX+H95r/8X/1U1gtiuaDBw94/Pgxc6pnSrGmugrJ01rzC1//Bl9//7/DeU3rHL/zt/5GrjeWXBIGx+UMW6nfa84v0O+T1DkRtKBQRpRQsQvLrKhWmu2u42rT0bWyD5RWKKvRxgpJKhmlpYNTmVqzglSrWDsTzExMmVJtkForSlXs5plDmR9EknFmosyZOlfkHZWi8c2GdrNFay29rONA13bkHNnuNnWmD1rfcPfsOU/vnqO0JRcIIaOdZxxGNm1H4y2bTVN3jiLlSNN4YqwJslrRWLcol1LXk9EKYsk0zhNjwBkjs74oyIlpHBdVTas5aEiIT0qQimKcAqUoIbXW0rQNV9dbtLNYY8kiHy8kbD62ppLznBMoSeYVoXM+nsiMY4iM07SokpeccDYVz3LmnBBEtQLfI6OLYiikdr7JYLTCGl27WFVVb6nnrpH54GWOs859KioxZwkoqprt8jsw34yYN3p+bHn87P1UPnxJYjm9F1SpNmz5x0ApjVGn/TC/nkajytnvkao3QZQ+56srVqxYsWLFihUrfkDw2oioQqEKYkmlkNJEKRCj9E1m5II7l0LOkUfXWx4/PQgNLLkSCbWQK6VgjImf+Vf/Ht91/Pb/4itYJarmm2++yeFw4G5/R4qRmDIxJIxcR3MYJr72ncdi60TzYHfFf/lTP8acpLts8/kF9lLfIu9Gvi4Xn6nhNqZ2MHZNy6NHYl9sGkfbeZnFdG5R+DKFnKRDVObsxLpqnVTZOOeW2cSF7JYiJFar2Qd6Up64p26pU/DATLIkHEfRdhu22ytc1xJCuEgNbpoGYwzDOPLg4UP6/aHyDCXktCYB7W+f0zaebdey7RyqZJq2IUwT4xSIKZ7ScZ1YgL11VUHOpBQ5jgNGC3F1zpFixjpLzolQIJCIBXxVJHXKGCXENpfC4dAzhYgxQkK9FyLqnMU2Xjo3X4J57nS2zEKqx1cU0tneDZ8cP/EyofJkI9ZLGNJp+RMJjKlQUlxeQ5PPzqtT3+u5hXy+qaDV+bl3TiZPWqo8t8BcxyK3SpZzXWuN02ZR242RyhtxGZiLzzM5NtrW5F0tPaLqRGyFoNbtv/j9qdun8gs3cVasWLFixYoVKz4V64zo9z1eGxHdtNcUMjlHtDmldRqjKNqQa7+g1gbQPLze8sbNhqf7XhJWK3JZIm1QKIax8P/8Fz8HOfFf/tRvWpTD3W7HNAVSzORYyCmQExilsNaJmkMhFsVX//tf4se+/DZvPNqhtSg+L0r25yrpaSbwnAjkLOm7M2E01rDbetAG6zTWGYoqaGtIFHHTljmRVaiOcw1N03B9fc2msVLXsrCgeb+ZlxCb868vA2vOl8slE3PBWE/bdWhrKDkTxgmjNG21t86zhK7xPHrzDe6e39K0DX7y5FIYQ8RZy27T4pqW1nqudh0lZ7wzlGwIURNjXEhZoSxK7qZriSHIPKFWHA77xbYrwVaR3bbFOsM4BsbjwLEfaZqG3XZLSRllZvvxPO8oJE4bS0qR1jR47xdyfb4f4DLpVvbXbNWd7csn4m/Onn8eHFR/crHeF5Y7UxjvW2wvA40KinimXp7CkgDKrHbX/xjFvWN9RkrLvA313FRzVu/p73iptSpUS3nOmXGcKOr0vnPOy3rPf++WKVYt4UZzTc5MZpWWqhuxNeta6XJWcbNixYoVK1asWPFZsBLR73u8NiJqrEVrkTJzTigc1T8qdSJL+qdC6xbDkTduOkKJfPS8p8xDaSDrKIVCpqjCfoJ//t98lZwV/6Pf8ZsXQvPgwQNyLsQponQmxYS2Bq3m1NgCJnPXF/7bf/Uf+L2/+6dkttG6Wf9cLJb1U7UuzkFB5QUiWnIihIlhGJimQCGDioQAxmpQhVErnHO03QalpNalFCHp3juaxuGdrWrUaY5PCNIpJVXNnkn5roqjJyJ6SbJOn5VWtJsNxkm/Zx4nscRa+X6cJsIU2G63vPnmWygUb3/xC3z00YeELJUkY9jjnZOgH2txWtM4h9aQUoaq5nVdd7b9hRADaoDdtsPVmVzdeJrmER999BF93+N9Q8oSTGWtoWs7tK4zvyWTC1hjmWKsYT4S/p4LjONIzhFjDWW/RzuL914U5DOVEE5E9EQSOduXEk40b/vpXsAp+GdWoOcZzPP9fkE8z/7Y3SeiWp0SgVEFpRLnR/VCoa//+dhZ0YUglpMle7HjcjoH1Ow+rn/RL25ayH5abh6UfHGOS2WMrC3n+V+EUxCTTOEWSS2OmViCfJ+LqP/l/uutWLFixYoVK1as+EHA62uSVxOlaEpWlGIo5aSUpRTJRYiE1g6lJ1KRgJutczzNA2JezBS0KDaqoFRZOjVvjwM/8//+t/zoD7/DF958IPOnGjabljBFUtakLCRFLqAVpYbHpJL4hW8/5tF/+Dr/1W/5cZxxgEbX+dD54l3lXGfd5u7PWodxpl6VImR0ntc0dY5TKbG0Kl3QzlOKYhgCOYtFeQoT203H1e4h17sNXTuroecX/edqHMvr5XwKIJqXeRlKVZ23u2ucdcQY0UYzxUnUUiKmOPqD1GvEEGm3O7xrsMaw3W4Zp4nnt3s23RbftKQUMcqilRYy6zWFhDUGqxSd9zitGYcerTJKaaYw8PxZwHknvaXK0FjLo5tHHI9HSs5Y76s9NuMMWKPxzovCmkRZXYislhsZJCE8Mcr7t0ZTYiHECWeamjws580SpKPPSegZma+1PbOCd0HolOKcXaoy36S4JLpLIBBq4V7zubA8V53mKuW2jKvbca5dnv0aLRbYZaPr+fmSZan27bp9y3lRf2dedn58HJbZ2TOSW15Yy6UFuaTTc4sMiNbv1x7RFStWrFixYsVnxKqIft/jtRHRYegxuvY5VoIhF/qGXISIplKIabZH5jpTVq2sUxTb3zxzhxgZzy9p98eJf/GzX+V//nt/J21nSRm6riPFTN8PkqQbJ6YUSaVAPM19FjT/6t/9Erkofutv+jK7XSszdBS0mpUhI6oOMrMq19NqIYlzQm+MktY7Wx1TEtJtrcVYTYgjakpCmpI8Z7vd0rYt3rv6Yc/U15P186R45uWxc7vj/U5J+ayrxVL2h9TXJNBGyOjZ82MMOOcopXA8HBhDIKfI0B/58IP3efz0mST/Wl9TiYXc+jrz6r1DqQaFxVXFVEKWopBwrVHKcfv8SBgSISW886SY8N7jvSekQAwTKU7EYMgp0TYF5+ZZ2Mv3aYzBGksp6UIVjjExjQHbWIyrXavnY8CLnXleU7WvVqJ/0j5fYqnVJ7Kp1Xm+LZfP4VzxPK1zOTb6bLaTWY+cH7/8i3tp4718HWGbH/9XVc2S/vwuP8Eee19NP7f/nr/ErH/ef+7ybsxpslbU4zkBe/3rv2LFiu8R8qf/vfnG//TqlVb14JPCAirMK6ax6U9MHvhssGc90N8rvOzfvI9b8tPQqFe7NP3LX/p/vNJyP/0j/8tPX+g//qdXWtfq4Fmx4vPFayOiKSW0Motyp3VNis0FMBQyMUdCmigkvHc4F1AKppgYQwIyzmiMmcmYqsE7ZSFF//4Xv87bD3f87v/hfyEqGYVu09JtPMd+IuYa/MJ9BSgxJsV/+9X/xDe/84QfffeGR4+uab1jt+242jkMI7kgyi5qIaLnKCUxX2inlBZ75xx+048TxnlySQzjKCSuaXjQNLhGCKi1J/vtTCAB5kCi+h2glsobefxFgiI/02gtdSjzvGTKmZwKypxsswBaWbquY7/fc3t7Sz/0HA8HvvmNr6MVdJ2jKY5xiqQEIURySlxtd8xdsF0n6cjOOQ6HA6UUNtst/XFfzwFN07aM40RMCZiw6lRPY63FGkOOE23rsdWOLD2o+oIEppSWjllrT4nH8/JPPnqCsW/StF11oZ4xsuWYlZcSs0+e65SvpZLlnDy+eCyUKhfHTZ8dR6XPbjJw/x/3U43RfbX1he1UL5LWT8LHbSt8sjJ6wXWrV33up6Um6Z5suroS1Zl+vz5DxooVK1asWLHi+x3qJRkuv4J1rfie47US0VICxgJocsw1NTcRs/RHlhoUE2OuKa4T3lseXG0YpztiVkypoJJ4/rQ+U8eUqh2N8NX/+DV+3Ze/yLvvfoGYxSZ6c7MjxOeMkyTowsX4naAUEoWvf/CEb3zwRGYIKTir+Z2/7cf5Lb/ubVSEcUrye1CqZXZRtBTkQo6JFCM5J9KskqYEBRIalSb6YSSVTLdpuLm+ofEt202Hn9Nic5TEVdTJdnsmawkZ0i+QhvvkaSaiIARPK0Mukg6rjWF2XObaubrdtovClpKopd473nnnHbrG8/7736EfRqyCkCVYyDmLMYUwjRjt8c7XWdct0zTy9Olz2rbh6voh/dBTlMJYiytyXihtTnOcCrx3aAUpyDEtSpKTQwo0vq0zvqJ4jmFiDJFxiqia/qooaK1om5Y4jjx/+ozGN7TbFmrSLsz3ac/JG6fU1zO+P9ugUXPa7D1yeq50ns4EVK0q0XOxanlR1VRnCu0yT6pOuuFMcFVdUNTlF4niQgTPnnv+WDk/2Wd799n6l4eWRc72y8VKzxTP+mmpZCnyg3luVuzzeqGiqsyz2esf/xUrVqxYsWLFih80vDYiaq1lmAJpCoBGlVod4RxGKelqTAWtDdZquk7mI+1xwGiPKpnHdwPDlKrTRtUZtHwp06TEh0/v+Ll//5946wtvYXWBrOialqbZcxw1zs9BSadPlWdwSsc9EZYUEh98tMf8hndptw7jCyWXWj+SyHO0UcoUEkUpQhwZp5HjcRLVUElVB9VCY4xhu9nQdg0KxdVuS9d4KJmckiSNItuk54v7OrMqanDdyntKWc75Yq5RKYXSBqUMxlpRClPGGItrGoZxwqAYxgFTaztiSqSc2F3tsNYyDgN919B1Dc55nj59yjAMPH7+HO9k7rJpNM3uhhgiXduRUiLlwO5qy+EwcLfvwRS22w3WGlLKTFMBrbk7HhmnkQfX10Amhom28bi2YewHioZMhpQpE3TNFlXAKM0QE4dhIueCrVO9RhVyipQc6VrPYX/gmf6IN/QjtGlEzZadtuw3rTWqkidUna3Ul1T1Qqs8J5X37qpdWGxRoPRZ0NCskC5rOm2DAqVO558+X2ZRgU8zpReveZ+bnpNbdZmiXGqX6TzbevE09TLr7mz9Pa1Y1QX1OYm9t7LLmyQKypqYu2LFihUrVqz47qA+eQrpM69rxfcer42I7jZXeB859CMxpuXiOOeM9021K2ZC7RU1RlQyrTSNT1j7gKvdwEfPjtweBkLM5CJKzeX1biFmxX/4/32Hn/iRb/GbvvIupSS0Vjy4fsDd/jmb1qOUqK8fN652f+byydM7UrLgHc6yqIXaGCFJGYrOZKWJUyAlSc+NMVZCO1+EK7bbLcYYUfT6wNtvtDx6uKPxVvpFq8pbuFfVco9UvMxS+UIqq5YhxFNyrRBZ5xwKMFox9APGiPI6jJL2K/OqHt86tAFUpGkdpcAYJjKFbmqlbmUK3D0/oK4VjffYmr4bQmaaRrquJcTAsT8yqCNf/OIX6HxDCI/Z9z3DONH3YlXu2obGGiGWWpOTKOfGGow2y3v3zkG18c5WWZl9zaScq5oMm80GrTXTNHF3d4f20vNaFKDSQtpLzqRqo9ZaL0r3fQtrQXo453NXa/2xM5en43Yil5efL9XQc2X1Zcf3vk13/nomhRdE+exl7p8T8/aK0Ht5zpSz17l83ZfP+Zx31CrFi08+36a8bNmKFStWrFixYsWKHzC8NiKaE1jt2LRG1M8QiDEyDANhCmw2G6yx5BzrxXAhJSFH1sFOWby/4mrbcncYeHJ74O4wMWVRGud5TVWJyu1x4N/9f/4jv/5Hv4SxkLNUflxfX9E2T8X6Wc4ui+9dH18meyru7noOh8huu0OrVNNqs1yeK5mF08qgiqQCj2MkRdDKkEikBM57nPdMk9S7OOf5Tb/x1/GjP/olusZLAnANIFIK1FmIkJCg8+27RyDukdPz78s93csYgzGWVKAfBpQqWKOJKTKN4HyzENGsYHtlSCnSH44oY0Qp1BqjHW2j2W08KWRSjBxj5Pb2FqU02lgOhyOg2G03NN6TUuDu9pa27Xj77bfov/FN2UZjGGMkHjODll5Lb52k73qDqvZioy0hBLQxKCtJutfXV9ze7hmGQQKtNOiz2VfnJCE4TBNxmmS/aE1SZ2E81Ya9HHGtoM4+nquKM6FfzussoVsXZL8myy5ptiWfqa/ysRC3s+N5Og3Ly8km90jo+WcFqkqSRb2MTN4ntZdzrS8QaXW+7PnXl+/fnH9fyfR9LFUwwnzX7IcVK1asWLFixWfHJwhI39W6VnzP8dqIaNu15FIwKTEFjbWuqkpJLsoRVapxBlcsISpSCKRa3GI0OFPwW8/VpuHNh1eMU+Tp3ZHnd3v6PhCzIiS50M0UhhClKqaeuRpF4xy7rsUZRZzDhuaBz489KwtjTHzzwye89fYDtKqdqPfSfVUuTHkijhMqa0rRxBRIMaOMwVrD/rAnxcBue8WP/9iP8YW3H6Fq2JKuM58zUTQ1bElsmQWFpkjpaCUEMkMqkLnX85AjY0RBnLlCzqIuWt9QKMQY0Bqs9VI5EwspjnTdBus8CepxAtds+ODDJ4xT5MHDNyhoHj+5pW0N11cN05joDz05Zx4//hBQOCfEOwZRhbvNhmfPnxL7gCqattW8+fAROX1ESLmm3haxJRsDRte5TwvU+dHG40xbo3A0Visa16I5ECkSRJUhxIR3hSlIRY1xhnGcGPoJh8yoGg2qSL9l0XUWt0g1jEZTihBAY4wof7VXU+mTJbcaollqfeYjoqq1u4jldialdep1mfUsaiapyGMLWauTldWGrSrbvB+sVBc/W4aLhFzZlEslViHW8oWE1nNcLXPI5d7ylxFKy9rOZ1rP4n9fSNylkJYQLyX24hUrVqxYsWLFihU/UHh9YUVZOh5LkQt7VU6JsDknCa1RQgZUUeQsalcpBYOpF9tK6l2UpvGK1kVuOkN8oyOEwhgVd2Pg2A8cjj3eyWvPKZ6Jgvctxhrm/JXZ0/hpKk0G3v/oMSX9ENo2FGUwppI7siiopRDCRIqxprnKZ20M2hiO/ZGcM7vdhi+8/SbOqto5WrejEhStdSU9ZX4DzJua76l0J6vuTKYFs8WU+ogQ/oyxFpRiCmIf9s5jjCPFiQTkJP2txmiGMGGskRsI1vHw4RuUWNjv97RtJ+uuM6hXVxvGYaqBR1tCkMTjFAPGOMZhIgTNzc0D9s+fS3dsjmw3LZu2oZ8mwFBK4ng4olDstjtsJWIpZYzRNXG3W+pxlAKnNdZoxikRchKiTkt0GauLpP22LX1f6I8TRVtULGhGoKA1SCvQvG913YcSfmSMWcLxVSVdWpvFNq0Uy7YBtTtWL2FaOp0ImlFS56KUEtV2ZpBKUfTleajU3A9byPmeMjkPnBaWCpVLh/rLLLInlXwhrecPqSICrpqXo/b+nlju3EBaSllunMy/IUq/KMUu52dZJnNXrFixYsWKFStW/ADitRFRo53UClZ1KJVCjJFxHJeL75yln1Mpuci31jJNEwaNNlLVUSiknETRAVJOhLFHKYNXijd3BvfoEcdhxBpLiCONs5QswSpGO4a+p2s1rpHQIqUMGUWSK2zgdD1dSqHkRMmK57cHppixRiyaJ3WqEgkFpfZqxpgWW2gpEJPMI/rGc/Pgiq6zPHiwo2mcEA4DxupltvFc0pLr+LzYPecE1UsUztUyUVVFEY0pMFMIb+T9z7Zf7z0plaqWikLd9wdc26IUDINUzGitub65JgwjAM452QdZMfQRt2u4ub7m6dOnHI9HdrtdfX1FCELKn93d8uDhg/rzTAgTzlmurnaUw4H9fo+1Dm0s/TCKQrjZ4KyrM6yijs7vSxJ3FVYrrq+uKCXzfL+nlII1hrZtuN50OGdkd+XCEAKkgm88WhckOKgOWJbzudD7ltVql1YzMczLjZT5RsIpvEhwsvHmZV3WSPTUTCRnUig3IExNoJXXmGte1Nly8/ZpNYdZqWWb5vNmJsvzTY371t75Y+mPLWIvNuakyM7nvrpQ3S8DiOZ52ZlsntvK5/N22WeqLPt35aMrVqxYsWLFis+KNazo+x+vsciv2vKqFCkq3ZwAe1L5ZjI6z5AabchZkaIobG3rGadIjBMxSncoWmG1pukailLc7Q8c7/agNU+ffsSXNu+AVjhlOPYToR/4kXfekGTVIjUguZwcujFGUpFaCiEcQDE4Evu7OxrnsdJDIypQHcpLKRNTJJ+9nxATKAkHijlhrcV7y9XVBue0WGNd7Vet6tYcXDPvNUo+qUozgajq00LI5kFBWJYpdR+n+nylFMMwoLTD1z7RUu2m86vlkpjCRIwB422t3amK4rHHOcujR4/Y7++wRtM0LUYrxnHk5upaCOfzZxwOB6x1NE2L1oqu6wg58cGHH3C12UKhEuAeY6UixlrHNAW8l+eEEDj2PbrAo0cPaRqPsw5RMeeQobpPnXSPGmPEvsxJCfdeAo0ePrzm7nBgjCOtaqTOZuZ9SqEwF2STRRmsdEydkowviarUxVxaXdWZpfrMNluNrnO1i1iq6/upN0VmnCyuVYHUZ9ZrleUPsjpPVD4NPZ/Pct6fN533zTKXWhdVWrZNqxOpFhv6KTBreR+qJu9yljwsXufldS4swTUpeKmCWbFixYoVK1asWPEDhdcXVlRk1rPkshDInBLkLFUoJWOdRiu4vX2Gdw2Nt8QkRDAlJfOWKUFJOKtom4bWeqa4WSpUQko0zqA2LalkHj/+iC996Utobcgq8bVv/zLTdORBB0b7uh3mzJpp0GpDyZkQpcZEGUNJhWma+OjDx7z95huAWInFAikX4MoqrLcYZ1FGL/ZLbTQhim31avuQm+0Nnd9ClpRZo7SIZvqSJCROBHEhNkqRFVJUUhIpZUQNnZUrBUqTSkahSHOoUrWKlgyKREkJtK12UJkjTWSsdzVRV5FCwRtNipHD7XOmcaCUgDGWzbZDaUUMAb9pMU6zH45ob/FdK6SyDxyPA0ZJJczVbkc/jDw/9CgMXaspBEzKGODmesuTZ7cYBV3bkJ2EHfVa048DV9cb2s6hisI6zXEMYtudJlDQ+YayK4TaExtj5jhMlFLY7VqMUTSNI/SB492eB288xBgtBFObhSAtQT5njElpIVK6/vx+yI8Q0csgn9N6qERXcV6/ImeOu6d6noVOXdS83BfBVa1OmTf6/lznafs/LpDoIiDpY7zpORdyji/8/Ewolrlc5CZKWeqF5lnTsrx3vfz5WW9Drlix4nuDUnvHPwn+9tXW1dz7G/8y6I9JGL8Pd3bT8ZPX9/nduTOvsP2vinQR6PjxeJXtf9V98WpLwf/3f/3Gpy7z6/7Uf3rFta34zwqlij+f17pWfM/x2ojofn9c5u1O830ny6Goj4VSEs5ZrDP1hJPwIa11FY8yVitJVLUWhUbnetmtRAXcbj1WJ3JRjGPPfn/H9fUN3/jG1/n2t76F0WKjtdZWK6wml0KKURRWqEpZBiWEriAX3x9++CG3777D9fXVyS7JaW7QeYfWipwTOSdAMQWxtz548JCHDx/gvUcpaBq/2DedcxcWT/mqXFyzlyIBRMBipT3HSZE7bZeEH2mMsZSsMPZUgZJyxiiN0QZIGCMK8DhNtCnhvcPowr7v0VozjgOUwu3tLcZ52rYlxYjSBmMcpWQJ1DUjoHHOc/v8Od45hmEkHg8Yawh9zzT2HPZHrq82bDqLNQrvGx7stvTDiNGaTdfRth3TODKMA/u7O6xWONtitZMQIWuwRhTPxjeoo+FwOBJj5G6/Z5os0+RIObLbdRhjCVPi6dPHuKbl4aPrRUE+K+6squWZJVUlIY5nlth5n59/wIvET3O+bLlQBFUllItNltPvxRwc9PLE3BeVxfPHZ7XyVxcn0lyq7bZUC7OcYqcbK+dbc/+8XbFixYoVK1asWPFrH6/PmlsUMSRiECXRN+70mNKEGMhlDi0qxDhBUaQE2sicpdguT9ffKSUgLVbfUhJjv+dweA4Jmu6KKYx8571vMfRHvvX1b+CMpVRpaw5dUUrm8TAWiJVEZpTWGCVBORIWI3OqX/v6N/hNv/E34tysYOlFAYoxMk4jpSQgE5Os5+bmhjfffANrNJtNi3MGY2u4TSWXF8Tn3sX6rLi9tIZDqSXhVcKDxBI6J/BqbYTwGFsJj0z3zfOopc7r5iKqrzZOlFprOOxvl9fx3tMfj/R9z/HJM5rOczxEnj5/hlaat958k/3+jjAlcpowc5hNQWy244Cq24KW+dPwZCJcb2i8pYmZxjvapkFrLWFKXmZRdVUR5xsYWmnatl3Op0XdKydlMOdEP4zknLDO4CeHbxqsccSYee+9j3j48CHGitW5cE7mqWJe/V4bVE1ePj9WC4k9+9kL358/R13aePVipT0no1wsvxDTcr7eS+vw/Hm2YH/cXej758+vHGczox+z6lKEGM8BXJ//NqxYsWLFihUrfs3jPNj/81jXiu85XhsRvdruxLpXCiEIUbPWUYC0BJ3AbDnMpdTah4zCoozCakdOlTzVsCJKQlPYHw6EEPBe03hH47YY6+nHnmdPH9MfDsQ4kUvGeU9RcBxEufO+IabANA6kLETUmgZrHSlMlFyDlFLCGMOHH37IG4/e4N1336l9k6rO8c3bVdBK4axFW2g3W7qurVUp0smpjRel0tYgHTWrnJepuAK12CfPo2LgjHwslSIzgTknRDMBNXXOMVOUpiAKdSGjojBzpQ1N08pxmgI5F6wVu27btlDgKhXG6SOudjvImWEY+ejJY5yTIKRhGHHW8uT2FmOM3DCoRDpnId6dtcSY0UrRj4FDP9I2nqtth3cyB5pSxDlL1zaYOmeqtSHljC1ZlFwjNwrm1GXvPIdyRGuF945pmpimUO26Cmsc1liMtdw+3/P++x/yQz/8zkLuLoioOieiVWleSKUscF8RvU8kRUVdZPOLgCEFS7JumSV3db7uy1nL++uX2pgzC3F1Fszrnp/zMnzSTY9Xwen5l2fkeY3MkuZbavpvLuvf/RUrVqxYsWLFih9QvDYiqpWQpVIyWhlAkXImV0JpjCGH+pgqaDJZg8mFXCIpZGII5JhwtianAqUowKCVxeiCMY7NtsFoQwwBA8RxII4DIUyyMSWhtCXHRCqJnGXOzRhD0zSAWgjUOGamcSDnjHMOyIRp4utf/2XeeOOhkLOzC+5cwLkGZyZiHNlsNvimQWshgzknmsax2WywTkhgqZbMORl3JpezCisVHkqso7NUh1h1c5agG2E4dR5Ps8wrikJq0NW+qrXGaEcusk5tDDFPKKPR2tG2ju32Cuc8KSZMVVG9bwBoui1tzFjrIBca33B9dcWTJ085HI5M48jdfk/bNEwxoFJiVIppDOx2V7XWRSzCbFtiTCitoUgPZ4gZ76hhSkCJkBRaO5QyoAwpJ8ZpWpJ7ZeZY7N7WGLabDeM41vPOoygYpZaKneurK0JKvP/Bc77znQ946wtv0nb+YqZSLfu5XBC7UslhWfatvlAgX04ay7K+F0hr/TgzY8v3SsKTRLWeSek9q25d+4USqk8q96sqjy8LM3rV5c8x23OX788tyLML4byWaMWKFStWrFix4rNgvYT4vsZrI6IxHSHN3aFCggpCkrwVa6pWimkcq3XToBWYxjKGRAwRU8N6lFJYWxNdlVh0XePxbUPJmZQiwzSgqjV1tnPutjfkksX2qxSbTUeq5FFSQPUyIyohQLDdbvHeE2NcKlu0huNxz7e+9Q1+4id+opJhmTlt25a0iRz3A622tJstKYP3Huc8u+2W7XZHzpkYC9bZ5cLcGIub31cpp4Ch88qMWXKqybCSoKtP5R1nJGeeyS2IijgTt5TlpoA2VmZGY0JrIfbOOprGS9puylgns7ic2V+99wAc+h7qdja+wWqD22w5Ho70x17IfM5yHOtxcE5USlsMm7ZjCqFuo/R9qjJ/BmsM1nk0+sJ+K+QzLjcOnLMMw0Df94yj/Hy32y3BVhpoG4fRmpTTQvBRmWmKfPjhU778I19ins+U/Xh5/r6MYC77Wal7JPYeKb1HQO8fp9Oyl2FFaiHCnNl5T9unzkIxTlUxl+fB/e2/OI/uPXa+f+fvT9bvT//Lf06I4URE75Pi1Zq7YsWKFStWrPisWOtbvv/x2ojoFAapK0QtFlylDUoXUpb00+PhwDRNlDgyhUDTNuyurmm8E1WrzoieAoUkHVZWq0iliP1TQdEZlUErs1xMj9NEKYlpOlIUaNviqw01p0iIqaqesyJ26kecL86F3DlKUXzzm9/i0aM3efTo0XKh7nyDsROuafHGUpSmsVZ6VI0TpTGJ0misXbbNOSFK54mscFKQRE0+zUGqs21ayClwnwSVUkg5Yqxfql60MVjvsc5X8hCJIYid2EuKa04ye+u8x1hLzokwTRfbZaxh6Hsoik3TioqJpBROw8ju+gZjqJZpv9xA0FoTpwljDM6Iwlsd0IEAAQAASURBVOlbj0Fszc5orDFy3BU458UmnaSPVClFjJFpmoBC0zS0bUMhM8VEPwyV+FucNuQUUSWLBTZLMrK1lu22Y3qWee87H/LOl97GufMZzcv5y3qKvZRonh5/WWDRmV32vm33BVJ6SeYkyGgRTasSW15QQs/X+bKvv5vHP+79fBpeILysJHTFihUrVqxYsWLFaySiZUn5lKvqohTWeVIqQCLFIKmwWpGNoXOetmtlZhJwRkO1scqsKZALcZrohxHnxVKrVcFog1YtpEgKgZQyIUR806Gtweu2Wli9EMBcKBayK3XWMy9kV6pPzKIQpiTdlsZYnHN845vf4cH1FdqI3XieG2zahpQzIWWePnvK9dUV211X94NYTHOGxrmFpJWiSFkqWeaE3IVo5PlTXtTOhYiUgjLmjNDIxX6ebZDGkEV/xjmPazxaG3zTUHLicBcAzWa7A1S1z2qstRijUCpLhUclvFMIKK3Zbq+w2jGNkyTupsw4jnjfUIoihIgxsp4Y4xKK5L1Hacux79nuNhg0VmlRxkumcZ6m8TSNwTpDjHmxVedsaNsGpQp934vyqjXOW7pWlNwwTRilMEpszVZ7jNYSq18KqExOU+2RjRwOgfe+/SFf/rEvsYQDvRD3cwpJmtNhZ/J/yoSdLdScBR+puryc++olxOyciOpKNRVKkpup87+1HOX8ecCimL9Msb1PUj+NBL7seec//6yoBmZZ53e1hhUrVqxYsWLFioo1rOj7Hq+vRzTPR3xW9RQlBUqCGAIoRde1GGNQwDAFpnHEaM7UvpOpN6fCNI6S9poy/XEQgptDVdEMzmpSAbSh23anzkNlq93RUHKZk2KQuUqZzZzVwzlEST4KWgsR0tpiredwOPD06VPeePNNCR/SGu8dB1W4vX3GMEWurx/StFJ34psW6ySRtusavDULoUwpy/vJEVvtyucqaKphSdq8OAd4bqOU72WHaWPQ1qKUVKwYY8k1VSbGUCtrMpvtRt5rrutUM/GRn+UkdTQhBMZxRCkl78c5+mPP82eOoT8ChbbzeO8IuZCqymydYRh6UoxMYSBFTS5wPA5sNx3OWprGQ04UCiEEuo2nbRtAc9j3jGNAqUiM+myetzBNI85pKBnvDF3bSoVLGOm6lk3XYbQRUq01MYsCbHQNiqLwrW9+m3fe/QLdxi9229MfqQLVuq30i/v7vjX3fprtaT0navYylXChs7PyCswVLuWF53D5mrxIGD+OlL4Mr6pafteK5n17+YoVK1asWLFixYofKLw+RVTpk5U0JyhCNsIUyQWatl2UM7Smr7OiqhRRs86qLUpJROJi02zbDus8IURStKLeAUpbrBMVUEhnJtaaGAn4OQX6lCKzpnOYzjyfOU3TomzNry/2UgMUQkj88te/yWZ3xWbTYXRhGHr2+1us1Tzc3KCNYbfbstttaJoG7z3eW5Q+1ZHknEnx1K96Huoyk9CXEY1lNvBMIS25oLUV67MyohAbK12ilQPM+3qaJtquOxFvXZb1WGfrTG+dGyyRcRyXahVjNA9urnmcErvdBqNF8WwQS2/KhWmKhClxfX1Ft3mHUgqHw4H+OHB7t6fkQj+MQKZpG5quJYdM3/fIW8u0bUfXtYQQiTEwjoW2bbm+vkYpyCmRUsAYOWaL/TcKadZKemeVdzgjvaOlFMZxJMRATJnDIfHtb7/Pr/8NPy5dqOrFpGI9Ezt1IpOLpVYOiBwXTseNe6RLEnTP5kBfckxf+N152YzlBVH+ZMVzPo8+6fH7qur9mxzfDe5v83l38IoVK1asWLFixWfCqoh+3+P1peaaqnYqRcoRSoKiyMpSpom+70mpkkvtMM5LyI1iUUnnepN5drNpG9CGlMXO6JsG3TTMCbSFjDGlpvRqtCnVyii1F/n8AjwXDGqpaZHXOqXhnoe2qIUUy8+Ox4FvfvNb/MSPfZkUA8Yodrsd4zigtOLm5oqbm2u8dzgrgUQCITEzCZwThOdKjxnSl3ofou7N86sXHaNKodRsFdYUlKxbyZwtWjMOA6HaZJumkf2ekqjTKIzVaOsoYWIYR7TSTCFcEOe2a2nbhq5refDgBl/rW3IpbLYbrHccjyM5gVKaq90Vj954RM6F/eG5KKnP7/jow8ccDkeePrtj07Y0dU53miLjGDDGYo1UyPR9Twhyc8AYg3cW54wkCytw3tB1mr4f6Ps90zSIutq0GLOl0R6Uputa+jHRTA3hcCQr+MbXv81PfOXHsNacTKVaI6K8ZkkiBpZQIX1KvD0pm/L93KFaKplfjtw9CzX3LKz3adrHkreL13vpKbJYiOfZY6V0TWH+eMwEfPl+ScLiBWL9UrK6qLWXKqjS+lRls2LFihW/ErzqDbJX+JMzXb/aqhzm05dRn77M5w2j9Kcv9JpeM5X8qct8XO/1d4v/wx/4v37qMn9H/bpXW1l52fXXihUrvlu8NiLqmy1aQy6JHEEpqWAxOdOfEbGcC0YVWmsxRhNiIKRJAoJUltTbnMkURLpC5ieLdEkmqnKlFUafEmiVyuRqwZ2VUK1LrUnJUDKqJFpvyEUTE0zTVCtX5GLeWAnyKfnsD5OWWcjvfOc9Hj58wIObazbbjhgjKRVc07C72uK9xXmxC2uFqJRocsmkmCBLME2plmFBfkGpglM4UaGSVmPQyPxiQdcQKF3fL5SsUEZI6xzwM4UB32zoul0lllBiwWiHtqIYKmMpKVEQG602FmsMYZq4ub4mJUmovbq+QivFZrehPxyZpsjm6grrPNud2K7HSVTqMWeudjs211dM00jTPcH7hscfPebx42fsj3ds2oZNa9HVFk1RpBRlXlhbwhTo+14U7sbRdS1CzBXGKNrW8eDBjlxkntc5jzG2dsQGrHWYuu8pMv+qNfSHPc+ePOedd79APcMqGVUyX1qhZlVTFanKKbOteyaG+eJ45bq4rsf0XEk9qaPnvy1q+fSp6uHZw2V+zvmDItWilEFrU1OW1eXT9bltXkHR52ucB15fUglziq+bb04sN3DKPUotd1zIlz9dsWLFihUrVqx4Jaypud//eG1EtO8PpBxrWEuBHKvdU1JWRfWULlDnNcbIvKYxmpItFFHISknkJFUnWXo4UDpTUq1qqZf7BYgpXVz0zjOWoqhW1bSqokJISyU8pQYZNeScpOtyscEWClKXMo5jvfCWdf/SL/0Sv+W3/A9wztJ2Hc51NF0rdtzGL3N/xsw2WrEKzyTlPGF0tguf2yovrI61zmNWRdG69oW6EynKGeP8UnWTqtKbcsa6jpsHj9DGkWMip7zYf09prEoIc7Upa62xlSSrehOgaVu0c6AND5zh9ulzDscB37Y439Z5R411lmOtdMk19ddqy/XVFf3hQOMdb775iPc/eEw/DDi3wWGYQqQrBQ2V3GdRvhVVHZf1G2OXVN35fTy4uaHrWnJMyN0KOfZKKZy1eOckcTdGpiDn47e+/S2++KW3qqp8vt/VxbkkCmk+m+c8I6pnNu5CteOWAilfSJ7zDf373O5EQF/8K3l+DsyxRotgqS6/ON92Y4SIzsfjkvee9qNs/Vka89nrvkBEFSwpWmevl1JatunjUn1XrFixYsWKFStW/GDhtRFRowslZwmMAVHuKhkzxuCc5XgcZOYvR0ACaYwyWDQpZaZpFLJVLYZKWTIZ5ywxJg6HHlF0ymJVna2vIDUiMxmt1+cLlNKS2FvEHpuY1bKEImOUOV3yG0OsxG627c7rf/z4MV98+23apoVGS01Kncd01uK8rduUzi7SCyULuTzNwp5sw/PHPK+pzuYDJdVXo7RFW5mFVdoIiVWiIKZ8ItsAzrc8ePgm1nkKmZQncslLKmtKCWMtWitCFoKqtca5qggrsN6x2W4x1hLsBFmE5m6b8O0W17akVNjuduT6fO8bxnHEGMN47Lm7uyWOgRQizmhSTHz5h36Ib7/3Hsd+xHsJWrLWoZD04jANFFXomkb2XFHEmNEarHUXfaopRbw1FK3IKZOTzMRKpU1GaU2KkRACORViKTx+/JhpCtJJe55Se3Yun1ugeeHnNfP2JGouyrSo2CdV+/zp5SWs9FVo28d1dJ7POat6fqiZEJ8tJ+9R1xAwfXrR8iJxfOH7+b/qcpm5M/aF/tsVK1asWLFixYrvFqVaBz+vda34nuO1EVGrxR46B/TEIPq6MVIjorXMIB72e/KYubvbM5Mx6yTgp+97xrHHaMtmu8U7j67qU0iJFCZSjMQQySVzdXWDsnZJoLU1oVZQxBY7z1/WAei58zHGQVJ5UyTFhEJLB6XWONcQk/RqzsExWiumaeTJkyc8fPCAq901RntSzmgjgUpt2woJz6laciPM6pQ6BQ+dSGNNZdXzjOdpdrTU/8nMn0FZUwmHEFEKQoa1puRIyhljLG3b0m6v8c2WlGUbZvtyCAHn3BKoM/Q9sZJ3a60Qt5xJpdB2nVhcjUE5SE1HzBHfNIxjYNNtMc4tZC/FyPXNNf3xyPFwwBhDipl+6LFG1R5QxaE/4r3nsN/z7LkEPnlj6pxtQ4iZYRyJMYHKy/xryQXvPdZJUFFWiqg1KSe00VitSCWRcqJMME0jMUzkkghTIIRM2zTsb/fc3t7yZvfWGaE8DUnO36vKvy7EzIXkcXGDYk5jXnwgpZw98T4xrM+fX29Z+4sK6Um5ntXIU1+prjc1tDKgT6op6p6yW2+vnI+BXrDRM1L9cpSLVOD5verae3tJSFcyumLFihUrVqz4FWC9lPi+xmsjoiFIwi1ZYbQnW7G3JgphnCTZtOnYbq/IMRFCZH+4I4SBxnuUAm81rbvGGAtKkWo3pa6BMo13TFrhG78k28qFeaGQa2iRnMGiUpWTYrqE/ghJCcMgxBNw1taLfiNziqrgrcVuNxQKMRWSdKIwhYlnt3fsdjdAwRqDtQZnDCUlUr30D3Ei54QxFm3MmV33pFza5XXFzjwHNUGd/yxF1GFjzwiIKMmFUypwURrfbdludnTdFm0bUIV+f6jvRzGNk9hzTSbHQBpTJeQy25lSYpomDofDQprm0BvjLN1uQ388kNuW4zAxjiNXTQtakTnZOrebjhRGnh+HJb3WWMMbb77F87tblLWElDkcAKUZp8DxeKRtGrHRNp5+CIwhMqXE1c5hncVYVVN9Y01J1lhjUaUQkyi+VHIUcySmRIwjioQ1hmksxJDxWvPee+/z1he/UNXDwtzhOXeFyozlTCZfoKPUkWJOj8rNCgzooiHP1Tj53E+7ENAM6Hm289667+OkmAvpPCmhMwGu/akzYbxPKu/NmJ70UV6y8Mugz7Zrfub8u5WrCiy/f6Wq7qtFd8WKFStWrFix4gcPr8+aW+16IQRynsQuWi+8tZZk1xgGjtUqSVHkktl0O7S29UJWVRIRaJoWaxU5nyo7lAKP2GBDmIg1rVVrjasBPLNCM8+x5SS9lVopULOFNaKVIiuFKrP9UUvtinOEKOmxKSViSqAszjVCJtE8e/4cZxyPHjxCa8N2u1lUR7JYj0MMOGfq7J6+mFVVSi022BkvdIZSSYauITTGoq1DG+kMpUCoYUK+6dhdXWOMQxlLLonj8YDSYJRhfzwQYsDOdTV1e+Z5v/nzbG0+bRNAwVpbZz8z2gTCVm4kpJJpm41Yk++C3Iwoia7rSFHmep8/fcrdcc/d/iBhUDVoyVpHTmE5XvOxs85inWEaEykVYkx1NlRRtEJpQ4zyvlVN0pW50UjbteiqxMp6lSTyWkNhJKSCLY4nHz25nOVExMyLGd55J1ROeqlMnhPGk6I4n0cYIbc1nnbZ1/MNB2biermWxda7/OzMti0k9FRzpDRnc6ufpGqq+zz6hdf4eLyoo5az7ZpvEomlfO5QPam4K1asWLFixYoVr4zPMaxoVVZfD14bEZ3nG0HIX5giaIXzol6lmBiHAU0hpUDJilIUurNY64HMdqsZhjtCmBjGA9vNFjjVm2hjKjmKizVSK401nlKUBB3lzOFwkNeqF+3GGJq2IQax4QI1rEaSbJumQ2sHShJtS5mqkqfRVgsJ1I6cpUfz7vaWOEY27Zbr6+vldSSdF1LKGKOXOptz+6JSCl/3yXmP6HlYUSlFUnK1EUVVW5SRwCClRWHVKKz2oBRN2xJTpGSIMZGLVMwADENPmMKiHs7hOzPxO3/deV9prQkh4H1zMb/qvENrQ8pyvJXWNG1LTokbo+mPB2KIaGD34ArfdLim4Rd/4T8yDj2//I1fxlov3aMx0HhTbyoIQZ5vZhijMNZg6uzwfr+nbTybrq2R8qXOztbZ0Po+ZqqklIQstREyPUr1ZBIlC6Hd3x1IIWGbOb14VkPnYCIu3asvwbLfyr3vmZ9cj3cNbjrt48v1nIuY57Ury/znmRI+K6LzeqpuXd/ziyR2Pq/yvRlPSQG+F451dk58Ei5ulizKuaQuG85t5ytWrFixYsWKFSt+kPDaiGgpEVDknBjGkWdPny8X0FcPrlFGuispBVMsRYOzoj7GquxprWiaHW0LYRo57HvGITCFABqsc+yuruk217QtlOqRVChSzsQwEdOIsZ5uY5gGmYEM00QuhXazqeorksJbYu2nNMQUxNapJKwnl4I2RjpHS0aTayKvBwUPHjyg3bQ0bXOmClXlTKtKaCHGk8porMVbIY+5dp1CddgWIUO5VpoYK5UkyjiUthhlcMZirIWqjMUU0RSm4YhShmJkZlMbsUuO40iIAeMMShXGoUfrmmpbiYfRBlNnQ1FS4ZJzJPQDk9H4drNYOnMRlbutCrBShoJYb1MC1zislQ5T5xuUtrQx8M6Xvsh73/oGu03Lh09uKUXInuwzTVGQSOJXzWIXvtvfoYzFKkXrPZQIJdB0Gm/BOSdz6Ao5f6yozxTIKdMPI8NY7dda4Z3neBxINjP0AyFM+G7LpVlV1d5QCdm6tLqW5b+zhXdRG19ae6IAgz5PlT2zi88rLvOXSleVk8oyRf2Vx+QcLUrJcjP/Vao6BeY03BN7PlXAKcw9K66q232h/iols9IlnBHJE7k/f/4Juiq8cuNkDn9arbkrVqxYsWLFis+M81CLz2NdK77neG1EVIiXwlpN1506F6+vr/FNIyQGSdYlC0mKMVJyXpJOlVI43+CsrQQu4L2l27QUhcxKai0JtMpC7Qmdg1+scxhrmaaJlCKu8QzDQIwB1zRYaxf7aY6JmEYgy7ylsXjvq7qmcb4Sm7ruUhQpyWObruPRG29ItcmSgntp30QJOabIDKYxRuZazakM+ySgKSgKVSRd1zWNpNoamYNFKZx1p6oV40g5Yowmh8gwDTTtRh7LMKW4dD4qFNoYYpB+UXVmNXXOLzcLSkrkOs8pNSSF4/6A9Q3TNOG8J40jupLhbrMhxlyPmef586fEOGGQ70uRtGPnDSEOGK3oGk/bOJ483dcAow1KG0IqHIcB71u6ZsN2k0m58Oz2lqSqmj5OTFOky5HGWigZbWWWNpeCQZRoBYQpME6T2I3rDKzRpp5XmXEYORyO7G52lcSdoLQWG3c+v7lwVmHCkpvLQknVOfkqi9oOZxWey/PzC0TtpHqekUMl1T3nXy/bWpefLdOgLtLhCmc3OZRCfUJJ+4UqqjUaIyFbF49dbu+l4jmrq4Vc1EseX7FixYpfRZRPdnEA+NvvwXbcg37hxt3LYe79G/T9hs9z+6/0q13C/tbm25+6jP3h3/1K64pf+8YrLbdixYpXw2sjokLWZK7QGMtmt1suoFMlRfJRUDXNNue8XEzPVsSiFOPQk1Jgs2nRShNiBmXQc31HmcnBixe+UiPiCVFzHI4oY3GVZMr8qvyjlXMi5yT1MlYqMIyZVbVTwu28nXOirTWWN954g+12i3MOay3lzHo7z83BbHW1lfS5Ohc6E9fTrGGpKau5gLZGPvTJ2itSJWQlwUq5QNd2jEPPFKaaYJqJcSJn0Ge2YOnnPKmyMSY5JinRNA0pi/JbAKc1JifG2r8aQpB5TBQ6Z2KMtL7BaMNut2Oa4mI7tk6U1DgFQs4UBV3XUUpm0234cJxwVrPpPI+fFo6DhDl1rUNtNuy2Tuy5aNrGMU6Oq60k/xptIBemmGDoaXa7Gtgkqczb7UbU5JwpORNiYOgH+mEik5kLhawVq29K8PzZc7747hdOZ++5NRrpBjXGXMzMns50Lp938eC9eU0lSc0ve97513JDo1x8f35O369tMbqGdZ0Y53IOzn24ywYoON/KjyOK8jxD0fkTyeT9NF+Y99lpFnrFihUrVqxYseKzQH2OM6Kf26zpis+E10ZEx/FITJmSFTHJ5b+pM4QmlzOHX7m4aJ5qRYrMBpqqMqqFXCXyQlAFpwtfcUWeWR/remOSMCNTA4isNXX+7uS11Gi88tL3iaZoRQyBXO2+JYtFNmexoMaYyLnwhbevubq6wloht0JETvOecxKu1qpaNqtq6FxVQ+/fJT2bHzTVglnf16xoGqvIQDOvo7DMceaUUQbGscdaj7MSgqSN9LFOYWIax2WfpZwgZyHFWkNJCxEVu6xmGPp504h1VrTU45NTou02hBBqEJCERzVNQwwTylpSjDy7e87hsKf1jbzjAkYrNp3n+nrHR4+fMsVI6TMlZCDz9lsPmRhxOWMUXO923B2PHI5HyFWHNF4sxcZgvZXZYDwpRvp+kDodrWnahoyW9OIS8d5J1UvRlAKPHz85zfXyop103h9KSwouZ+fa/fnKc5xEbiGk8zxmXUG1kt9TPpcPWeyceJ6veb5ZIzdubL0hc0l8l9dVLw8NOg8aOv/Z8rXRmGJO4VsX3paPv8M/23s/7nVXrFixYsWKFStW/NrGa1REhSBZY2kahzJVLZzDiao1N5WMKjDVfksQ26quH1KJoslO7KcFsah66wlJeiVLncu7n0Qrrl9ROSVFF9rWo410UWplhGiiaHxLzplxHESppRJSZPuK1uSiMMZRUlxSbndXMh/ZVltuAdD1Lg5ir5yTUkEsy9Y6tKkdoaq8lATM4T9GSZIvKaNMQZExuqDqHGopBW8dOQT645G2aemHPYWC1ZqSRVEVQp6IIUIpWCMEUWtdK1ucWKMVZKXR1uK0Yr+/k1lBZVC67oucZeaTwjBOdBuIMeO9RmuZ/dVa4awFbRhzpvUtH33wIc9CgJTZXu148rRHK3iw7bh7fsuUIlo7tPWMU+L57YHW29odq2mahuvtBm8t++ORp8+ec7e/paSEtYYHD64x1pOzwRjo2pb9fk+IETXve+OJsWC19JDmlEk58fzZM0gIg6ri4znRnBOfUQqNRaTmmZjNHzIXedEGOqufSh69VEerEj4TzZIX0nifhJ4TVfkd0TW4qtrelxnjU4eo0tXei35pk8s5Po5Ia2XAzMFF6R6pPD9vy2nHLQ+fzaCuWLFixYoVK1Z8Fqwzot/3eG1E9PrqISllanAsRRWmacQaU8liVZ7q8pvN5uLCfyZZ0ziSUpAaD2VRusE5z35/ZJwCvmkwWtWe0NpheH6xXJbrfUoWdVQXRUoFirpQkESFtUAkVhtqrGRNGUNRuip5Yq9t25YwxaUeJqVU5zsLZrHcXtqEtZ7rW6QehHs24hmzxfM87VTL0KDsv5wo2ZBjJCvFs6dPqoKZluCfXHtRZztwqEFN3vtK+mX7jBEFN4aIMnOVSsZYwzD0S2JwypByZBgHlDF4Z6EU9rfPaNquEh+HrUFB2hhyjnRdR9O2hHHil37hF+kPe9pGbkwoDGrrePONa57d7bFa03jHpnXSmarkJsG8b0qK7LoG7yxhHIlJrMXTJF2mJsmNDqVOAUwnG7jYianKnpA5jU6KYRyWIKdznKcJn44hp+N3Znn9uEyeRQA9O99fpoDO67hIwr0gofpkxa0k9MX50o/5eg5Seun2fTJRnNOTJZ348vfrPC33tC8+LsxoxYoVK1asWLHiFbES0e97vMb6FrnszimjjSaFkZIiMVfV08kspaSy5rNZzUtrpNKKOEqXqFKghfswTSMKLUQMUVEXklZOF8SXs5rQ9z3UcBmltHRtKg2ki9eeZ0Fl3tGJzbiquDknScZNYtcNISyWYl3nSnOZZxHFlnu/jmXevnMOMHd4npOUlDM5FUKIXDViQ80pUVIArclJMcbAsd+z3WxJOZDL3JsprzUOw6I2t23LNI0cDgc5DkaRs6pJv7JdTdNIkuw0kXNEqYJzhhhF+ZqmAWU0Wm+xRjMc9+Q4EnJAXV3jVSPvWymMc6QYsdby9ttvMewP/Lt/+28YByHyFHAlcbXbEXOmazoa7TA642yh9W4hoVLnkhinCesbbh48IIRA1zgAxnHE5iRzmBq88Xjv2R+lsialSzVPK7lxYa3kyPbHnu31FlWQFON5yXvzj7PFWkIxSr2Z8eLM5QXBK9QBhU+w8aoXiejpMY3W8/z0i3Ur91/v/ozrfQJ8jpdub/353GW6hFiVU8/rC+ezOg8JUesf/RUrVqxYsWLFih9gvDYimnIkxYzWhmnqiXFCKZljjCnS0eGcq6StCLkwp15EFMQQCTlI/UgupJRBRZI27HZblLbVdliIMdSLdXkNpecLYUlcpYYZNU1DTucX3kXmQmcCWz+3bQcIMZ5COJulg1LVyhACh8OBN996k5l0W2PRnNSrmVic90AuF/7Lf7iwFS/bVoTQa21p2mYhk0krdMoYI9s9TQHnPRmx7Hrv6fuelCLTJKrhHKQ09kcOxwM5Z9q2XQJ+UBJqpI2BnDFacTj0ddsNyhj0NKGNQZVEDBOjNnjnSDGQ4kTnnPS1KkPXdRwPe7x1TONEmCas0bz99lt8+Ue+zLe+8cvcPr/F+Q1DL52h3nqePX3Gw92O3dUG3xjGKVBKIlYy65uGDBxu9/TDSE6Rxlkh7KUwjCPWGqzVKC3Hu21bQiqkKZPzKaBKcVKaS8588MGH/FDrsd4sObgniNItN+fKQkIX1JoddX5QXxj/vfz5JymRoogr5lAsrQxGi0pcMlRx/gU1UlVFXjGHIpX6/7LM5s5zqedvDU7LzOfepaCvzsjomfJ5/pbOzuVlBSsZXbFixYoVK1Z8F6gTbJ/bulZ87/FaFVGlYJoGckkYa3DOY4KlrQ7UWOdCFwvpfJFba0vCJKTVWSGsMUYyUgsTgswFFhIhTGy2bbXGFoiJnCStVitIOYuSkxXGaKypAUr5dPFujSGWRD8M5Fyq1Re892htxfRb02zLfEWvMptdh60WVas17pxMQ1WQ9AXRfJk6ev+zdIjKVxKsdLJBqkpQY0wYU1+DEzHR2uKcR2vNMPZVzUyEIIQ9TAObzZaSE75pCCkRS6ZtNzjjyDFAjoQ4opUE/YQQ0EY6S5XK5JSYhiPWXGGdoz/c0qVEmiYmbdlut3jnCeNI2zY8e3zLmDM5K3ZXN9zcvMntsyfc7e9AG26f3nK1u5b5z9tnhDRydb1FKc2zp09JKeO9p21bNpsNTa3fKVnIt9IGpQ0pJNIwkqyhsQ0oxTBOTFFCmGyxdSb2BFOJ9v75nvE4Yk3D/RoXPauUc1AReSFZah7RVIDSS7iULrKcvkdML+2sL2J5XBkhoHPisT4/r+YDLh/3A4o0qvaU1nP19MyLwCQ5xc5cBPdsMJc5Q+Ic0DVlubyEZX7cDOmKFStWrFixYsWKHyy8NiLadaLgxRTIIVdrrChbxmhyOVVCzCphCAFrHc61pBSqsiPEc57Jm9UWpTQ5V4LrLX3fM03TkiI6RenJFBulRtcQFwkLsmidlnm3lBN3d89qf2m10xpzpv6cyKN8n+vcnBDdcehxRuOtYZrUkpQry192mp2vR8Sr08X6bAVW1TIK5VRjs7yXSiDqvOMckHSq/JB0YmMMwzAAYllNKQlxK4Wua4kx4Zwh5UxKmW6zlRsFWmZ4QwxopVHW4pwEGYlKXJYEVa00wzBgtLzWJgw45xn7I1YrNl3L7TjQNp6r3Y7D4cDxeJBQpdbD9Q7fNjy/O2Ct5cMPP+TNN9/k3Xd/iMPhjmEY8d6z2XRiu7WnGppc03BzScQQOEaZJXbeYmb1mdOxJ2dSmS3aQjLn41tKocTE+++9z263wfkvLGFUs2qel3NV1wCgl9GsIiFVyzEqFKXI87G5dz/uRXvr5U0KfXHOSfAWyE2KUm9S1JdF6bNvz35XXmrFnYOV5vXenyXldF7eP39fRDn992W8c70FuWLFihUrVqxY8QOJ10ZEQ5iYpgnnLE3jK3kQW2EIE6YG2sQYL9XAAmGaOPZ37A+3hDjV1FbFG2+8QdtsibEGA1FIWapZxnEAFCEUmqaRzsc40TYt1jpKVRElGXcixomSE9M0CQFUCesMRnuUMtWJWYS0UqqqWiqRsTV0SNEPI+ZwR9fJ7OUMIYUwk8n5/V0Qz0pGdU3APQ/FqQ7KJSxHa7MQ0Rdm887CjJSS4KacMyEE6QatJLRpmtohGkmpoJQEN1nnaZoWbSyqFIa+l+Afa0jhtG7nHNJPKim01lliqoQH6A8HvGtBWz784H0ePHjAputIIbLdbun7HlvrZGKc2O42xLs9bdswDCNKH3n29BmUzPXVFYUN0zSRjGaz3aC1JCHP5DylVFX1wKZxVTGV8CpdLdGN87z5xhsUnnJ37ImxkrmL8CHpjdUFDvs903CDtQZqDc98HOePkucbBfOxNvMRg6WntKLoF9KKXjZjebKJn9+sOF/23pxnUZwn8uYiIb7zsSj3COXpPZ9+10r1EuczhTSXcsEdS7nsEL1/Dp/syPMPWLFixYoVK1asWLHiNfaI9ns22x1N09EPI3eHQ515kwCbuRJDab1c/Cql6PsjIdbZxmqxlTlLzTQGrE2k2lmpNAxDTwgTpERRmrZt8d4zTROH475aghtKKqBl7lQIaaEsNss5cKZKXUp8lqUuhxYFbrYRC1lVQjKKwlrHME6yTiUdp7qIAuadlXVVK6eq65UUX1FeY5iDkOSzEL6y2C5TkZlEq8xZUqp85CTER2stM55KE6YRKFJVozXGeFSB/nBkGkbGMFIoXF0/pO02Ym0Fcg4MhyM5C8lXaNquQymNdZ5x7MVuO01QpF7GUgjTSNtumIaRFCZSmUgp8MF732LTtmw2W6YgNw+cgrunj1ElkihsfMvt8wMxBowxhJg49r1sf1NnIrUhp4z3HZtuy35/IIRYrbCKrmtpvMP7VkizEiJqjMUYxc2upeRrUIpndweZNYYqI8pn7xuUUhzujvT7gXbTovX5XKUsWkohq1PI1DKTWcmh2HLVMuMpT5Lu0UIBdU5AL0ncXJ1DluW0yuS5AmUZuzwLsxIDbrXaQil6IbqnDZT/LBR0ucNRldGqrJ6beC95ZZ2FPVu+6qiz9FrV2bK8zoJVDV2xYsV/hnj3n3z7lZb76H87feoy75juldZl7o17rPh06PuVYB+73Kfjv//fvPtK6/qJP/vNV1ru3tzKil8t3BsX+hWva8X3HK+vR7RkwjRxe3sgo7CVfEK1oJ5d1M72w3Ec8c7RtA3jaNntdqJEIr2Xs8onKiPEGMgpoilCwpxfVFaArtswjhPH41BtuBL68vDhI0rOpBQJYWQKknibcsbUehlJsJVEmHEcOfZHvPey/SVh0NLLeQxsthusCcg854Sq6qFzVsJmAEoR4l2Q/xSFVpoU42IzjSlV1U9IXkppqWzRpSy2VDgjMNXSaa3FWEscp5pSK9UrzkGKkTAFxmGAUogx0rQNXduRU2YaRtq249jfEcaJUsAYh/VuIcUhBIy1EgIFlJxQCpyzxGlEKY3RhuPhQNO1OKOZ+gNPDnc8/egDrq8f8OSD90hT4HrT8tFHH5K9p+m2XO12PL+7Y5wmtLE4LxUwfd8TY2AYA0Yb4kdPuLp5gNaWw+HIZttys+vYtBZnNEYrrLE4azCKWtcS6lwwOGsoOS8771yRlAAjxfHQ8/57H7C9bmna5nQ6nz2nlMypaUeR08kaLdxMukFn1TTnCFpuHEjd5kwk5fdkIYJaCb/ThRyz9NHWg32u4M5hQxmp6pEAKUiVfyqlSC/5lZzDs2bCPP9VfiEt9/zren7N6uxpdvZl/zrkxTJ8HnT0afUwK1asWLFixYoVK37t4bURUdc27PcHUpkrUk7VKkqL5S/nLGQuJkIlULntsK4qj7WSBRSxBhqJ8pOZpkHm11QWlamG+syJqCAKUYyBnBMlI6FDSmZO1Wx1NRaVUiUXp2RbUbQ0oSp5bdvW+hBRiGKc6PuBq+vrhSArpWi8XxJec85i1FQKbWrgTZH0X6mBCaRKQlOCECea5kR+5jqXc8y2yJTExmutpaSTFTeleEH4p2kip1M1DaXgnaNtNlhnee877/HWW19gf3e3qHVaa7yXYxZCOLOHiuLqnCOnIIFURokCPQwYrQl1Ttc5Q9e1pGngo+98m+PTJ6SUefb0KfZs7lSNI0orum5D2/Q8e36L1dC4h8yJvV1rEJKfuLu7o+12GCvPj9NA8Q226UgpEGNiouC9qWFVFmfFSj0MPSmnRYk8t4THGBfr8X6/53B7R+McRat54Rdme2c1tJwdL600+YLgZSCT0ly5olFZnc0Bp1N/bFGULCqorlb2Kmgyp+dKsNfs062ENGXhtgsR1ShVFvFzJob5TPaUMKSqlL6MKFaVc1FI5/d63v9738Z7pquWRaW9tAevWLFixYoVK1a8ClTh80vNXS9FXgteGxFt2oYpRlIWa2isRG0JkdGaGCPD0DOOgdY3uLYFWEioUgp9MaNW6ixlqspLZr+/XUKK2s01vunEYnk4UHK1q+qCthbvO7Q2UvtRCrlEKLnWsZwCk+ZAG+8cpYj1M6aTxjST0c2mRSs4HA7LNk/1+XNgkUEJoUa6KXOKVYFLpDx3Y0aOx9P8pFKKFC81rXmOdH59pQ3WSn+mNqKeWqPleSUT4xlR1ZoxjRJgNI20XSsK5ZMnbLqWMAVCjLQbsafOhDvGcVHjZqvp3JO6VHRA7bbUxDGhjWYcBkqyuMbQNJbWQBoOhBgxJEq1WzdNy/5wBOtRCjbbLTFlDoc93lp2246SMyEmpinSNA1Xuy3ee5TSlJIoKTONkW2raJqWXMQqHKaAdZZSTD1WHdvNlo+e7eucaEFrg7EGClVpV1grlTRWaUpKaO0ukonOZzXlB/MXlbKpc4PqTHTPHq/n2fx7sHhu5QysQmOp5LDa14s62cU5WXuFzM6vdDkvfK6mUgpKn6zC59bhmai+iHIRQHRuCZ5/XO5V2JwIbanvc1VCV6xYsWLFihXfJVZr7vc9XhsRffrsMY3f0PiG/jhCzqDAWo8xjgLYxuKMo2xE+TulwKpFcZrnIUWVk+tqY2AKUZQdbWi7DSCX8o33OOvQSmOsZpoCISRAE3OW7QBKytUQmSklkWJc6mRmO3DXpSWZdbYnqtrNaIzGOqmPodbTDDWsSG82jP2ARpGVkdeP8jqliP1WyChLSJExRgKDlCGn02yqJPQKcRPFTwhC226AwjAccU4IKQFyDOSqWplKGpWxDP3ANE1Y69hur+j7PWkasd2WcRpp25ZcyWtIgVztvqrUblEKymhs0zCNo1hKU6QYgzUWpS3DNHK16whTIvQjmg3WdZjGku4OGKUwusi6neHJ3ZFcNExBVEsKV9sOp0rtaAXvHV3rsDZxPA5oY9jutoQQ6MeB22GEp5m+T7z5xkO221YCmsIkpNHIfO6hH9kPA1OaiDmiipK+1yJxsxrkPEiFvp/4+jffx3zwFJyXmxhW03ix/TpnMDXgyBqDMWVJuNVaYap0r7WWBN+qiqvZZq0UqqQ6y6rPCCFQSq1embddcK7gLkSPRRiVr0tBaobEIlupppy7WQhtNQgvz5nXkdXpa1Uuw5hmsprqnKvCiP343r8QL6r36qU/X7FixYoVK1asWPFrH69PEfUbjJaeR2M01hqOxyObzQZjLCA2WuccuRRi7akUZUogdSYnNUY+DOPUM42Spmu1Wq6Fd9stm24jdSUxopTBe0/JdUaUJOE+WuMbsWGGkCWUqBJfay2NMbhG5kFLtV4mlVHKoNDc7Q9VBZVZ1JubB7imxTpPNInb21t2my2N9zUpVgv5ZCahMt8qVSDyYa2VYJ4zK6MoZ4ZSJGk4Z5kTNVYOa4yhfhYr8DiOMgOJqK9SU6MpuSwke3t1xTRNMvNpDDFEus2Wrut49uwZ1miOQ0+33SzkSkhwRhmLtZYURUmepoGSE123xWhFihMpSvJuLonhsGez29C4hiHfYZ0X+yuaw9Az9AP9GMg5Y51FGc3+9jlWG7ZdB0pxe3eH9Z43Hr1BQXE89uQCxlqOhyMhBkpOHN8fGOPIGzdXdG2D0aCzIsbTfjXG4n0DQWzXSs3VN6eZW2stKSXe++Ap/+kb3+Ebj+8IRYih0QqrpR5nns20xmCdrim9Yp+1VtVjamicxllD4z1N07DtGjadp2sa2sazaTY0raNpDM5prNHYWvVitJF1mSLk1xipDNLSLwqnWhhdCadU1hTgNJucKzFeKKNaEobkPFPyTFFBde3JPQ9JUos6Ou+ruVf3vu32MgFYFNMzt/yKFStWrFixYsWrYVVEv+/x+mZEbbvMFTonhObhw4dSq5JOc3bTNBHSyW5bcsFodUE+Z2urhAhFjLZ03YYQItPY11eUecJpmhiGYZnxTGFCkTBGQdFoO6fYpjqbKbN4bdPgnGMcxbY7d2WGEISkaoO1pqo8c3BQEQXMGqZplE7OEGkbTy6Zjz76iO12h/eOUhLGqmXuUhRQIbsyy6gval5yKVjv0dqSc6pJtlVdipHsEnCy0cp8qGyz8w6KzLoOw1CDmU7vp9TXsNYSU8J7z7NnzwC4vX2G8x5VMikFvO9kHaVwve1qMm4kTlLBEyYJmCpZrMHTOGCURqlMHEfGEjFKZkK1MVxvNjx9dkuMkdu7O3JRhJDoR+kMnUJmH0YOY+Dhwwd01w/44KMPMb7BaSFJIQZSTsQk5NoYTU6Jp0+fYslY8xC/abHGYrTFu4anzx7z7OlzxnECJR2wxpz6XrVSso3VAu00vPnoho/2PeNhIheIqRAVEtikFCXUmyTlNBMqQVyAkplgc6ZKUiDXuUxVZ5KV0jW8V+yzWilMVVatkmRoraFpLE1n8d7jnCiz1hoa39C2DbvGcLVt2XQtbWNpvLgDfNPQuIbGeumX1dKDOiuzxsoWaqUxy0xrtQorRVZVXde1qFTZSlTre2YOcLpfR4PMqZazYK0VK1asWLFixYoVPzB4bUR07iaUtFxq20Mmp0yYxAYbUyKmVOfeqJUsQhJEXVIyV0mhaaWPUlXip0OkaTVxsyWXgjGWxntu7+5qHUmhpEjJaYn2nrIoqM4YShEb6nbTMfRjVXYkTKbkUBN2Na1vySDEowjZvbm5JuVAzlIFo5Uk9SplcI2EFD158oTrqytiSox3I9oonBPyKrUi9ixhtJqEc15CZrQWy+v5rKgxkuI718rM4TVATQAOaKXxjZCOcRgYx4E4RcIoMfQ5Z3JVsqZpxPmOw36/2KKHYWCz2UjAUZF0XyGdYqPeH2/JYSIME8VZUDAee3KY8NZw3N9hjWWzaQg5Mg2B/jiI8l23s2mkVqakDGomhInnzw+I1mbZHyeePPsm26srirJ88zvvcb3dsttucdbQes9u0zEO0gObU8A7S4yZ/f6As5r2WmaOS1G4psG3LbZasHMWW7ak0Bbp4ayECkTt3HYdj642HMfAGDJ6UQNZAn80opAK1ywUY2S/zVhYmDzZKGpSrnxWClQ+EbhQEnG28pZpefrdvuqeVQEtCsr55H2RmyIUuZFTyHU+WuOdwVm73OAxRlVCKmRWG4u3VtRdpaqiWqqqqzFG6m26tqX1DmPlXG6cpXEW7yUh2nlf54UVzhqp0JmJ74oVK1asWLFixWfAGlb0/Y/XRkStdShdaLsGSmE4HBcLaQyBmCSQxTdio7TVcmqMwThRQFWdoSvagNLokjFkcsncHe4qGevEkKgksdV5T8mFlEUxXFQqpbHe0HpHSRJ+o1UmE3FGk6qK473HZC1EtSjCGMm1M9NYV1NGNVrbqsQVlHa1uqNwPBxQSuGdZ384cnc40niPNpq2NHTeyYxdgZwL1qqFdBdOVmStDTnGat8Va6lUkcq2TONQO0ItWitinIhBkl8phRQT0zCQwkQcI2kOi1IKbSTMSSy6kZxiTbGVZOLtdsvt7S3OO47751ht8Y3n2eOPSGHg7vlj0jCRoyjZyQdimNA5EceJrCONN4Q4oYFpOjJm6UdVVvPhh08IoeCNJ2Y5no13pJQZxhFNwRtDsY7j/oDxllI0j5/d8eT2yNV2wxcePeDBbsuu2WKsrupo4KrraBpH2xpyChjnmMJEyompWplNJYsFRazpw0oppiDBSFYrOtPyYLfhzesNj2+P5DyhlpsGVemrk5KzFVXV6Fr5pas/V2d9ogBqnt8UdibdcvK8XKQ3balqyXohhdSOWVW322iDrqQv50yaa2mUAq3RRUkHW4YwZWIIS5BSyWV5H2cZR5WEy+y0vMeEqtZwSp1bVqbOoObKp2eHAKAU1szbpdHWoJXYhf9X//v/06/OH5oVK1asWLFixYoV/1ni9fWIZiFRcRqZpoGckiSuWuh2DWiNt04u1AsLkdPGoLSq85Aag64Jt4EUj/TDc1LRXO0eERK1E7OjKM3Ye7pmS6oJt0OQWUhb7YdSfZEZQsAYdZpzI1UzoqZtG1Ce/d0tQ98z9gPjNJBLQWsLaIxvRKlDX/SjAlWZEmIUU0SctBPb7XaxI1/0lHL62YwTeY7V9lgJRpYZxJgSOU21biTVucYIZHKOxBQY+qES23lOT2ZGYwyMYWAYZG4254hzGogMw8B2e0WYBm6fP+Xho4f0xz3OOKBISFEOHG6f0fqWkgMfvP8+XddgjcZoCHGsFucajlQ7M6epZxx7Nrsrnj674zBB1pqnz55jnWMMUfKslEKRaVovJlGFhBx5L7bmmLjd7xmOR55f7+gaS9c0WK3wzjAGaFonFSjaknKudla3WHmlduW030speO9pnEeRMXWbrVa889YjbofEtz54SqzPncN7hPfJzRJhlwVKqo+XRQmco39KKVLvUk5W1tMcrj4LH1KnapQiIVfMSmWZg4pyDToqoqzWc09UWuScXxRe+ZBk6JOyW3KR0KI6AFrmhGolNzx0DWOSmWY5l1Eyt6yUJeUkVUQFYp3BjinLXKvSEOT3TZ2d2ytWrFjxq4pX+HuTvvHtV1rV/+z//L/71GX+1U//9Vdal1PmlZb7fkcq3/tQgPYVxj/+L/+L/+Mrresv/IXf80rL5cPhlZZb8Tng1+AlRN/3/NW/+lf5B//gH/D1r3+dR48e8Qf+wB/gL//lv8y77777Xa/3F37hF/jJn/xJhmHg9/2+38d//V//15/jVn93eI0zogZtFClFurbFOV8VPLO0RWgU0ziSkyTPxhSxykBKqCSzdsVolDGUFBgOTyEnXLdDGU0KkZubh1jnOfQDhYmYpmq9zBJQlDNdu6XrNgzjQOMsQ9MQw0AME23bCgEwpqqCicN+T+MsJRqM8my3tRM0QwiRWEZK8tw8+sJCKmK1fI7juKTVLgRAWaCQYiAAKYF1GmubxVo7E6K5x3QmkKXI1J4xhqIKMcqspzKGlKoSp0TB0loRU5SZ0pKYE3fn11BK0fc9Y+wrAS+UYsm5kNKEUhKw89FHH1JK5ng4QFbsDweM9gyHI8SeOPZMORNzZhwOUEJNklUcDns2m01VaCfGnFDAlDL7fc8xFMao+OjJLb5tGWNiDHLTYoqJnAttI0pf2zWoSS+Eqm0aGl9rQ3Lhbt9jzJa2FctqKXC7P3B3N7DtWh5cb7i52lbCftoPui6bz36Wa6qzrqnIJUV0MVx1nutty4feUcbMUkF0/pnZzprJiYWgyj7n7PiKFddgToSz3oyYO0mXr2eGqDRzVdFsHQdFyQo9/+Nbk3lncmuNQal85gpWs3BJptQZXkXmdM6VAkYb0mKXz2KZrk931hBiJIksjzFeSKqtv2dFS1CWolrr5RxWzPt0xYoVK1asWLHiBxvDMPB7f+/v5Wd/9md55513+IN/8A/yy7/8y/y9v/f3+Cf/5J/wsz/7s/z4j//4d7Xun/7pn2Ycx895i39leG1E9Grb1iqWgrEG320WNUoruWjNIZCAse+XKhVjLM4rSs7S96gU4DCNY3P1JXabBxyOR6YY2HYW5xoSkVIC1kCxGusafNNijUcbQ4qJfuhRQMqZ7fU1Je2Yxh6lQWkhI9Z74vHI9c2WxoLXCpWyBO+Uwu7mBmM9IRd829F2W1BS44LShJB48vQ5qSRKDORpIoQJ43y1fiZCiJQCXdfivdhqYSa6sQbmzFbJ020+paUrdE7FNbAkDEuvqFpmF2f1qpSqgynIOZCS2D9VljncmbiUFCUcyRjunj1hf7fn6vqGOE4c+57+cMAaeP74MUZlDscDsYl8+MH7dG1Hv7/DP7gmZxj7A9vWE2NaOk1zToxTYT9mHu/vuD0E9sNIPBxJ+WQJNVqTY+Lxk56r6x3WG3nuEGg3HY1vls5YaxVkQ46JsR8p1mC7hoc3D/DWE6dAjJG+H1BG89GTZ8SQFuJprTtZbUvtdY2BYmTnGwVjjOwaz4NNQ+sMCl9TaU81LABKzapzVfGVpSDrnFVKY6sFt8zpulUNn5XVUlBoynITQoinVppS9Bm5PSXYzir6KRBJLe9J1YhbrTXUMCSxrIOd/yoYS06FMpPcqirPgVwSNnS6FWmtIRUIU5T5XluwVqzClIypN1yAeiNELRbmFStWrFixYsWKz4JfizOif+Wv/BV+9md/lt/1u34X/+yf/TN2ux0Af/2v/3X+zJ/5M/zxP/7H+Zmf+ZnPvN6/+3f/Lj/zMz/DT//0T/N3/s7f+Zy3+rvHayOiD994a6kJ0daAkf5KpRQpJ1SKlNFgC1xvd4RpJJeEcQbjTputjMHaBu+d2HNDIKiJsX+G1YZu13IcAx5w3hI0hJQhR5LSOG+ZpoC10k+qtCZRsNbQmi0xjUBCUei6js2m48Nvf5tw7GmswzQW1zQY77FNS7fdog2kGHFWAlqU1cQExja4TUvO4EncffQtYihoIxbRQz+ibCvzdiWRcqRMsVodVU1sFdXTGBayMifjnofglBiXx5RSwlsQsiLVMLXWJhdiCkxhIGWLsx6tJEimkKsNOJFSIqfI82fP8M5DKRyPe7797W+JvTaNjMcjRiuefPQB1hqZI62JxilGmVMshXHoscaQolSzTCFQiuXuMHDXT9zeDmhtGPuRlAspZ1mejHMWHTJPn93RdU7CkEJm/+SW3baTsCmr6HZbjFI4I+mwXdvQWEOKEdu27DbXtJ1Hl0yIgX4YFiV+tkVrY5dEZkUhxIDOotorFKlAyoXrTctV58kp1n0MWiu0MfXGgHhddWV4pYgSXwxLaqzSojiKcfZERHMlastWqPkGhJLltJZ0W2MqYazLVYV1TkM2zLYvucFAkdliqcuBWV1PKaJUXNRZpUu1/c5EVp8sy5yScGc7sgGMt8SQ6jrEHpxyFhu1NnLzCb2sd3XmrlixYsWKFSt+0DFNE3/zb/5NAP7W3/pbCwkF+NN/+k/z9//+3+df/It/wc///M/zO37H73jl9b7//vv82T/7Z/n9v//380f/6B9diShAKJpme8VV04iFUZ/UpxQn0lT48MlTVJTQINf4GgjU0u0eojXkLBfMpMx42JNjTykRkye2HobhOU8/2HP18F2yymhjaJuNXBAbj93s5CLdilUwpbSoiFYrNl3LOFn6/kiOgThNbDYb3vniF3E//MNo63h6e4fG0HWdEA+VUUSSKZgCpIDzDU4bMpkHVzumUFBpQl3vIDmM9aAs/ZRJquFwONbU2lDtlaKEGqMoRUik905Uu2Ve9KSOLvOFtWrEGEOoYUTWWiG6KVOSqHzjOACZGCeappVZQXJNjhV76jRN8pwsgU19f+T9995j7HsOYaRESaV99vyO/WEvNmptltnT/X6P856YxJ6svJfXzxICNKWJvo8cjiNTiNKBWkceQ5JZQ13Ddry3WKuJMdA0DQ8fdDzf35FCwncercBZi67zj+M00rYO6xpJjC3INqjMpm3lBkO34fYYakhUrlObAqVEgZeaHKnC8cYg4mihaz0bV7gtQeZWi8ZoSZnF6BPRqp+VBl0V0FznLo02NfFW6B6ldsZqhdWa66stfX8khkgpBpR07c6r1nCK661fK2XAmvq4JqUsqncqzGJ6jFFSpmvFjXUGVTIli3VbmyLztMqgtFlmVSURulprtSZFsXtbo7EGnBH6nEqWwKucSQS0kS5UYx0oAyVX1X/FihUrVqxYseIz4NdYj+i//Jf/kufPn/MTP/ET/Lbf9tteePwP/+E/zFe/+lX+8T/+x5+JiP6pP/Wn6Puev/23/zbf/OY3P89N/hXjtRHRrmsrURKlTSmWzkenNUZp3nrrTeI4UchY32GaBusaUcnSxDD2lJzR2tBtrsjZMxyeUZTFN1do01K04sGjLzDESH844pyXdFvj0FbIbdt0TONAToFoJdEz5QAqsdlspAczRbrNlpwSxRqKkovpm5sHTOOI0hLCEmMgKbh5+JDp2BOnkfFwx/440m2uePTml0i+kLMjN+9SSsbaBm09KcF+fyTFx+wPe8I44ayp44AKhdTTOOeWuUaoFsxKROfQmyUgqQYfpSShRQA5CqFKQeZW51AmpVWt9hBSMtfFAKfqF9+SUubZ0yfs93ccbo/EMEEC6x13+z37fkRR2LWmhtUEUXir0phzJqXINAWGkOjHSCyaY584HKSXVVRSQBu0lvXM9mXvNG3XUHJDmCYshUfXO+IUKCWJ9TVlmrap3auFaYhMJrLpvJxjxooluWSsspLkqnQNdjol5S6znlqR0iXJN8ZQSqHxnrffuKGfJvogpFnpgmKeOT3dZFEorJkrS2rSbU1ZzmlOpZ2TknVVkTP98YDRoJ0mRYghgTF4LzcjYgpVfZ3Xp2pKr0Bo7+LHJlf/rlKiIKNP88LOSOhWKWITT5naY2tAG6j9n2AwSrpHh3GkPx5BGZlzVVK9Y42r/bGBWOZZb0nx9U0noUlrf8uKFStWrFix4rPi1xgR/bf/9t8C8Nt/+29/6ePzz7/61a++8jr/6T/9p/zDf/gP+Ut/6S/xla98ZSWiM1rfApmYJpTR1YIIVkfi7Xc4Hg5Yv+H6wRs02yuwDQpNCIGUIykpmrYhZVExQyk03Q3Xu4eEaSKOAZsyTdthnGfXKZzfnshYnqspJLzFtS3TmGlMwZhCykoogdI4u8VgSDlQyoTzFms9MWtU0di2EFJgmiY2XYfRmjBOhCxpu+NxwiiPcy1DODKNR3KMGOOw7RW23UiSbj8iAm9imo6UVAhB7LWHwxHnTFVGq921zCmpLOmqVNvnfHFfqmVXlYKGqkJmQpgkPbiG0GQ9B8+oJR117tKEE9lVGp7fPeN4PDINgZQyd4cj290VT5/eMo4Dh8PA1dVOiFQSBS9nSEWqbihgHBynwDjBOGUSmX4cCblUwhPlJoMqeK2JWfovjZFOzpyzqI7WM4wT203DzcNrUclzpm0szmla3+CsrUFREzH0mIfXkC0Ki1aeYQw8fXYk1FAkrcQqrVW5mBGdg3u0FvKVc2YaJxpreecLb7PZXdPHxO3dLcPYL6FYGlDaQNEIV8ygMtoqrLHU3CmwGqWcqPy1LkjmQK0k2NbFnNWMWhGiqNjeOzatRysnc6dkUk3TLTVr15mTvbYUQ4wyE2qslplQBWBISc4brS1adZSiSDEQ640KavBVoaq3WTJ/W+8ge+4ORznfFHjruLnaorwhtYCSKp6cqgpsrdQJ/efw13/FihUrVqxYsaLiF3/xFz/zc9566y3efvvt7/o1v/71rwPwQz/0Qy99fP751772tVda3+Fw4E/+yT/Jb/gNv4E/9+f+3He9Xb+aeH2puc4SwoQ1npQL1ll0mkiHZ6Txls4rihq4ffptrvQXabYPyMqIkqQkyXWaJkKclqoKyEyTKEOJxDhNPN8feefdL2Gto5TaA2oMMcZl1k0shhIiE6YRpaU7FCQJ1DpLnCb2d8/QBkgG1Smcb7k77hmGnnGSChatNblaM7tNh29a8htSQRPDQBgPqHDAWYfvrlHWMQwDIQRiCPTDyDiOHI89YRTSJj2hhZsH22W7U7VFzqrdTBQl1OgUjz5bjec5UhDxNNYZUsmsUUtyaamdk7MNdZ4dnLsux2liHMfavZkZw4SxlkPfc+wH+qEnxCQ2U2OINRV3HKalTsQYSz8EbvcDKcvM4GEY68wgSCiVqWohdfv1hQpptJBxYzWZzO3hjlQmusZLV6gXQjdbaff7PTFGrnYNKSVa11CAWBLP9gcO/bGSz7pbSllsujJHKcR8JqIzqcvVevrowdu8+yOPsK3n7vCM29tn1a6aKUl6W4USSsencxqtMiUHUoyyrgwxSWDWMEq6s9JKHs9FVHFdA7hi4nCQEK9cIlBoOw8ly+9EnlVyRYyRaRwI9dwBcNpStMJ7mSGW45TwjQGENCtVK168lUqgei6Uep6gFKra2o0C3zi0MfR9T0oJb63M9FoH2kjHaT0/JTyrGqDXIdEVK1asWLFixWfF5xhWdP+e+B/6Q3/oM6/iL/yFv8Bf/It/8bvehP1+D8Bms3np49vtFoC7u7tXWt+f//N/nq997Wv883/+z/Hef9fb9auJ10ZES8kYI8mkxmo5k4xh6C3ab2i8R2srvZBPPsIOkUdvvUNWYssNcWKaBpxvZK6xhuqMw1gvdA27m2t+6OYRaM3heKRpmlpdIbOSEsIjc6aFgms2ON8Qpp6ShQjEnAlDT8ksnaDON/hmC8awvbqmaRwK2G53hBiIGZS2GNdijEUbsUZOY2TsbxmHW2zT4TePKMWeLLYUlJaQmWlKjMMEVYVr25a7u1u2WyGjKUaohEgqbyTm5j4RnWdEZZ8L6T5POkWd7KbznKwozvksFVUt3a0pSndpikmIktFoDHeHPTkVQsxkFChDLpqQoMRMiECOGKspY2SYAnfHgDGaYRrox5GUMuRUX28me5UoVzI+BzYJ75bEZedbUlCkNHG3H5imEYNYrm92iq5t8d7TdR0lD0zjiFUGpQrWKdrGcX19xeHYU7IipSzW5FKw1srrG1PTalluBEBGo+U8CRNN43nrnXf4ov3h5aZAKQWrNSWOTMOe4/4WcuThzRW2nsvjNC4BRDkp2s0W4xpCLqALKUQU0FgnlShZkmmfPr/jyZPHUEOlrm8ecH1zRc6JYegZhpGYEilGbm/vGMeRGGRGN2fZdwqFda5a3OUcTDlTihKrsFKEGlYVcxJ7clL1RkWhqJrgW7IEerXXlHIl52EqON+itKUoharnz9xdWkquduLVmrtixYoVK1asWPF54ed+7uf4G3/jb/DH/tgf4/f8nt/zujfnY/EaiSi0bYdSmilECfkxGre5Aedkti9nrC1c2YjyHX0/ShJonlAKmsZwuP2I/nlmGCRh9Ys//OM0bUuq6mdOmTCOWGelkgQoOVFyIo49MSZygZCiXISnjHcN26uHKG2YQmQcB2IYsU2h8S1ttwHlSCVL6JFS5DTR9we0sWjjRI3KoiJpayhF4zdbNtfXMmeKEvtvncMUMukhJ66vdzx/9pzjXS9JqCkTwkDbNVgzcTAH6XDUQqiNrgEyZ0TzvHdU9reQi5kEzCSv3FOjUrWgiiW39lbWj5RTPXjIPKm1eK24u9tLqm5RxFQrSarNs2SqRVVsxEornj3fkzH0Q2KKd4QUQCnGcaSUBHVmclZxldaQCyEEjLGUAloLYc5D4ub6mm67ISdbVUDN0I8cnj1n7I+88egBKUW01txctRhjamKswTuPSpnOe+72RyHklQTHlMg5oLWW2hxjSVECfYxRSOumIsSJfrgTMuoasI6sTmqyVhbdtLSNZ9N6TBnxumCIpGzwfovCMPY9KfcQCtcPH4BtADBa6lJ0mpj6PQqNah0Pbr7IO194SMqREAPOe25uruXGQsmUdFJAE5qnz57J8VOq3jjZoErBG1FqnW8pRXN7PGCdJ4YohBqZ2R2niWkKUOSmxPF4qGS2kFOQ4KsQyUUxTQltHEWZJRCshESYQg0kC6g8VJV8DStasWLFihUrVnxG/BqbEZ1Tco/H40sfPxwOAFxdXX3iemKM/Ik/8Sd48OABf+2v/bXPdyM/Z7w2IppR9OOA9x5lkFqIlCS9szhiyhilcd6QfCeVK0WIkNaWkjPjsGc8PhP1y3dsbh7RbITc6pQZhwFVw1RAwpDEApl4/tH7PP/wW8SYsL4hYxjGzM3DB7gHFus9SjuMK/jGMfUSHuN9Uy2LBhULU4pS1aE0OWbGKda6EUUYB/phoNtdoYyl67Zo15CMqaqpwttMmXp0yXRNS7SZaTjSOMc4TcQUMEZJR2Mo9GWc2yDxvmG326Ar6Uz3FM+ZhM7W3VnROyeh88f8fY65JqpKyM+cy6qVQqtqTTUG13imkEghgZIakxACKUW2my1G1TnEUi3QOaNRHPueKWZCLAxjoJ9GCpCydMqWSnIBnHOVnOblfYUQaduWtnVMU2HMidu752w3HSlMKKW4ubnG7ToGCxTFlBLOWsbjgdsSuLnZ0fiIMS1FG+IYePr0KYfjhLW29tuCMfLrEaN0xcp71zV5tirGuqC9YeiP3D77kDC8i+s2FNKiRnuvpAfUOry+YjpmQuhJyP7ym2uM39Jurnj6/tdI43OefVBodjc03XWtSQF0xnkPtQYFY/GtKOoyawk5FYZxIqUJW7tgtTZcdS2te0tIvjEYJcm4aTjw/MOvMw0j25u3aB+8zYOba9BW9nu1XkvtT7z4oz+MAynH6giIkoBrJNwoJktWHuO3hJhI0xGrqHO+lpQjx+OBGCJWu1+NPzErVqxY8V2hpPRKy/3o//3Zpy7z//pjn3zBOON/0g2vtJxT5tMXWnGBVzmaWxVfaV13f+C3vNJy2//bz336QvnVzrMVrwf/6B/9I77yla98pue89dZbv6LX/PKXvwzwsYFC889/5Ed+5BPX881vfpN/82/+DV/84hf5I3/kj1w89qyKEj//8z+/KKXfTS/p54XXRkR9IxefKUmAjTFCrnLMxJhonKOxojyFKPN5qdRORgqhzjhqbdjtrnDtDXZ7gyr14ttqCWzJZak4EXnKoIzl6uEb7B4+FNJRCmEcCb1UjKATh7un+HZDycgMnipoXZimofaNijpWSmAc96Q4VjJd+x+VZgojSlOTbxXTOIDWNE2LSRP93RPu7h4TU8G1W6xTpBRpGs9m00qtilKUUu2fwx1N45mmgZQS222iadySPHp+M+e+4jkTziVN9+xnM6S25B7OfjArrM5ZrHV4n5imgEJCeHLOeO9pmkbswzWpN+dc5yATmSR1LeGk4OZSZJ/OCbVzwmy1UIcQKjE0MuOrJHxp0zakqpJqoNlsGI5HDvs7mhpSZFA4Y7jZ7YitZxz65ZzQ5mT/tc5h7RzSJIR97mCd38u8X3POlKyEnBsIIUmA0DBy3D/FxX3tfjU0nacMd5JUqzzKNjS7azQ7jAFyZjweGQ+3eKd59Pa7NI0nTtLvqoikFIgpMhVF13W0TSv9oUZShubtdc5TChjjuL1NcrMDK92kRmMU5CzzyuTEOA6onOm214z773D7/jcJY+LNH/n1+N012liG44Fp2GOMwVpqgJT8Km1NRy4ZRaZxnhQnNBmsVMQc+lu0TlxtHtKrQIoj3jtReLXHezm/5q7ZFStWrFixYsWKz4RfJSXzK1/5Cr/5N//mX52Vfwx+6qd+CoB//a//9Usfn3/+kz/5k6+0vvfee4/33nvvpY89e/aM/z97f9Ir23qnd2K//9utJiJ2d7rbkrwkMwWlrFRWqcoFWBAkoQADNdBAn8CaeCSNPU6kIMgjjQTYIw01EmANaiQIkATDAmTAthorVVmZZJK8vM1pdxMRq3lbD94Ve59LSeZNJZmHTK4HuDiHe8eOWLEiTnA/6+n+5b/8l/8VR/mzxTsjolCWWYtqwT0eblForFZ0bVebRRc0trZr+pAYp2mx3Vpu9jPjNHHYH7i8TDxqG5wJZBTjnCgY9KK2yFKEU5QC0dhug0JIKZBCzWJa11KWX7IlLyUzKS9NpDXfdnNzxzR5Hj1+TNu2SFbY5oy2K5TkkSLkKuvRdptKiLQmn8Y8lCb6a7744R/gXM/u8gnd7gqtLdFPIEdiTLx69XKx06p7JbdQVThRjuPxsGRHG7RWCzHR/wmZeyCb5f48nIjLPel6i3zdlxOdCKs8FBUpVa22ssycGGPrcRVZCqAUxmi0qSpdjNUOW3On1eaKUsRU50P0UvyT4onon2zElaSeipmUUjjrmGdPKRlrGowWyrJbiYCzjsYZOmuIKdYm4JxpmwYt1TraOEvbOIzhPlt7st1qrau9eCknaprmfj81hPBWPjVXgq2rSlgbfGH2hdvrI7dvXvP+B48hZZyD7N8w7V/hlEG3V7iL97GuqaqyqaTMmRtefvYGf6ybs8fB0fUXONcQYiLGmtM1xlCoxUKKwnGYKKXgnCOlSIwzpYC1DbtdB6VgjKYUmH2d1JGS6gZo8bRtj8JB39D0O/xxoCTFPNwhRmOanrZtMOrUGmxQul6IqK93YhxHSoooWy+GBH8gx0iYEzlkUvHEdEsmQQrMc0IpdZ+3bpy5dzqsWLFixYoVK1b8quIv/aW/xPn5Od/73vf4N//m3/Bbv/VbX/n+P/7H/xiAv/7X//r/3/v51re+9Z9E7074F//iX/DX/tpf43/8H/9H/tk/+2c/k+P+4+DdtebqE/mpRPRwOLLdbnn96guePHqMKENIhZxV3b0XIVGwTYvThhADF4/f4/2PPgFJKBTZD7z69D9SUqTZXNFdvs/gM6It5n7OpM5bIFXRyqlQiqbpdm9lLatFuJRCSNW6a3QlAY9sJcmjn7m+vePR5SVd1zGOB8I8kFOkkO5zeD7M+LiQOVW/FsPI2ZP3ePLs2/hlJ5ISgVCtoeKXMhwqGc7Vmll0QZuGea5TMdYajkdDbUzt0Dy06J7w8EasRPxU9FO/V79+mmu5v/1b+5MiqqqyOcPJ6qs02jhEh2XvUpaN0PozKWViLgze08KyNWmWXcyCIqBUfQ10FFJR99cdtNb1+S4ZVessKeW67xlPky61eVZEEXRtBfZhRiRX4qSFMM+14ChnspJqc471/FKE1tW3vlZUdddaGms5juNSMKRBq5pltAbrbCWCpx1RkSV3Wlt/JWhizNzd7HnvyQX9pielmeP+JTYOlKbBtop+05JyvTAQUyDlgu1aHn/8HbL39OfnuM0FOStUDnWHdp5rdneZ9ZnmQOuqdTalvGRnFTkvzcTZkPyRw/6OzWZH328wSy4zZ8/+7iU5HDi7eMJme4USwW27e/KtlFDSQJwCAUVOmcP1NdNxIFNqE3Ap9H2PaTouLi/rZFHOZFqMNbS9IxdIy0WNcdiT0TRNR8oRrTVaaYyyxOh/Fh8pK1asWLFixYpfIcjPsDX3Z9a++8eAc46//bf/Nn/37/5d/tbf+lv803/6T++bcv/+3//7/Lt/9+/4K3/lr/AX/+JfvP+Zf/AP/gH/4B/8A/7G3/gb/L2/9/fe1aH/V+OdEdE4HUEU++OR3e6Mjz78uNoPD9e8fPmas/NL+s2G2c9oEaxtKDnWrGiu5APRDMEjJdNrzXj7BdPxDTon8jzVRt7tY1CWrDQK0Pe7kBlZpkAQqTZHASWlbkTGUCcwSiWCPg5IyZAzJWUMmsuLc7RzjGEmlgz6oWSpNqAmRFcr8KmPRZWEUhva3iCloEuu9mQSogykUEnoMh/iYyTGav1sXUdKMI6BzaZlmqal1RVc00BK96rdCafnemqbPX3t/s/TPujJyvuTHoe3emTqbcp9zlApvaivdV6llErOfPDMc+A4zyitcWppn1WKlANCqrndUvOCJkNeSLKIYI0CUdX2KTWnmmLAmmVHNkbGFGhbR9s0WKuIMSGlMI0jm02P7dpK4rwnZ03JGmcXm28x9F1TiXipxOqUQa0WVLOw9II1piqlKVFMeWgpLvV9EmKk5IgYR86Zea4TPM2mo9nsuLi8YjjcgoZ2d0VB2Gy394p1ip7k6wWMm5c/Znj+PR5/9Gs0j76BNDuapqU9q8qrKiA5cdzfMA0HdJowpirVOUVSTPg40561bDc7NtsdWjlSDjjR0PQ4Z7l8/ATCWK3iJVQLtGpAC0UMoqqynqVePLm+fc1nP/oeMSS0cXSbLW3bMo1HOq05HqulXRlBtCPlTPQzMWdKmLh9+RnjzSuU7vjOn/kLjAVCiESZmdLDFu6KFStWrFixYsXXxp+ysiKokyv/7J/9M/7Vv/pX/Nqv/Rp/+S//ZX74wx/yr//1v+bJkyf8w3/4D79y+1evXvF7v/d7fPHFF+/oiP94eGdE1M8e4ywxzlxfv0LEorVhd/4YEeFuf8DmQr87RykIKaO0oTEKvRCgavnMtMYx3/6Y4/WPyMmTi6CKZ3/9JYwz/dUHqGYLUskQPFhPm6Y2kyKQk6fkyHQ8MA8HrGsw7QZthZA1ZdgjaWKeRqIfiaZHLj9ANX0lLKoS2xLqdqY29bFM4yi5oLTCSFU/p2lg2L9CtEXbpWVXG4wYbBPBaBKZEAMpZbSpFuNxrAVMdUc11IymTGx3iVPS7jThcrK6vk1M4T9tyj01xdYGX/nP/lt8sOo+TKqICNba2k68PGYuEHLmzf4OZ5pKom25nz8xBlJa+G0WnDF1ZzTEpUypEmNtNLK0Cmuj8HOoqmTRiKqK5D2ZC2mZ+1EMw8DxcORsW23RhHSfRQWqdXi5AHGy2wqFzabnMMQ6MyIPVuTTc02pztWcpnBSTAQ/LcecMaLwPnLYj4xj4luPnuK62oartk8oOZCjZxqObLabmnSWWqJgtMH0CvvkEbfPv8/zT3+XzfEa3Z2TUybGSOMa8nLB5HC3r1liV8+9cw4lDUp1lByIwYPr6PszSJE0jsQwoU2P6I6CoihAeeJ8QMjkMGNMg6ApKNCV/BYKZ7sd3/rud9G2pd2e03U9etkLra9pUzPTJRPDRJpnlCroUsAZ8nxOHPdstmc8/+z7zMMbtBSMcXTbM9rN46/5qbFixYoVK1asWPGnF23b8s//+T/n7/29v8c/+kf/iH/yT/4JV1dX/M2/+Tf5O3/n7/DRRx+960P8meLdWXObHV3fUrTleNxTUqhNuKFwdnHF++eXhBjrhEYMhJhAKdrGUXImpoSzFiuFFDJZGTbnn7BByKIRUy2lttmh2jMyVTlUIveECyCmiNEGZxRz8NxdvySHkZIyz1++RDUbPv7k1zCSePXqx6jwpraJ+qrM7V9/yu7Zd9mcPaaIIhWFcYaUMilnlBQOt/VnRNXypcZaWmcJhGpzjJGiNNo0KGN58rTjm9/8Bp9++mUVixYVLpPIJWKtJeWanxyOAyFEun5P17U0bXtP0uGhMbdClgbWt4qMFpHzAXJvsa22Xb5yg7cLj07zMcYYtNEoZSAKx+PEOCdaZ8kpY/uaYVS65ly1ESigVKFQW11jTrVIR07TMer+uLXWKF2JqjaKxjliCGitCSHz5m6PMRatVG0sNopSBrRatlGjB2vZ9F09R87wwKuFftNTbg61HEjq46acEPVAWCv5zw/P/ye2WEUEMoSQmaZEKbraWHOmlEjwHrLHTwdeP09YW62+dsmpYhx6c8n2ySeQItZ1aOuIpRYWEQMU8HlCUzg7P8c17v7YrNFAJoaMskLXtWgpzP7AeHhdN1O1kKJGlMZohe13RG2YDm/IYWQejxSg6c8REsY4MoJRiquLC7rzR+hmR15am4tuCPPEdnOBdR2xJIbhhqncVGKrFK7pubx8zPvf/nOEMDHcvOHuZWK6fcV494rjzQ9o9H9+uHnFihUrVqxYseK/hD9t1twTuq7jd37nd/id3/mdn3rb3/7t3+a3f/u3v/Z9/9W/+lf/i/nRd4F3RkSbrse6hjPT4ExbCYgACCFmrq9v6Lqu5vEwWKtrQY2fENFgWoZxRGuYxgmtO2TXcnX1jCJyn2PLS/FNyYVS0pLrqywklIgzjtZqpsM1092XdEYh1pFSneyYY0FE49oNjz74M/zwd//vbFoQ06BUSy6Kw5svSTHTXz6rbb0p4WOoZTyAkYQYU1VNpWtWVAlKGXIMCzE2xJghT6Tsubt9XXOZWWEUaGWYfaIUwaCIuUAKlBxRwHQ4YgRUKRhqa6+IQilNRkN5Ozta6jboiU/JQjXVW19YcK+llrL8vRJFkULlTwZjqlU3ZMHnQoiFUhQ5p/q8CkRfz4ezCq0VRhtSzAiFyUe0KPJCTFOKiGiU6EpwtabkavstGY7DEaGgjVByxDlTs68isORETzbZ3XZD323RVBtsCIJWBS0WyQK51DbYNBPSRIoaJCNYWIhkjBHRgpAJIdXHyqnuaZZqmzbaQIEUIiXVCyXkjJQalPTzjFYZbTQpzox3M9evX3Pc3/DJt7/F+x9/hLgOff6EME2EGPDjsRJ2EUqcyTlzfn5FQoGxNF2HKEtJCU3GWMVYMsQZ4kRJE84o2u0FOQekKIgRMVCUoETTtBtSjOQwUPzIfLyl+D11U8kgyqKMA6UJ08z5e9+6tyGrAko08/GA0RblHO3mghgCIUWsqXM3PgZyiUhWnD96D9c1qDThD7fcvvqc+e71z/fDZsWKFStWrFixYsUvHN4ZEQ0hEELNetaZDlezeQB5Yjgc8eOEMRqjA/Huljge8H6iO3vExYffIZaCEqHrGkpMzNET/IiytubbllIZhZDI5FxQWmiblmmeaKiKqh9uiPMNrdP3qpdRsN1t2bgzXNuTlcZurnj2zd8i5wnvR3bdBc5tMdYSUyKmTJgPHMcjYjTbzRZjXSWsuf7yj5SaocsNbf+Y43CLM5a239Ud0nHg9voWP06V7CyccJ5roYtzhnkOWKspseCsRqk6MTKOI7AU/hRoRBB7UvW4P9eVhBa+1gWRUiqRv7/xww+d5lWcsxzH2uCaUi3QscaSclWxh9GTQqhzKcqx6RyKSoRyKWidsVZqiY8IqRSsdaSUKpFeyopOjbWNcxijCPOMNYrdZkMqhZgyoJknXwlfyng/YwRc42iaat8NIWKUIaeHpuC+7+j6juMhsEictd11yYjO04So2hCccyGFejtr9ZK/TbX0icI4Hhnu9py3Dq0tyiq6tqFkD7qBpLg9vmY41imZly9ecHF5gXEW51q6s46cE9M4QKnE188FqwTXdaA0kw9VzPZHSk5MuSBe16+FI8ebN0DBdmfo7hxJtjZBU5bio2oN1iK4pkU3jmEPU7jDxEBMEylX7ZNUULbFnSVcv0G5jpxrSdLzH3+fbdsRH71Pc/YE0RorhSiZYbijHCJNs6HfXuC6BlGKGDvImsZ0vH/+hDyP/zUfIStWrFixYsWKX2X8KcyI/qrhnRHR0y/5p63GaRrQWi3FK55NaxGBrmspYri+fsF8vCbMR4Zp5Pzp+3Rtu0x1KLJWjMc9t69egdJsL6/Ynl9UC2HlN8QUyDkRjYKSkQIxRsI81BmQ0iCSESAXhe22mPackjNWGWzb0H30bY7HicPtDcdp4PXNc+LsySlgVGEaRqLSvP/NbzKME0o0xljECBQhJs/1mzcY5Tg/u0ApwzTM3NzcMRwP5JQ5Ho/c3h2WsqREiBmJup6LZa8zBoXGoJZx61IqMTjNt+hUrcCIRosA6n4zVRT/2YKYB8vuV75IyfkrMv6psMdYS8kPucnayLtYknVVwwDGyaOVYpw8fddUY6dUEhdiqkSUgg+57olSlVzv46K86oWEFmJMOGdxyyxOijXX27c9x3EkxNPtAtY5tDZLSVOdCnJLA7JRddYkhIBzDW3b3edOQd1Pu5wujvjgCSEu96Wqmp0yMWaMSeRc/ys5cXNzzQ+/9wf8eme5ePSEaRzIKaCVoASUznzjm1d84xuXxFjQ2tK2zVIAJYTgl93VQiqJbrOj3W6rhVfq+7mxhjDf8Ol/+H/TKMWTD79D++g9fLGIaRHbIaXut2o/4PqrOmHTtMQsxHlcnAGL7bhk+t05og3D8ZYwzQiGfrur8znOIcYQwxH8sZZPhcDh+fd4cf0cqy2bi/f44JPvItYSSqHkmTdf/JBpv2d39pjL9z/m6tnHXJxdkVIkhCMpzPUCzYoVK1asWLFixYpfKbxDRTRiFEzjDeNwIBXQV0+rnTaMDHevGMaJDz/+Fpuzx9hPdrxUv0d59Tl+nDhcv+TFi89xmx3f/rXfIBYYx5Hr5y9QtiEWaNqOpnE101lASqFrmtq8miLGOnxIKN2A2Gpf1RklCm1aTLOhoJn3t4wx4nrHq1evePn6FiN1YsRowWhh9J6UQLmWp48fc3Z+yTBH+rNLSkyElChAbyyjPTCOI2+uPcHnmvt0Bts0GOuYY2LygZjLskm6lAkt1k9tFDmlpd2VpWE3k5MQY2KeA6aAXuzAp4KmUkApIaU6W3KyQrPkRsuDbMr9D/wXZFOlFEZrki4obSiFe+tsyQVlNE3bcRhGlGiCr1uoSleSp6U+H60VthhC8mglpFzbdCvpy6SYwJSFZEPjbH2cUmdGxBiMVtVyLYICzna7Oq+SEzl7EpVIV6Jn0NqglUKbhUCXwjT7ZZYmk1OqNtucCTHWhtdUOAxVwbTa0jYNztX3lhKFc7YSbwE/z7x6/pL3P3ify6tLvJ/IKWLaBmNsXcfJCSlgmrrFatumHr/SaAPRQ05wux9xRXCuqflarQjek3LEtWdcXT3h+rPf59M/uOVJ/vM8+ujXydaR0EjJNG2LcRvu3nzGNNzQth399nG9qAGM+z3JHylknLPYtuWsbXnkemyzIxdF9hPRj4RwxBlBiQKJdBvLn/sL/x1/+Hv/H958+WNuX/6Q8fYFm7NLrj74Jv3jj+m/84jnP/j/cv36c1588bu0m3MuHn+Dq6ff4umH30G1O0KYfz4fMitWrFjxX4OSf/ptAPn0Pz8U/1+DuYSvdTvFT79wp0X9cQ/nj4z0Nc9Z/hnKTpmv95hfBzv19fasn/8PX+/cfud//um/Xpd53dD+Y2NVRH/p8c6IaOs02e+5ff77lBQw3SP8dMZhv+fu+jlSIh9/+D5t1xDChOl3PPvGr/OSwvjqFSEnjsdbfEmEkEBpPvjom4hyuLbl7NEjtLHLLmWqUy7aQRZyTNWuG1OdnVAtSjQpZ/w8kkWjpCWFQtGJFy8/rbk7sRQMTy7PyWS8D9UWOx2xrmVzdsn27BwKzFNgs9kSQkTJg7JWClw8ekq5eYP3CdsIm7MO17SkVKr1tJ2xriFnRS71gyqXVLcygULGKoWSvPzDqaRY0JQMMQGpME0zWtulCbcqgimV+/9d+WlZqmcqTv+m7ydelq/LUvJ0+npVDqtVFbHkrBYLsCBFcMqAWGL25JxIMbLbdMueqEaUQnKqSjQFEzTOCCFCFE1JebHvCikGtCr37bw+zAzDoU63aLMonRYRj1uswoIw+0RaJmViySitgUJKAaMMSjuM1hyHkZu7A3d3R2YfSLGgZMLaet/z5BlDJBWF1YKzGuc0ZmlFFmAcZ7IpWK0QLFdXj9luzwjzyNnFOZt+Ry1gyszTiFbgTN0CDX4mxoCUgmhNvWShETRNmzFNA0rjx5nNpr/fYx2OI92TTyimY3/9WS37mkbabkO3u6T4gOSZedqjTcOjJx+gdCblwhwDptnQtJY3r1/UkqbNOdp0JD8w7m8p+YcEP6BFY13dBzV2g9WOIlCSp7Ga7/7Z/4bnF+/x5ac/Js0jw901sRS++/4nNGfP+PbFE9J84HDzOXevn5MTDPtb9tdfsj27xJj25/hJs2LFihUrVqxYseIXEe9uRzRFclacP/0GWhmKasE4zrTQtQryzHC8YRgHMDt6FF3b8d63/iyPPkyIVlw8+YhMQTc7RCuc1nyyu3zrAklBlYItgg911qLvu2WvEkilWl0lL5nJmqErOWOURkQRc0QUeD8gYrHNFqc1xrWUNqOURs7PKYCyDRmFpETMmTgF5hAr6ZKaIwwpLI2sLZeXG0QJMXq+9wd/iPczbdPWdlillyznw1xKodznGhvTorUgCqpjs9z/V8lWBhLjOFVVdNkQVepk0T2JneU/Kc79z827vE1AT02yZdnafPvPkjNaG5RSjMNACLWMSYkCFMOUaIzBNA8ZzFIKxipCyuhSVdNTS61SCqsdIfr7x9fGUnIhxEzwE1Yr+r4npYR2i+IIi1op+JQYhoHWOrabDXYplEopELPh+rZaoed5JuVSLwhQCb8xjgIE79FSFXVnKiHtWoexldySQFK1eufSMMye0Ucumw6tG0KCnGI978qAUhStSPNEyZlcEjlF+naH1YZXX35Jaw27iyv0cpHCGs3t9SvGcaobtViKCMqdcfnROV3fI0ozzxONNbx+9SP2b37MxbOP6XcXFNMhztAZiwJSKNwe74jzHpUVbX9GMYaufwTRM7z+MYzX2H5LpzucaVCl7vgqbWqJlGjEtnz8a7/B4/c/4bMf/SF3r78glcI8Dah2yxwiVxdPOLt8j6cfTUzDgRRmxv0rhuMdTb/7WX2srFixYsWKFSt+RSDwNTwCX/++VvzJ450R0dkX+v4cZc6hCCUnYvakeWAebpinI9q0dJtLMC3WGLz3hLmqgroojO1QxoJyFDJpacmtjaaJcTjihxFrTM1OivD889fM88hmu8HaTbVC6jp9kbInxkAIse47Gsc4R853j5nEIlrT9j0p1skVESH6iTl6Ysq0mx3KOPaHIwDWOIZxIuWybFIWrDMYbXGN4e72FWG6w7U7Hl1eMIwj8+S5u7lhf3t3P8ECCzm8L7StEyuiqrW1WmtBa6EIKL18E2GaZtquB5URpe4zuW8T0a+Q0FyNMyJvt+z+p+T07a9XcqsXslntowWYvb8nrFkKIWXGMbHrwIdQm2+1rnlDHzhZhE9Lpmo5XmsVMQXGccRaS64hV4ZxRguY1t2T1hQTok2d5TGOXDybzYbkI7e3twiw2/a0zqKMQluNaxpykWqDLmWxL2dCTszjwDR7YvBs+pa2sVil6Nq2ZpqXc2mMQZnaCpwL3N4c+PGnn3P5+ILtZUch17bkAoIixkDjWkzXMytFmCeywDAGdtsOKfDZpz/gI9tx0W8gJ16+ec08HZaCqAanG7TTaFXnW6xzi928Nj33m44ff/9TXr/4XZ5+/Of5xq/9b6t7IEac1SCKi6cf03Yt0+1tvZCiQWlH0T3do2o1FpUp1qK6FlFCKoBYBAOiSaJISTDdlo+++2eJ3/yEkhMBw2G/B6XY315z1u9Aa0QsSiLKtrTthqLcH/fjZMWKFStWrFjxq4jVUvtLjXdGRFOuLbYl1jmPEiduX33GcPMcYy1Nf4brLyi6I2aYrm+Z54kUPCmMqGVTs+83GNNj255iFEY0oBmGPTevX6BKoihBm4aLiwsQqv3ybs9mUxU5pWpWMFGWohuwOTMdD8xzwFmD6c4oueYvKQUFS9Y0Qa4lOqGJDMNImBNN0zBMntu7I6XU1tfNZoNzDcHPeF8YhyPXLz5ls7ui210Rgmc47tnf3hJ8rIbZJf95Ip9KwKhKgNRSTCRKUzAY01JEIShQmoIQUmScZqy1WAMouVeES6nXkjIPrbiV7/7E1uhXoN/aGU0LOV7KgJRCFPclOZz2WpemXh8CfdtyN3isRDadYesaIKPEoKSQpNRpGykogRjmat0stcSIUnctY4nLfUPMmf3xUC3Xoii24KxFK2H0EaMUF1fbZarFkHIkxkAMlcRt+4aLs45hnuuFg1KYUyL6QJhntAhdU8uRove0fU/rWrreIZLZ7w/4MGONISlDyYKUwPH2htvXr9md7zDGkotU1T1HhEIIHtc0mKbDNo6SCvM8oUzDex99lyfvfVRzsDGjcuCDD95jmD1N02FKLdxSWvB+5Hh3w93ec3nxCGM75vlAv33Eb/63/xM//MP/wGFgafotzHFmGiYK0HbntNtH9b2QM4jQGg3K4Nlgrz5YrhIKSVtEO0AhpkG0XWaMDDZn/Fhfg257iQjcjRPT8YjRhmHO9TXJdXd2mOH1zR39Rnjv/cc/nw+ZFStWrFixYsWKFb+weHdENEZi9IhSDMeJMB9BGS7f+whrO0IU5gjzOBJzqqpazuRYtzUvH19RiieFhJTE/s0LQs403Q7teiiKbb8hDLdMPtB3u6Xh1HF5+YjZ14KUEAIxjjU/qJapE+rv5Pt9VZ8KNWeaY6CkaqGEU0tsqQopihgL+/2RxrRAod902MZS0kO28vb2Br88NqVwdv4e2jrm2aOUJsYIpTykNktBiVqIYcYohTUGaxRaCcbYqoQZhzIW0VWlEm0Wey6M44T3Ab3kKavyqDkZEcpCdh9swHzl+f3k/EstJlrU2sUKbIyuqqiSOlVDnf6IsdqocynoUhjHgXHMnG9brrqWUmr5TWMtIWamMKN1HTaNMZJSYjgukzraLHuwEa0E3TQMwxGjQUQtu6IZURDCjG4sm65jHAbmcVhmViIpqaqISn2crm24ujzj1e2R4zgyTjOTD/UCggjbvqN1DW3jMEpw1mKtoXEW12ga6wg+EZMnpUDO4MPENA28efWCx08vkM0OHzKH40BJgccXZ4zXz4mNY3vxIVkZlIZeGXJKTH4khIESCylGwvWPULrw0Xf/e26Pd9zd3dJvzthdXGKAdpvIwx13+zsa57m7ecnx9jUKw+XVN/jk/Q/ICKqAdQ0xGOI8Mu1v8aIpaUZKolCnYM4uH5OyYzoKJU2EkOjPrtDGMc6eRxcXpCxkMbi2w2qFtYbx7k1VspVi1/e10CollFLElKp6LIq263j29AOub2/5wQ++z3/7c/mUWbFixYoVK1b8aYWU5dfQn9F9rfiTx7vLiMaBw2GmLJbKUjJzLIhtGI4zOdUsoNWKEAPzPFf1T4R2d0ZWBi0G42B/84rb2+cUFKItm6YnpczxuGe4e8UcI6Z1dW4ERYyJFAs5zWitmee57kYae5+hvLu9Y5o8FxcdpShyygsJKsSUCKGS0Zgi8xR49OQZGdhtd5RUiVqMkTevr7FLUdHd3R1a1wzkaUO1a3r6tkFiqFMbS7mQMbLM0tTzVXOTQmMtzmratqFtNV3b4ZyrDbnW1AZbqu2ytuTWrKWIMM9zLU0ShSqKt3OeMS4ttCIPZLKUZa6kohRZLLvc/6xIQah2Vr004p4uGihVKLHUPOPS3BtCnTGxppJKDLWlWIMioaW2BPulNbeS4/IwGWMMuVSCqpUip4hSDq0VjXXVskymda7Wzkphs2nRAl3XIcpgtCxZ02U+SCliiLUlN0RSivW1UELXdcvci9SmW6VrIZFkQpxIWShZUFrom4acTbXs6nrBIMfA4eYV83BAGUf2mbvjzNXlBV3XcfP6BW17QbM9I8R6YWWePSnPCBEpC7kPE36YON7uQQXmaY9SsLs4RxmLNi39RpjGkeM40G8f89FHnzCO1+xv7/DR17KuohDdYJszXB/JfkRlz7QPSMnEnEnjHfuSaTfnjMc7nKsXYrwPPHtyTri+4e5uz/bsApHCMB5RZG5efUlJI7vtjoxGlKF1DcBSMhWgJCBjjObq8VMuHz9jGKc/iY+cFStWrFixYsWKFb9AeIeKqK+FMCXTth0oxTAO3Fxf45yt5EUUzjagDNvWVcIqwuwnjsnXHNrxmvHuJSIZpTtsScTxQIoBZxSh6cgmgTKkAjmlRSmUe7J0apE9tcLmXOde+r4HhHEcIEe0qsdbSaupe54+o5wmS8SHyDDOzNMtMSX6zRUvXrwmhsTZ2RmwKIUl3SuKx+lILIngA36uszJ6aXdVWjCYhRwLSkNjNbvthr51bDYNxjga11bLLRlZnmfJgtZyryqKyH1xUA4Ja81SLlQnYlLMRApaqaWApz7XnOu5qqQTlMpLvrTcb2ciBa2rUjjPnp807OdcN1vnPLPpuiUDWolxYx0UasbRKFqn8QlCSPek1lhHCAElQt93QMLPM6UUznYbQozEGGmdwzWWw90dJUVEMm3T4qxBS81nns6lXlQ7rRWzD/gQUKLuibuztlpJRWG1RolgdM1i1uOP5CQEX23FFPA+AwXXNLRtw+XFBc+ePmbTOFCKnAvbrsEaw+F45MnFGZePbLUJ370khYwowSiLUbWJGGMgZzbvfaeWHTWGvmkpMXF3+4LXLzQXj56htCZnQ9v1y+unKMoSc4tpEmEeyDrC8n5yrkWZnqSFMHiO04AzFqU7uu2WmArjNEPOKKnFWMOw54vPP6Vte0oOHO5u6LYXdK6FnHjy+BnHwzWidG1GPqnUpVAEWt0iQPKBEANMma4/4/JqteauWLFixYoVK/6IWOdbfunxzoio1ZZCphSpmTltuLi44NXzl4zjEaWqzTLOI6ppMOYMZ7copenbhpILsz/yw0//gHC8RonGqIbrL54v9TsZ4xzt+SP67SXDceD2ds+m2+GatsbhlMbHSD71buVMURajFFrqPmbds0yUHMhZyKXuTE7jgNYWoy1aZT77wR/gZ0/KCVUyRWu0NDx5/ISUEqUkxnFgf9zjXFMtskVIJTPP1c5ZciHnSMwKYx3azLjGLI9j2G4amsbRNA1N07Dpe4y1SxHSkucUatFOTJUAqqW3iEoI41KyRFGkVIlmzpCWxldr9b0/Ief01kxLXh7j1Libqp03C6pUguaMxmghhlqYVFIGVVCFpZVYoGS01cyzZ9fvmKeJrrXEkmlsJfcIeFtbdlMGFiU85wg5oIHOWkrONHpDKBk/z4RQ7bTamppzBUJOKG3JuZBywuVAlozuW0QVlFYUI7y8OfLjL18RfUArTde1tM5ijcYokLxYsk1VUGvBltTZn1QbmeskjkHh0Zu+kmttyCkjJZKzx6c6zdM1HfM8knNBkbEiKMmMfqIURdefY12PNm5J8faEEJhDwLUt26tntNvaNptLRgSMbcgpkGKmcQathbNtz3TwJD9S0kwUhzcGWzLHwxtKDpQwYW2LaEsWodgNu7MNh8M1oheLtjJYMmEYyDHh+i2msQyHO4z1oDQiGe16tNI4a1FSXwMpET95hsNdvZiEIiMMg8e5nr2/+RP73FmxYsWKFStWrFjxi4F3RkSHm5cUpXH9hlwgThMoy2Z3zvFYCZlWCkEomWVuYyGFZEJI5BxRtkc1EWcs1rQIdXvT+5nDMDDMI/nFF6RcMLbF7y5qCYxtaFxHjDMh1FkN0YoMi5omaKPR2qK1ISVLihHJGZFlhqNkUkqMYaIUqY29RYgJtDYEH5n9HSFMWGMwWtG3LdPs0drU3KYx98/xVIyrlObs7BzjHCBobek3W1prcM7dz5445+73Sd9GIJBKJuVcm3SLurfUxhhAqlLql1ZbUITgmaaRtrXYXO/zZIcF7m2y9y24OX/l6pHRemnu1SAsCqKQk9w/L5QQU4ASCVq4vdtzed5TELQolK224TwHWgcxQ/YJgQf79jyzaRq01sRSbcGta9BKMY4jxmpyTjTWYLUwTSPzOGK1YdNvEASl9H2mFQr7/Z4vv3xOjnW0paqgikqZajZV6zojrrW+b0A+5WaVUlhnq71ZBL2UX8UQUErTbXbM84ik2uYc5gE/7mnblqbdkIsmlvqamtKQiqLfbInLxYEQqsp8avMdhpFhGKDkqo5bTQiBaZrvVdsYZvbzgTDsycMNyu8pEukefcLF1Xd5c/Oa4Ce6rkFwmM3Z0jotjNevCLevaXcblDbk6BFjlrMhbM8eoZozslZoVRAxCxEt6GJI3rPf3yBlJg03+NsvGG5fUGJA2Y7+8mO6yw9JYnhzc8vTp+/97D9gVqxYseLnjfzTJZSNmr/WXYXlYu9Ph/+pt+j4ek3k+nSV+qcgfe1j+5OF4usd/9fB/pSDWvHLhVUR/aXHOyOin3/6eZ1D2Wxpug2pGMQ1ZApts8VoYRoGGtfgGoe1jpTqtIqPmbDYIK+efrLoK5m+2yCmKq0pR3KKtSlUCiU/FOBMfuS8b5GS8PPIOE41W6mEFAPXL79Ek9nuLmj6Ha5dFMzFYhpDoGRhmIaqkOZaViTKUHIkUth056QipOSJYSb4uZJNag6x7XoUVSHMKS35U0Apuq6lsZazHGsm0bpKnpS+z5vWtt8TwZRFdS33cyrGKELIi512OemSCdFjtCWEwDAMxBhRSpNz5HDck0tLRwdwb42Fh5mWE3LOlFxfg9O/3hNpNcYs8yxgdd3srLcsC3mHyc9QMl1XM69OaZSp8y8+ZLKtbbFBIqetmZwzMQRoW0qp0y/jcUKXgrWWpmmWvdGyZEiXSRNR1XaqFDFFnNZLLlUxTzOvX73CWUvjwKgGazWtcyjJSCmQE2qZvck5E0JAMMt0DqQUKSVhbbVKu8bWCwZL/jimnsvHT/EhcRwGwvGGabhlf/sKpTsePX2Prt8QUsG6DefbHdo4hrs9KcZ7Eg714sB2uyXGyP5wx/x6xOpaaBRzpqRI4xyPnjxDmx1N0yGbDWH/OWm6Ybh9QX9VHz+GQO80WjS2cxjbEFPm5e0rbg837PJ7XD3+Fq9efU4OE9o0WNew6Vvm6JEESiyUatfWRjDW0BiNSOL6ix8xvfkBKh3RFqyxiETi+IqyOcM2j9gPA3c3Nz+vj5kVK1asWLFixYoVv6B4Z0T06be+W2dM5olxHrl69JQoisPxQKHH2I7NzpFiwi9FMjlnpjkyzwmlLNpYjG0QEhAJJaOW/KGY+n3b9iCJFBPR50WtSoTpQBRD9AeCn7B2g5iGvtvinr7P9csf8+rLHxJyro2vtsFaR+MatHYoU5tuc0nkDDEkYqo2VzGOhKII5JR5/fIV8xzZbLZ0mw4QhsMRa12t510UUVEarVqsbdENIKc0pcKYOkeidbXGnnZRgXulMi0Z12UetJLWtxp4S6mTKKVk9vs93tfdVGNtVUTnCW3UfWHTycpbCa96a8plydPmtFh7l3IirZdM6TIzoxVoRYxSVWdZxodV5ZZZFHeHkZQi711dYK0mk3DW4uOMUZrGGnys2UkRhW0aZu8xpto/ERb7dKFpHEog5TrZcmocNsYC1bJsl1mbulFq0UbTNG21FutYp3qMrvMx1AMuuZBVqbM5SiHUmZWTqioUjDJVBVa1XKlpG7a7LajC9/6Xf4vYht/8i3+J9z9+xvHulusXn6KWEictNZubMlAK0zhgXKFpGnBuaTSutmuRWoR0dn5OEYUWUOR6rgXCPHI87NHK0LgOcS3SblCuoc0j4zhCmgiLLXgaR7qup744mqZtee+T3yT5PUVprOt5b7Pl1fMfklNid3aOn25J0wGjXW37NfVCTQCS62hcR9/1mPe+zS2FMLwAEhqDFE/OEb9/Qd9d8ujynGley4pWrFixYsWKFX80rK25v/x4Z0TUNR1NV8uAqtpT0EBzfk5IibvDbVUKqSSPfCKRmpIzucwUZiQrjNZoW7N7OYeqLCYoORHniVISMXoO+yM51Vyg1oocI00r+HmGUuhcQwyehKI7e0ZWDeV4wzwdCJNnHjKHAikXCgrXtIg2CyHu0aYquqIKfh6ASrzOtueELtZsYS7VTiJVWbVOI9S8JSkgolHKoI1aGl0fyOZJAVUiLKua9+RQloKjnEs9BlQtvJGCNm/dTqqFOL01QRNTWHKGCu/DQt40KeV7UlrVzjr5Ultzl58Noba8pgfFriy23RNRdtbeq7ZysgKJMIfINNejzQJZajuwEpBS7bp921KGkRQLojRJhBQi4zxjra4W5JiIsZLx3aZDisP7aVEvE1oMRYTGGYytpUjWukrudW0e7l1Tp4AEtNQPpEJVeUWqbVsbfV9WFGMmBI8xBq00imrnNRq0hlQCIoHWdXz80UeEXGiajlAU7e6cZ1L49Hu/RyGj/czV5RXBz0zzLU56/FxVdkq5nz+xjUOk4ENVpLuuuyf+GeidZttaNn2LMpqSY83Nphn8kecvP4NS0ONIr3s2j55impbZ+3rQShZVvmCaDT5MxJhQSvPso0+AuuE6vvkc4ghprFM/waB0gyiDkOvFGV3P+fl732I+nsM0YIymlIyfp1pqJYW8qNYrVqxYsWLFihV/JKzW3F96vDMiylKCcyrDyTnft7v6ecIoQNXDs0qTc2F/85ocZlLKlBQgR2IMbHbnPP3g42VypDbSplzbaVkUP6UtPnjGySMYBIsg3O4nUvYUZsybPdZa5nlGG0e/6Th//B7DYY8fB2L0hDhT8gzZM+wPlQgYR9Ne0PUXteBoOjBNY218NZZcKpljIYFl2UUVEVIskDzRH8lxxjbnnO0usNpVcikPBPJtK64g6EUJhXqfmofplJILZcmSPsy01PuRAnqZotFak0s979ZavPdoZaqyK9XeqrQCPNaauleqqjoaY+RwPDIcR0qBOWSstfftvKdd0pwzRp+aeB+svjknYgi4y3OMcnUDtO6voE0hhIxRlTDiI7nANASMrkQ4LETZNfreqpxSxGqFaywpCakUgvdYWxXltrF1F7QxqKWjapwmxnEkx4Q2GnJteT3lQJWqkzaVdFZV2lqD976+b3MiaYVoh20djdWU6CkxUFJAWcWmbYje0zQb5v2BLz79Q2Kaubx6zPnFFcfjnmne45q6vSlaL+9/uT+PCOT72RypqrBzpJS4vb5G+USJR7QxWCWIVuwPN5Q4c7bbsnn0IX48Aoqm7dBK4+dAzgVj3lLNFUQ/s797w/nFYw53eyQc6fpzXt4ekThgRdE4hZWEkgx5RiRTikJlhbGGogygMd2OL14+ZzzekWIgR08qQtk8ZnexfXh+K1asWLFixYoVK35l8M6IaCFCYVHnUm0hXYhR1zaoZeuxLLbPcZ7I8cCbL/+QVCIpQdf2dLtLtNMcjnfkXLchT1nGXAqIIiQoWXN29ohcbpmnWFtVVQGppT91GzSRUsFah16IRska61rEONrlF+Z5GhmPd4ifKalaTnNOzNMRYw0pThgji4U2L4qvIaVyT0If8pyJOB3J84AIuI1ZCoyo+UQl98/nREKhkiMW0+2pOKiSE7PYdMtXCoZOP6uUoppLH+7Lx3x/7pUoxmHCObeQxmo11abQb1pYyn5yzozjyOFwYBpnUirEVF/Pnyw4Au4vMpyIaCVwCxHOmcPxyG7bUkgYA8YqxtHXKZqS696rT4yjp20NzpnFulyJU9PYmkXNCUzN3ratY5oTjXU1A9o4rBFKDiip5VEJaJuWzaaHPNbzXKdMKVLbl9VCCJcXYTlXiq7r7kk3ori4vOLjjz+kAJtNh2sUptvS9A0lB25ff0548UNuXz0nhxEEvvxyomlbckm0bS1hIid8nFCmwVj3UI4EP2HHrrusxphqXTeGbntev69qU/Buu2MagZxpXIOKnhRmtIKUPD4klKn5WhD8olSm6GmcIoSJy8tH/Pj3f8Q07PnRj1+iAVUiXaPZ9I5N39E1DZ3RaGMpUqoKKwLU17fftvSdJXrPOB4ZholmKZ0q/GIWYaxYsWLFihUrfrGxWmp/ufHOiGiYj0vuUKBOc6JEkXwil9Oe51KCY4QUAm23o7/8gK7b0vU91rra5lm7bGoba6wk80TMEEGratEUMex2VxgzkVLCWIs1lnGagIA14H21mebi723BqiQw+p40uabDuoYYIzEEgp+XXdBqVtfmbavhYlVFUyikJbcYQt0MtQas64hojGuw/QVojVJyn4tUi521yAMZlSUDeWqjlSJIrnMZVmmKzl8hoG8roidiUxVahUTuZ1OU0RyHmckHYgqklDHasOm7SprtgDWGMEeGYST4REqQE8xTbQc0ZiGJp7Dqw6lYCn8KKQsxg1C4ubvjaucoJEoKGKMQL4SSSL6qsiLUr6tMTBFTNFbruiUbIs5FcgpYY0gloQr4ydM23aLezpRc9zm1NoBClEIyOOs42+3IqeBDYHn71IsVS/uvM7oq0DmDVuSUFuIuIIqUC4fDgSfvPeXpx99EaUvXaIyprbtpOvKD//XfE8MdkkLdko2FrIXjMPDxN76FDwOTH8gs+V7JtTn6NF0j9UKBWt7XuTy8HzabDZIS0zxXWzeFogTRjn57icQjcRwJ00CajkyHN7jz99k9+gixDUUK8zRXC7Bx3N48J/iJJ08u0NZx9v43KUQ+NI7969ek2ZMQxiiMdyPOJMwx0m2Fq3MHJYAklO5Rrufq2SfEaSAGzyZnRh/Z7i7rc9D25/Mhs2LFihUrVqxYseIXFu+MiKZQKCVxUvWETFzycKIUojQimpQSPqRavtNsePxsg9EGUYsFlVrok0vm1OGaUi3ZAantqEaTYmIYjmhtiTHek0ofPPM8VcVtnu7JmyqCNpaYC1obrK4qbSyBME+kHCm5toVa59CnrF7OIJU4sRyPEk3KhbZr63ZoSjjnaumObe7VR1EKuxCnk/p1IiKLofUrquj9g/BQXnQi70qE/Jad9217rhIBBamAKKmTM7HamSuxVQzHiXnJWVrjAEUMhW5r8BJJsZY0gao7pAsJLpSaPy1AKYQYyaXmVsuyWVr/K8vPZKxraduWFPOSfFXkXBBd88B+mUERAecsIVYVUmtTHyaDQmjbBqAq2gjBe5TSOKtJ0TOOA0YKm02HNpu6MxrCksEUXGMRLXgfkQIp19fTOYcWRds29wVOUEuMBMFotSiMge//4Hucv/8ejy8u0cvrlwWkMbz3zT/D4fY5h5sX+PGIz4lvfPMT+kbz5rPfxRhb071uQinN7Ce87XG7p4QMOftawiR1MqZpu2ovJrPZdMQ5EKOq27Eiy5KqYF1HURaNpXhQZkfTW4ruwDTkAjkEQqxWY70xPH32MTlGpvHI3c1z+u1ZnbCJiU4SikLbbZiLxrqGtu1JORP9xOsXX/Lm+Y8IYeY7v/5bqK7ndhywqtR8btvjzreIaWsWt6zW3BUrVqxYsWLFHxFrRvSXHu+MiE5jqurhYsE1ioe2VsD7QIz1F/KUH6x7pdQpFmNre2xJLM2tednxXFpblSLFTCxVDa0Es06J6GWKo1o587JR+tX5k02/IaH48Y9+TN93PL2yGGu4299SSsLo2ip7sq/KUuhzOo7T3IeoqmhKoVo8q/TLYX8gxki72db90pTqLIgyXyWbLMpuKRR5yPHJW3+vz1eIqXzl+z/531fur/7tLYtnIqRK3kPKDFMghFyVODR3+yONC6TSodVCelMilVILg0qmqNqOe5o5Oe2Hnghuplp3TySUUlVepTQhJrSu+csUS70fFEoJwQeUNkBhu91ye3tLznVf1JiaZ40pVjU0RkoqPL66IjctISa6psH03dIgnJfcp1rMrrU8x88j2igQQ86FcZwWAl3fP92m/Uq+9e1j10pTSGy6Fi0wHa4JG4NPEas7XL9FVGZ3ec751Tmkb3P7+jUpZZ69/wHj3Rfc3fyAGEacc4y5vh8Rgc0Tim4R3S07rEsz8jIbdHZ2hve1NMl2DfilRCtnjoc9XdehtSHmQtEOu7sgxUygllMdD3fL6/NgF3/15gVWt7SNJaQJayzONIAmpdPrGTkOtyQxpGCJ84BzDZ9+7z/y4gf/Dps8BcunBc4eP6U5OydpRSmWvr9EbE9Wdski/+y24FasWLFixYoVK1b8cuDdEdF5wvuJtnM01nESRZRSHIcBYx1t2+F9WLYnE/M8431Aa0vTOoyWJV0myy+z9dJIfsuGWkrdpqwZxaqq5VLIi6qVcl7ITAI0bdvSNA0ihpcvr9mPidvhjvk4cHl5xsX5I0QVxmkgxoDWmhTjQ1YQCDFhl53KUwa2FCHgq0KppD7fhdAt7KwSK2tqS6uqMx1vE0j1FqE85Wfhq424DxMrfOW2Nbe5KKPLKMyp2CjngveR2c9kYJg8s49L+RNM3qMQjLbsDzNaCdYoIBGTJ1OVz7iQ+ZyqvfqU+w0pUaloVU/rBQNFHaZRTFPkeBjoLs+W1yejlQHq7I7WZjm/IDFirK0bsSkhWmGtJcYZEYVQCbKfZrq2pWk65mkgzInGWZwzy86qxloLIjx6fIVPmf1xIKeEMfW9VNXlwjBMSCmc7Xb3JH+eZrTWNE1D21icazBa8ejqkqvdDpcjuUzgA9N4R0xzbQS2LWI0prGoDHfHPW3/mIsPf4PDZ/8WCChVEDRitzSXH6HP36+zPSkSY1xs4CAlQ8poIEwTxjlSDCTRS8GSXpqNI3EaCHGisKjTy0WbQlnir/W9mHLGuZ7tZkvjLH62NE2HbTaA4LpdLQHLMyEGVPEkPxOmwqw1jx5dEfffZLh9TkpHrl//gOPdC0x/zuNvfJPN7oqUobF9vS/RqyK6YsWKFStWrPgjY51v+eXHu8uI5kAksT8emKyhtQ13d3vatq9kpnhmPzFP/n47sZSMtQZtTbUfao0sZCml/BYhU4tiBLVcx6C1LHMwDzZXpRuMq7+aKxRaPbS8RgJfvnpJlkrchpiwo+fsQlcLarHs7w64psE40LbmD6VkGlfQxpIKhBiBSsZiyqSSKKlO0ZxtdoTZ11KkEmm7TZ0HUQLqqzbcU1PuyTqgEGIBoc62lKXYqJS4EIuTUqqWrObbim2d5KAIMSXiYrP1IeNDYpwCOac6BVMyIdR2WtGeFCPOGfrWcSL4McZql01VJiwIJauqlKaMT9VyKzzkRsvJT1Gq+om2aGtQJdZMKAorhWNMaG0RQKtMTh4pCqMdMScg0RiHU5rkI85ZnLM0nUMpoWksRvVL+Y7DNQZtCtbVxtm268hodn3HPE+o1jBNnr51DJNfiHviOI3kUrDGYLWhbRumaSQmQaSjbRznj7YoVfD+SNNs6LpzQNhfv+Du1fPFLm2w2y3vffgtRGlyiigR3PYp4exDprtPsabHbq6wV9+gPX9az41AybUNWDL14ocfGA/XUBLGaGSONNoCCUFjreZwuEEUOKUpVBU/p0yhbrPmxQp9uvBRUsaaBmMtqRS0dWQKOUeUNjTdhhgD5AZnMzmOtfwoxzqx5Dq+/Zv/PdfPP+fN8x8xD3cIBRUGbj77AflqBDEovSFksK7FuO7n+lmzYsWKFX8klK/5G2n56UVrVtIf82C+ivQ1/IN3+U9+m3mrmj/xx8w/w6K7H8TLr3W7X/+/fvG1bhe9/+MczooVvzJ4dxnRhZxkMrMPdW7FGm4PeyQLZ2cbjsdblNZo06CVwRqD0hZj2vtm1pSqYnpSqu7nS35CGTy1c9ZcosIYR4gjx8OhZjZty+7s/N62O44TOXqMKIypOcD98cDrN5aLywtCSqCrpXQOCaMUu02LdaYSlGkmLrbdnKst2BiFEQMFjNYcDrfc3rwm+Nqs+8GH30Ipc1/iVB2gP1ky9GANhcWCvChktaV2yQgWQRV1P5NSW3bLPaHNpaCVIsbI7D0hxprxi2+3+i6kZTmX3ntCDMx+QqkzSoqkFGs+Ny9ZTYGcyv3MSM5AUZQTkebUxJvvs7CLybjOkWhNSH4pa4KSM8YqRNtlhzIxjJVsl5QRpYkx0bo6x1KVbVubbZVCUTDOkFRGSChRtE1TCaU15FJ3Th89foRPkXn2ONei9Yht6mt5ej/N81ztyDqy6Rv6jSUET5GIdj3nF+dcXj0iZ2EYPMZ0iAjd+SM254/qxM3siSmidIMSxXD9knn/GnLElIF+9wF29z6TL8SoOFy/rCVPKFI6XSCoz8MaTfAjRlV7tNYNRVl8KrT9jm6zRWvN8XjA9D2Hw4GnT5/WEiZtapZZ1X9HpyuBh8OBeRxpnMUYRYw1Z5tLIeV6YUVpi2hdC8Q25+Q4Mx7uaBpN52rm9OrJB5h2xzQO+OMt8/EOkYnp+iX7mxve/3XF1nyD4/4N2uqfz4fMihUrVqxYseJPL9aM6C893hkRFalWUa2oOcRSm0LNxqBFmH3dpnSup9ud38+SaG1Ryt6TMh0VIcz3JHSeZ5xz5Jw5Hgd2u7Pl8SqR0yQUmXF/wzDt65CJaPxcOB4N2+0WYwzOGj7+4D1yihgptVlXHFdXF8Sl3KZxdrHjKnLKDMOENfDFl59xdn7B7vzinoSqJacXY0SJJqfAfn/LcRhIEWBpg9WOkmuO9JTh/C9lPH/yeznnh63R5balFEIIWLs0xS5cMKdEWnY4Y05M3jN5f59BfPuxTuc2pkgmk2PhOMywbIlW8q7IKXBSZ+vMSLVFn7ZiTwS33udpRKYsBVMRYxSlREqOWGdgCigFxlTS0zhHfdlnQqhK+anMCaDrOmKsKmZKiaQzkCoh1ctkTJzRqkFLnbERrRZ1GPq+v79AQg9ttmiEafaL1fhkic54P9G0BtcYmtbx7L2nXFxd0HY91jVoJQzDEWtNVam1Q2vH5uycrutBNDnWEiYoxJKx3RX28inKbvG3n+LffMnsR4zONO0G6za1LVkJIWei6bg8P2M6XKPJKGqu1GlNjBP7Q6LrekoRUg48ffp0sfZGXKtxTVNV9BRZtobYbTZ0ztb5miWHm3MipogPiRAjTtc5G6ULow844zDNhnE4UPyIolCKYrvpsY2lnF0RQ+buzaek4x05zrx5/jnt9oKbV1/i7GrNXbFixYoVK1b8EbES0V96vDMiWgmFWjYwFRqFEY2yCpGM1i1qs8PZjiSanAWlzbLpmGvOMydimEkxoJTi+fMXHPZ7tKkbkTEmgvdvlfvUdtHD4Ro/3oIsplzdYG2DEmEax8XmqthtW+I8obJH5TqZMdy9oel6iDOSZkr0+DgCBt1subk5YrRlnmZcM+Pa2pSbc81Ollwoqlpax3EihMoMrav7oQ+K4UKw5PTnSdFUS6GSvi9pOuUy1UIeTqQPKnGqG5EgUhbHUSaXzOznpfXXksuptKlmO4EHe5I8aJlaabJEpnmiZCGljDYFrYRSKnGpvFP/xM/KgwIqsjwdQZ0sxFpVmyoZpRW2qPsspwiEVNt3S8r0Xcs+BoytFyf6TUdjNdYatpuWGAOlFMbxyDze4axh03VoLbTOYpYCKa01c4hM00SIgWE4EqJHiVnyuQYlGmdaMomUAlrX7K9IJoRI3/c0jeXs8ozHz55hbIsSiNEzjEf63pHnkW57xm7TE/xIMLqq+yQuHz/h7HJHFo1rem5ePseUkWcffcgwXjDevuBw+4LoR1rX16bfGPAh8vTxxygpgK55Y61IJ2t0rZImxsTFxTnTcCTGiJ8njoc9Ire8/8EnKKeJ2eOnuc7uAKVUC3sK9UKAj5GSoWk3tS25FDZ9R5xHYvR4H2lcR9dtGPevydMdhYLfTxQMqtnSnZ3Rbv4MYTqyv32J93V39+J8x831y5/DJ8yKFStWrFixYsWKX2S8MyKaS7hvqNVi0KLRWkFO5JJIORFTIoSJusEpdc6jKMY5gFFLm6dglGb2gcNhqOpPTOQQ8H5iPN7R9R3OuloydJyIfgBgGo5020t2549o2y0nHbGUQgJinPHzgel4V1VKUcw+cvXoEYfjEcmJkifCdEcqGmdsbeVVFq1MLRPKlVjGFChKYCnjsU3Dhx9/wssvvyCnxOXVOYKQcsSKq4QTWX682lpPQze18ZW3yobyYtdcGk3fmmo5KcEnVRJKJcMCMVcyJQWcMUxLO++pv0ZrTTkV4yiFOt2DTkw+EmOdgNE5oVVGK9AiS2Nw/V4qpb4m6SEnI8usjkipxFMJMWSk5IVQy7KXqRdNNZNVzRV3zpFCqjnQxpFSRCjsdh3Be7Qq7M62GGu5fvNmmQOqrbp9t10mblJVSFNt0G3ahv04MPuJEALjcHd/pHOYyamqskoJSMZYXc8hkHJkd97R73qKVsQcUWhc2/Nou6OUhO53eH+kpImSEvPdG+acyGSMUWjn0HbLmy8+49Vn3yd4j2l7vvHtX+fyg0+4ePyMaX8gzCOzH9BasW06tLHYxhH3L5jHG1rT45odxjnEbSjLPu9wuCPlQvIzmkRrBD/NhOmWRjXo6LHMSKxWamsbtG04zgM5JFCC1S1WGVrrsNZhRPHy1Zd88cPv82u/8edr47JuaNsdf/iD77E7PwNV0EZotcI1Dh9mSjRsr55hrTCOe1y3pdtd/Vw/a1asWLFixYoVf/qwlhX98uOdEdGzs7N7pTIvVarO1DZUH0bmYazzJk2DKqkO34th8on90aOsw5hM5yxG1W3HR0+eLKpjoqTIPFnm8cA8HAl6qhlEq8lJCAE2Z+9xfnGFdR1aW062UhFBcgZlcK6rxDZGUgZtpJIrSiV+KSJoNv15JTq6xeqqbhYKKef6nGLAWIvRhpQy8zhQSuHibMt+f8f+9g27M7005j6olydb7Ml+ep8TzQ+52PudUB7I6en2pyzt21/PuZLDnMDPgWmaKCVXpU8B+cHuq0tlpW/vmaaia2mQMsxzQAkYnTFZsNpgdTWK+hQQVG3UDZUQKqVIpdSKHBGckfspnZRrdU4hE3Odc4kpoUpGEKZpojUGa+vOqCLTbzumcaDkROssVsBqONt2WHXJzc0bBKFxZskSq7cuOGTqJYdMSoEYIzlnrq+vaZqWUiDGWC3hpl3OT33txVSFfnu+o99t6fpauHPac40hcna2q++pFFBEjnc3WKUpSqOlXpQQ0eSiUSnx/PPv84f/4V+jSqaIY//6S37jt/475mlECbRnl1y0PePhuirxN1/A2WOuHn/I3SsY92/wIaCbSC+aGCLDcCTlSNv3teAoBgAuHj/BOU0JA+JHbIlkCkosKUS0FsKwrxnUzTm0DWG+Y7h7g2s2uHbH5aP3alGYGPrtjnkaKUrz+Bu/zuXVU3KKaGuwjWOaPf54qMTftsRw5O7mFXaeafvNz/8DZ8WKFStWrFixYsUvFN4pEQ0hPBCkUhiHgfF4YBxHfAiknOj7HVbVGRTRhra3KDNynAOqQPCZsOxBigLjLEosikLbNfiuZX/Yo5XGNY6UM511WGNBa4y1KKURVcip3Jf/zNO0bCuCsQ0JRfAeZfRih034MBPDjLMtV5dPQTdo2yKlts2mZV5FSiWt3s8kf4dQJ12C9/f24lwiSrGUCmWQDOivEE1ZdlbLkueDr+ZFy080+N3fdvn+vZUXIadq840x4f28lPxkjNYoY+9/JsVaBsV9FlWw1lLKQMqLlVfqHM2pk9g4zTh5QvJorWuxk5KFKGdKLqgCTuuvPMec6+PnEBBh2Zitu6PWGryfOB4Hzrcd274l54BRYLUixUC/6emswWmFEbg82+Gsqk2/1qJEL/uc9fxYa9BimcIerasNeByn+6xxzW9mrNNoLVhrCNEjUshonOu4evIeT569jzMtbbutFxpCpOsbjNGE4Il+pgBNv0WLJoYZHzKu6eh3Z6Ad/njgcPMCIeOM4JxwvP4xL36w4cmz9wkpMNweKfM583BH9AcQGMYb2sv3OX/2bbaXz0jTHj9Hjne3zONQY7ol0zWX5KZlGEaePn0Ci0KdMkiK5OJBGURXtX2cIrbpOezf4FyLaVuO+xfk6Zox9IzzTNvveP9b361t0CHgXN1avXrvCmNbSprre35OiBjOLq5IMZJj5OWrzzkOA0+vnrC9+HpthStWrFixYsWKFV/BqmT+UuOdEVFrDDnPtE3P7CPjNJApxJyIGQoapTRKdM1vqkpYUs6c73qUnnnx8hqlNI1zWGfr1qKcdjULVmua7ZZ+s6kZTQStDUqWGYvsmeYJpaoN9FRGk3MmhISPoZbfkGlcT6Mc2+2WaZ5wpRCVQVtH320pyizENd8TLshVnSVBThhRFCXE6BEB5wza9EzzSAqFpmlqfrH6dxFV7jOULDucJ5SS7wt4qsVZUGLwS2X42y3CJzwQvkSI4b4ldw6BvNzemKrolRO5Ryj5oRSpUC8IGFstso3VizWioGQpH4oRSsYur5loVTO9y3SLUlJba42pEyy5Wn3VYjt2pm59hpjou46QYVpUwRgDPhqcsxjdYLRGdy2qKBrbolU9ryK1TMrpHdM0LU3BmUha9lHrXqi2lnF2XJxd4ufIYb+nbS0hZlQBZQyiFMdppFO1bVgyKOuwtkVrS4q1JbhratZ4LEdySrWIKiXSHNEidLsdzjVw94rxeEOyLcMcMTrzv/zb/we3L35IYxTOaLyfSLnwgz/8A54+e5+u3S4vf2JzcYlzHxASZNHYboMumenVpxyuPyPGmX73BNedM4XCHCLJz1it2JnE/os/YLj+HGMbHn/4HXBbYvJI0yC05Fx1YtP3bB/B4fo5w/GGxiR0jri2oLuCdUIJE1o0SCGmQAwRYyzTsKfkQMwJYxyCQmuLMZYcA+9/49s8Td/g/NEzTLv9uX/erFixYsWKFStWrPjFwjsjosZopqngrMO5nqbteDF/ye9/7/c5O7vi2598F60FcmYcD7VtVqk652Jb+tbRdZZhGJjmGe8Fax3ONrWEKAd89FDKfdYyS/3TOkfJmb5vccoxTZ6cIoiqG5YCKEVKmRjr5uLubEu7TLs0yuDaHkoiBY+qntP7ptq61VkJYoqBEH3Niy45VykGKaWmPkXdN61a6+p9lbLsZp5qfh4surWltxJRUaVaeUUo5cG6eyLTP9m0e8qOpoWIhhApgPdpIa51f7XOs4Cx9Vwore8twUihJKlkw0eMrguhJVciqkRIKS5W3vojSlcCl95qzUVKLSDKmSLC4GdKEbTSGCnEUnDO3hcmNU3DPA5YZxmnmcY1NNbUPdazHTmFWtSkFdM8L4VCDVorGmtIKZFTJsZwv0mLFLRW9F3H/hgoBbq+Z5gmQg51d5VKsIZ5Igs01iKi6BrL+a7n6nzLbtPijGIej0xjzR9TNK5tahnUdoPVjkYbXv7Bf+Tu+f+KT4pP/vz/Di1CTpHXr59jjKOIECj4mMlSX/u744FHbVs3aCnEkAkpY9szEpaSFCV74jzSSiaqQDp+wXh4Rbt5wnZ3zvTm99jffQlhIJcCORCL4rObH3P54Z9l+953ye05RTR62b+d5xnV7rh4v+V484LgD3TthuCPzHFGhjugoenOsE292IOqdvUY0/380tXVOcb2eD8Qo0crg9K1ZCn6TNM8XGBZsWLFihUrVqz4OpBSkK+7+/s17mvFnzzeGRGlQNtsKEVQSjDLVmgIgQ8//JDtdksIM3fX10zjHq11JQY5Mc0BEcV533CxackpVYvpPGNKoKRIzgGhbkmKVhRRZKnKHGRSjhyOI1ppvI8Mw4hrW4wxWOuIMZBzxBhTFaKSEfRCtDKH/V1tF01xaXc1mEXJO6mUKSW8r4rqvWtWauMtudT5EBGaxhFDom3bepN7Avmf2m9Pfyql7seca4lOgVKWc3Qiq+Urx3NCjJFxHLm9O+LDSSGs308p1d1VU2dFgPs21ZyrQvp2TtUYhVaCKLlXok9421bMaZc01z9jzFXxVrWOaAqR0Xv6rkVEEcJEKhpjDMfjUN8jup7jnCIlZ6yzzPOEOd/Qb7cYDV1T24+lZHLwWOfAVmttnjNd19FvNnRdX++rFIZxZBgGbg8HbvYH5pSJBUqO5AIxJ5Sp7z9tDFpr+kbz7Mk5H7x3xZOrszqTIgpEEVMtg3JNgykFZwyGxKsf/y53n/17yJFHH/4Gm4snaG3JRfj1/83/gFEZ1+9QWtesc8kYVdj1W5KfyHGEHJYLIIEYC9tHH4DtKDmjzt8jaYUuE4SEjhNxesXrw0tSnFAxoEoth1K2r7M2Cg4332cmcfb+n0dMV9VQa7HWVRU5BkzT4e9esH/1OZv+HGcaQkrL9E2d8amXWjTz7Ktt2jl2uyva/gJtOtq+ZRwOhHkil8TkZ4bpmlweLpisWLFixYoVK1as+NXAOyOiMQREGbyPpDzTdC0hRP7cb/wFHj9+Rl4ybDElytLXWkptb9VKMFphraLSsURRBZxDjMKHwjSmOn2ipG42lmptNbrmJsVpUq7NrgWFaRpiKbXQJWcaI8sGpKC1oKE+VkwMx/1SclNzlTEmQqgKmrX2rVKgTNO0KGUWdTWRSi3lkQIZ8NPEPI9VzXWOE5G7VyCXv5/uU6l6HrSqeUfhlN1UFPI9SS0/cWXnpIaecqICHA9HjsNESA+E07m2KtGUOsei1FJ4VJb7qY8RQ6yEj+oYFanbp6fioXssDbiUvJQFsdiIdW1lKtXWGnKupDgVrLHkMuB9rMVAi9qcKVhrCVRV1RqD6xoaZ+kai7Ww7TrapkNJIfp5eQ16pnlCvMcai9EPuVQBrLWV1IaIT3VTNYS8zO5kUGB0fT0a5zg727Hd1pIs8rwQRIcxttqztSDK1LxrKRxffs7w5nPubj6rFy36xzz++LuMsyfNd4huOL96VtXOlEEp6sMVyJ6bmwMbKwiKXGqztEaRS2Q83NKfd2jXIRcfE90ZSitU9nz+/X+LiiO22bJ5/F363QXZH/A3X6JLoGiDthbRGr15BEtJlhGhxECRWihlRIFruRunum9rWkyzQ5dC1oqCQYosO7IaWS7MTONQS5XGW7QZQVu0ttiNxVvH65trxmGi7bqf0afKihUrVvzJoYT4U2+zk/Azfcz8NVSbm5x/6m0A/m/73/xat/t/3n7rp97m//KN//lr3deZar/W7X6WeJ7cT73N//n/9H/4Wve1+ezf/DGPZsXPFOuO6C893hkRbV0tsVHGYIohhZkP3n9GSo8Zx1AVNglEarYwc2oZLZQMRtdCopIDEBEl90U/ja37j+MUCD4hRtDGIKLvm2UpUHKdqyhKEEyd80iJRinapge3lBL5mXHYY0wlI0bVjKtIVUC1NiilKTnXyRkf7pVFqO/tLJAESqmEosZAC6I02jq0dVjn0EZQqiCSAUFrW1tbtf6K4igCqjyQzvr/Ow//+6TIngjpiYSmlCBlNIqSBR8KuSS22+29oqtR5BxBqvVXqdq0C4WUhRwKJUWMWQjrYh+uxyUYU5t6ldLLayaoLJSiln3OhZwuKEWQHBmmTEyRXBJIVSVTLqjFMnza79x0XdWCS+Js09A1BWugbztSSPg807SWtu2Y55qZbZsGAWIKaMqyWVpLn7SuGUdlNfOcCV7q+VwufDTO4azGaoXVmtZZnj15yntPHrHterQSYpwpJaFE163PBLQtx8MNz3/wH1DZo0Vj+kdsn36L19c3FO7uLwqUhVxrEUSDiKqlV3GmMZbN7pxSMmHak6YBXEs2LSFlpv1rou7IGVLI5OTZbnouP/5zzOMe0/Qo02O2FygR7O5D/Os/hOLBarI4lPSMh4HRX9eCMGPRsjQxK41yDeePnvL8B6847m9RbrO0TStKqe/9nCOiHZuzKwoZeMnh9jXO3FUS2vY41yCqzjE9uXhM3NZm5RUrVqxYsWLFij8K1vmWX368MyKaYrU9Tn7Az37JJ1ZCJbJYIJVmtz2DPi2/rCdyjsSSyCXijCFHoZpc6z5l1Sirylbtsg2xZAplaYzlnpCFkFDGLZZLhcRMyRGjFdM4MI8DMfmaKywZJKFE0XQblK7WRaVO9tNT5lBQjWOe/f30R051EzWlvBDgB8U0L6TRGLOQ5Qc760kVfbDkns7ekjcVwRhzTzjfzoZ+NU9aSWnOdeZmfziyP07VYtq2S2NsfXyoCqfWBpEHEqt0QTAghWl/JKWMLMrpaXhUWPz6iyKW74lzJXxGPzw/JafnUYko5aS2FoJPlCyM40gIGS11MkdJvQDgjMX7GSHXjKoqNI2pSnljGI4D3gu73fb+eeQs95uj1hn6vkPpWlbUlloJ9erlHfOUoBSMKWy6DmsbrHH1OWlF13dsd2c8ffqUx48v6TqHc6aS9ZQIOVJEIUqzv7tBacU3fv2/QUomhtoiLK7h7nggv3Wx4mR5FqVIKUAcIQQ2/TnB3/HlD34EfqzPV4SiG7rLb+K2F4w+onWhazdkYwl+JqFw/RXN5oKwlDAPQ33Nt/2WH714xfVnv8/Tb3yLpx//Gk2/Y4oJpoHDfk/ftvV+UuDs8hG26Wiacz785M/x6Q/+I68/+33OL64w3SXa9ihtUMrQbHfYtifFueaeVf3XWcSSSiH4iG00GVDOYkmLkrpixYoVK1asWLHiVwnvjIjuDwdiziB6sWs+WD/HacI1jpJrCU8KERbFKJdF3UuLzVNbYpGFYCZmHwkxExMoZRAysWSQ+9ofYoxVfVqsk5RCyZGSI0pgnkbiNFZiVKuOINfMaKV2Qu8uluzliUjUbcuYIiUXQqi/XFeymUi5lgHVG8uDhXaxHb+9F3qaSanEzCznpdwXEUG1656I6k9acU9k9O2yotN95JyZY2I/HMmlTpNYY1Gi7u9fCeSSFuIrD8+D2gQ8DAfUYuM8mYK1PLTtSk4oai6z8Pbz0g/7pKrAkhkVUSAW56qtOYZCinA4DGhj6bqGGBJaaRRSS5wWYldipCQwSrBGSCFwfrFlGme8D1hbW4JzjvdNxnWrtaq9IUU+/ewzXl9fM44Tx+ORtjPstlucbujajqZpsc4iknj86Jz333vE5eUFXdfhrMbZql6GnMilHmfOiTc3zzm/eER/+QylhKZUmTUWT5MLcZ6JwSOnzO1yIUaLoXWB12++oDXC06cfMw8d8+2n+OMN4zSSUySGiSZ+B92cYVpVL9SUgDaCNtV2rMUhGookEgXvPbfhwHR4gcoDrz/7Hn4KnH/wHbrLx2w3W5RUYk0O+CnUfdACIWb2Y+Dpx3+W8dUPuX7+Ke1lpN89RrketViTY5yZhz0hzDitybmw2e7QxuFjRBsLSpPjxOtXPyQO13/8D5QVK1asWLFixa8WVmvuLz3eGRFtmoZWG5Z4IiWXau/Ttck0xERKqZIspSg51QIXcs0WimI4zoQUUVpq1jBFFl6JVhq9FOEUNCmn+zziibiJYiFd1VZIqY9Zkl82LGs2MoQlpyoarS2u6SlZLeU0Qi6C956ccrWeAiCkFBaCCZTagpuW4qDTf5wstItim1NGUChZTK/L7fSiJsJDM+6pROiUy3y7mKiSr7TkMisByiURU8SHyDh5BLnPTNbzslh+l+Ov5HG5fyArRfATucSqIAvVSkp9bkpptFYoqcdXs51vFRYtZ0ZpjSxttMYshFoU1lZLr7WGu6HO6hhrgEzTWDrbkkJkc97y+PIMyZ5S8rJNmejPOu7mW/w8Ya3FNS3TtDQnl6ra5lwvSjjXoI3hbl9bZOd5YtNbnHtUc5JaaK3h7LzjyZNHXF2c07eWTd9ydt5zvtvQOHuf5Uy5qtqkzDwNaGfZbrplgqi+Bwo1d6y0Q2lLbmamaWJ/PKBF2Gw2tP0WC3z6B/+WpjH0TYTjc1oBd/kU9egZfvLc3twwjtfc/Oj/RXfxMefmN1BtfQ1jCPd2Fb1YhYuqanXTGuI04bpzbKFufR5vuXv1Bc2mJ+mWTMIahdWKTd8hJaMlMyXPzes3TOOWs7OPudo8o7EdRcB2BussSpm6mxomSorcHib6vq8zSKLqTq3SIAatHVYJ217/vD9uVqxYsWLFihUrVvyC4Z0R0YuzMxJCFuE4jLUZl8Q4jKQsuKZDW4MzmmILwQem0fPli88Zx5HWtijRZBG67YZN0zEPN+RwS45gVUsRQ7Ya1V3UzJ0IrWsopdQ9yhQQMloU2hpKsaQQKRjyMt+hRNF1srS9LkVCWoNo0sKiSw0Toq1eVNsCKkOqO6SiFEYZKKkW5Vi3FPpkcqoZ05xhGEascRShFv8s8y05lyWPuWRO/wtlBScrriwTKiciCrUNd5wGDsPEYT9wvBtomgZnGmKOpBhRYnHWULtPF4V0sTkrrVDFcHt3AMAaXTc85WFCRmQhoWr5+7IZWon/ySZcyW1B0GhkmVA5lRjV81hVTGNq9nAYZ55cXWJE6GxXp1N6S9/2FBLD4Zp8npmnid12xzxNTPOIdhprNNM8EfxEiAFtFMZYUoZxChwOE/v9wHA4oEqmsZUsbvqO823PxcU5l5dnnG86tApcbCyXl48oMUAsoGueNOUCueCPt5QwEyeNuJYiBX8A3Wxo+jqPUvPDDtsYdNPjNmdEP1WVMwVKLHzy7T9HCndMb77PsP8cKIhWKOXI5oz+8cdcbX+T6XBHTHUCZj4e2J1dYbSpyrIPVUHWGhGF1tVJYJzjW7/xF3nxvd/j1fMfYvsebS15PFJcVY6HoTZGu8Xqi1WEKUIWpuOerjG0/ZYhZpwDXQrzFLA2kkPdzzXa0LUdh7s9h8OBdrNFG0vfnmGbHpTQX77PtLc/j4+YFStWrFixYsWfYqwZ0V9+vDMiejhcI9qhbEPrNHfjHcP+hhAjMRvabke/3dUJDNcjTLx6ec3+do8zCqdhHA7EnHj18jmN3fD0sUOnhCoFSQcKkCKUknBnTyiiMaZOhrStYw6e4AOlKARFzCBaKNqhpSfnRMoRpasyF2Ni9jM5BIxOD4qk1lhjHghiTpjGEVUmhAKlqoWytPeG4KvSSarts0oRQuR4HNltdwvp5F5N1ffZyodJFODe8vr2n/c7nfAV5XWeA/v9kTfXt+wPB4ZxZJpnRCmUrtnLt2deTtZfJZVgUSDnQE6BptEYrer8x73aCUrMvf1WlKBVRr1V2CQiqHurb8181oIkMAKtUWipj901LaMv+BBobEMMke2upW8a2sbQtRZroO16nKmK5qDrLI4xho21jOOIFktKdcs1l8y229C2LdZaSlaAYhgmKMLFZUvbNpydn3N1ecnl+Rm73Rl93yE548cbhsMbzjbnWGdIMZBiri3DSvA+kMKESjPJB/IUmUti0IamPefR+7+O2TxGiaaIXpRRwbjCLIqcIn2rePnZF1xPng8+eIbprpjDgTzeYpQFB6F4rBPGwYN0iMmkPNI0Z7W0q9SSq1Mm+NT8K2RyiuQcCWlm9+wD2vNLdrsNKQbqClBAJBGJxGmgiKC0xY+JEoVNp9Gur0VjMaC1JcbE+bYn+IE4zRgybduQgTCNtI8eIdYSSmYeA4fDLW0c6dotXXeGyEpEV6xYsWLFihUrftXwzoio1YVEpGRNKkLXbTnc3rLbXdBvzhBliSnhpwndtrx+9YZhnNhsdozHPT5US65FcdY1KK3Z9ucouSKGieF4AyWQSyTOA2UY0O2GnB+yj4ra3hpjxoe55huNQ1S1GBplGcY9Pk4Y3S72WkVOiXme8d6z2WyQkiDGZfuxIBpSjkQ/VpurblBSC3xKqbnKECJ+9uTiF8KsmaZxKR7K9wVMOWfsolKWku9tuSfydyKd5i0imXOdT3loz834OXA4HHnz5oaXr25JudA0LYfhiNHCxfn5vX1WLZMtatmZVFqIKZNTWgioXoi14m1TfcqREKql1loDWqFszRsq9FLspJdiomoD1ro+r53TqBKrFquEs13P6ANKCnq5eFBKRulCDAEl1TJacqHtOqzWpPhQ2qSMZut2+MnjxIA0lJIrCXWOUgo3t9cMQ90ofe+9Zzx5esnZ2Tl9v2G73dL2Fudaci4chyNd+z7D65n98Q2P+w9QYvDzRCyC1o4YMuJaBINKAZUjOgfmMDEfXvPZ9/892yff4vzpRxTRiFZIBiWFtmmZp4HpOHFxccY8TezHmbOrb9LsnjDfvWQ6vATdcnb1De7uJsp8xDUNRWqOurjCPE2Irnll7z3WWpamL0RULYxSiiSAcYh1BBFSTmQ/YCXhGoOzFqMVPgR8mFDJ4zCgDKJakhiC90QVkZSYTEHSnvnNp6QyY/vH6O0zIjUv3bY9Z5sdslMkP/L5D/4D3//B71ZruHH8xv/+//gn8rmzYsWKFStWrPhThFXJ/KXGOyOiYTrg+guazQbQbNu+NtI6B0pq5i4bjMm8fvWaL774MblAiAEfEomB1hkohfOLM4yxvLl+g5ZMt9myu3wfpR3zfCSVAsaScyIGT4o135hiJpel7VVBTBGHeciW5oJWddYlxapc1tkWCNTcZSkRrRQp+dqKapdNxRzqvExZtjbloRmWpQQpl0RKgVwyBUHJBSmXmmfVilwEpQ2IqsqUMkt7rCxFRSclNKNFQ0yIpCUHWR5adkWRKcQwYwzsdht8qNZXlrKlEDxKucUqK0tGFvTSADvPI8MwY01Tbbfqq02+pRS0KVirFoIqmGV/U1UpF22qAlmy1ImWUi2cjXPs+gYptZU4F2jahsZpmsYwTTOtNZSUkCI01hFCxHYWSkGLIsX6vGU5rhSWSZ/l+TfGghSMthSp7yPbOh43lqvHO/quQTnF06fvo01HKkImo7RBSmFndyiETfsd/HQkaY1TFisKMQZlG5TdIkvJU8kZUi3AMmEg54TSjs35GSEl/n/s/UnMbnme14l9fv/xnPMM73DHGHPOqsoCioKWELK67UZYZmGpWLCqRavFwpagFxZYCOQFSBTDAiMhg8XCEsKyBAu8cBdCBrEAmepOISNTJUx3dWZlVUZmRGTc6R2e4ZzzH734n/eNRAYisojKW5X5fKVQRNz73Gd+n/t8/9+p7wdEK8J4JIYJrSDFpqRao7Cug5p5+ewDqlj64RG2f4RxPcr2rOxMOByIOZJToNTKFDMqz1jvqLkyHkeCCaxWA9ZoBEErjYjBu55cMiITznvUtlLjyPH2iv3xqrUTW0fnO5RupDOEgK4ZasB2K6qGMAfmEHj5Yse2B1Mz8/UHpN1z9M1HyOopevVo2detSKlM08yjp0+5fu/fcHzx7TvH+QknnHDCCSeccMIJP0Z4bUQU0RSa0ibLPIfrPEpajyy1MB5uuLm9Bgxf/eoXmGMhhkqphSkcKWHCa03OieO4B1XIzBynTEXjrHBzc4sfVjhr0CLUApU75bBtddZKyzZCKy2K00IAaeUq4ttGoghC27V0pqMVEiVijMSYuD0cW5OqNouC10iZ3NV61Tu1ExDBecd0dUupuVUqqZYDjSkCCqVrU2BrhZzR2gG1FeAotWw1gqoKamufbcpoQuUlY5pb3jLX1hzbCp3y/dYn3DXXNlJZa1Ng79VbURwOR16+ekUuGWvdfetwU2eBu2mc2iZdmvrrMMsGadtDVR+zViUY1ZTS9dBxvt1gSmQ+3LaJG6XJOdJ5RykVtzakEDDGNkIlqW2uaiGlgLU9eZnkmaYZ7xZ7chOGF/szeO/w3qOWx2aVZtN7us6ScmxlUSnSD2fkKqQcqKSmympFzYmshO3ZOVo50jyjjEaZgXrfEpzx3rVDjzCjBJy6bHu1oqi6KYAxxPbc5wglo41FrEWXiJI2WSOicH6FMh2+X1MQROpSTKXxqxW+ViqFTCVXzTRNENrBxzQfkQBdb4jHgnOO1F5kjFps6mLIpVKoKGVb+22YyCUxTiNKaWy3QrTF9oY0BpTRaKvJtTD0A953xGnHYT6w3X6OVf8A8oxWkesXv87KWo63FW16Qhg57m7wzvHkJ36WF7/+PzC+ev+3/OPmhBNOOOGzhthP/gr19FN2sSn5dCdy89K38B/D//r//b/9VNf1zl/97Dacf/9/8998qst94w//Xz6z2/xW+nSX+6//z/+7T7zM2//Pf/2prqvET3mj/4EujxM+Y3yGGdGTsvp68NqIaL+5QHdrtPEopbl6dYVIYR5vePXsPY6HAzkJtSrmUuhXK/p+g1+tKBi63DPuX3H14hlKBGM1Xdej5JwQR3a7F1zf3FIqPOnfoeIBe99gemdj/bhkp02Q5DQT8kRFMwwrck4L8RPCPBHnCa2bSqiUbpukpbSspXJc3xxRCL1XbNZDUyxprcCZu93Qdh+Mtviu53A4UADnO+BuxqMpfHcFRNbapT1Xf2zPLXeNs5WaC20FRH08TZMLVQuSMvM8LoVBijlMVApKNKjcgrSywhqFNhqtP/6bc54Dr66uiDminaGku9tpyqlptbFM00QtFbOQ8Oa9VW0TVi+2XOqyt9pIkVLCk0cPWa8HJAaej3tSapncEGeMEqo2jZAmi1FC7w3bjadzBmcM89ws0cYYrLXEFFuLr9Z0XXs+Sykft/kiGKMRFFpB13nEKMQowhzZ7Y9UPbLenqONaqVPi1Jbq5BqmwdSkhhvXqBVpF8/wmyfoLSniBBygFqIVTCi6HyPMu5+PkiAME3k2BqISyl47zHasRtfksKMcw7nO4zfYP2A9StSyZQ4EcMRRWIaD+QUWW3OKWKRWui8Qy2v/1tvPm0tyApy/tjOXcqixJem8jf1uAmT2vV0F29RckCR2z4qZpn1Uay6wpQqypg2+YPgtKbrO3JYk0LEn10ikgjxgI8GMQNVPNRKmPZYXbFK6FeXbH7X/4zr59/9rf64OeGEE0444YQTftRwmm/5HY/XRkRNv25fcJXBGst2veHVRy+5evYB83GHWvKKKSckJW6eX3GFoO2K7cPPAS1TuVqtsMZRauL61SvCdCDkmSyK7cUjzs6eYJxDe4szHqlN+is5M45HUk5oJRyPI9O8J6YZJZp+aJnJw+HY2kNda72teQLRxLQQPdrcSEUxOEdJhcMx0HmLaE2KiZwqObf22+ZaVYuqBf3qjIxBWUO/2t6TxXq3l1paLvQuA3pXVnQ36ZLvcqlwT0DbPqqmmPYTKiGilscIwmq15uZ24mZ/ZLW2XJ5vcN42giaQUqICIQSurm6Y5hllhEpqxUmtJxj9fYVFOEfKabEgy/2ETV2yiYKgpCnQSsHQe87Ozrg4P2PoOqbDDq01KURQmVoKKWdEaUzvKUvZTkozFMd6WJNiwttu2QRtz4s1FhGh7/tGqGtFvG/zIbBYd5tC64xBiyajQBlW2w2iO7rhAtutQCoqJUpNzTKcEr1dEcJEyoV+OOPm+bc4XL9gdX7F6vJN9HCOsQ5Ky2LWnEkpo2pEGYtSipTSovjv6HrLMKzuN2FX6zUl+eYVB3KO5GkkpKUQqkZKPLK/fUkKR0iBMh+wm4eIcWhtmq3W+eWEvZF/Mdw3P6cUyXFGAOcdiJDzUii1TBtVTCt6KlAUbVZIClI1aEFZg7MdRrUZo1zy4iioHEvEWov4M8zlQM6VVARxHeuLdzjsrokk0jSjlWJ1+eSH8plzwgknnHDCCSeccMJvH7w2Itr5npwLKQYO80SMI9Y7fOcJx2bVzSUTU4aaMKqScmluh0WF6zfnIJDnIx++911qzmijcGbF2ZO3ePD4HShyryRWWcp4iiBVLfuhhd3tSw7jAWMsm+0l/bDCuoGUCn1p9ltVMjHNxHhNToLQg7KIEuYwI8rg3JazswFrGxGqVZjnCLVNmmjXdknvimMagRSGfoUoQ62FUjO6aowxy+yG3O+D3pGVlgEVlF6U1pzJJS9KabPugqBEk2orRQoh4boBnTNlTAy9IWW9kI92O7lkSIqYZo7HI/Oyjeq8X2y7FZEKqjXb1pyYQsHodn+VXkhMbRM2dyQZKkbrllstgUrmcH2kTDvScceDy4dsz8+w3YoQnmMQlBacNsQYoUQuzrcc9jdYA9PxyLXS9F2b8FGiGIaecZoa+SqFw+FI33fUXAghgBQ630qjVF0aiamgHevzB2hnyWEmFrBdj/UbqoD2mVozS9oUSkbGI3F/S/Urukdfhhw43H6H+TtXrM/fxJ0/wnQrtLH3WdO6ZHRZNm6d1fSrnjKPzPMr1HpNKUApiDLIkg1OIeE7txxMCFKFGALaKoxdQ8mkeWK++ZCzJ59rG6hLNpdFOS+lUHNCpJJiJqcKopv13DbiXpcssChNpRDDjNSmeO8PO2KM7flWGut7jHFUKrECOaABZxyqE6YwE0PCW9MOK5fm5RgrSYSiPIKhxD2761ftcOGEE0444YQTTjjhB8BpvuV3Pl4bEU0x3Fvoc4yUktsXXNest4WEGMFbjVEdYnowa9YXTxDbU0pmnCZIhaIcj598HtEGZSxd5+lWW4ropuLcTZu0FhlCHDnc3pLSzPXNLSKGs80Fw3rLar1pWb4KIoVhkGY7JWP1lqoLWhuM7pEKr55/yBgOVBG2G0PXrxh6j0il5NTaYyuUWpt9V1gsqu2fUhOCYK1rT0YFpQxa20ZElcI5B8seZCkVay1KK3IpsMyvtM3QO49ChdoKhWqNjNNMjAlj2kyGs5WUMuvBoo2lVrjdH4lLDlNpvdwng3UaJULOSwlSbcHLu2kQLe3+GWOWBt/2T12KmWqtyx4phPmA04C065nm/UJCEtoq+r5nf7tMx9wpwdqTYiSGwHq1au8HMQhtG7Wq2ghRTCxzrljf3tYpRfQy2YOoZs9ddk2N9bh+zfryAYdp5sX3nlFKZnt+xjwfcb7lPmWZL2mWjfaGNdYSjSHHiHI9lI7Voy9T5gOHm+eM+++xvngDOzwgKktGWPkO52zbiS0CNSICN4cdV88/IHx3Yr1ZM/RnWN9hnUdbB3rJ4S6W8qo0/dkjFA/JJUJOSE2kGCg5obUQw0yICdf1eO+XXdmmzNba3mvKqLs3W3uf0N6jJWdEtQmckjKlwuXlIwDmEEix7dOmXChKt0mYFFGl7fEiglZCiXHZpgVnNFMolBKJWIxfY6VQuzWI4/jqg9/Sz5oTTjjhhBNOOOGEE3774bURUSVQqeSY24ahUVBabuztL26hsqiiFeUM/fqSqjpCSoR5JMaILZWYI6W0IpqYAlZpVM2UFFCuFQrdIyfCdOTl8/fZ376iFtCmZ3V2hvMrfDdQUK2ltn68qWmsoYpHAOt7Sq7NXlkS2vaE2x3aQEoTtbi2p5gSKWdyzoSQ0NqhXYc3qrXF1krOiVQivluz9m5JD2qsdWhtUaoplneZUKXa5qZeJjiqlJZ7tG2+hVoIYbrPvcYYmabINM3EGO6fBqOh9wql7PL8tBIlbRoBNtrcK7JKtcmalFKz7JblfqcW2Pedv8+v3pFUaiuFyqUptc55lG5FO5WmqopUas3kXLm9vUZEsepXKNGUDApQtWKspeRCConeduSY8L1fiFXCObeUFE2UUhh6j7MDfd81xTsVSm4Z4laY1A4mtDJoZTjuD1StePToDVCaaR4Zj0dErunWZ0tpk6CWoipRoLwnTZZaWnGPQoHqKP0Z3foBTK847l8i4y3+/G264RxyYh4j2cQlixwoJWC84d0vfolaMjEG5jkTUmKKifPLDmcdKIW1rk36LES6lorEmRgmag4oaeVaKS8WXiPM83x/gGCMpfP+npDGaaLrPbIcFFSkWc1rpZa2r1MrlJzYXV/R9R1amq1apOK7AeVXUCu318/be1/a/QvzkTTeoNNInfeUuCPnABjs+iH27C2kv0S7Fa7rP253PuGEE0444YQTTvi0qPWzK4Y6FUy9Fry+sqLOEUMCu8xtUBCt0NtzSkmEmEkhYbRje3FOqYo5BmI8EsNMSpkSI/F4xe7FB5APKClMyjCqDtWdsX3wFKUNSoR5Gnn5/CNSPDSSUDPWrRnWG4yxiGrXT7rLQUJOaclyKgql7VSWNutSSkFQ+NWWTSykOBLDzMvpGSEcWOJ5LMwFWfYyQ4ikON2XEeWacL5vpTClYq3GGIuxizIp0tpirW15yUXVVIv1Ui+23ThPSG2TMjGCUoV5PnJ9fcvhcCTf2ZqRJUNo6XJuba7Ios62yRij23THfSkShSILGdMKrT3glz1R1SyuQstjNt9xs/BKQWlBKSGnxDRODJ1pzwXtdnOpTNNELi9baQ6KWgrNYXunslVqyYQQKClyOBxwzuOcZw4BrRTG2EUxbnbrnFt77ZwDCEs+V91Pq9ScePHhe6w2a7ZP3sb4gVyEnCdEWaZxIuTKarXCKNs2UWXJ4YrCe0OcSyPMohClsMpQlCGI4uGjd5BcEOvYH4+k0P5srEdqntndvkIpsFajxVOTNKVUG5TSPH36BqL0Yk1f9laXQxIExCis8mhVmceEGI/zXZvOqYJe8r5xnkkpYbSl1KaEpzhjpaDiEZHKNLWmYu97RDSlNqU9Y4hpJM8H9tM1IUSomVIVj95S9NqgbEffD4zLXBFS8d6ja0/Y7Sk5UEulFqAcmF5cs3vxHYYHn6N//FVkdU53+fSH/vlzwgknnHDCCSeccMLrxWsjotM0k2JGa4PWBnJT05R1gMW6ijKRmDO3u1tyKcwxMI4jJaWmSk4Tx9tn1DyiJFOAkjMV4azbYLUllcz+eODVi+cgmVITVQTfbdBuTUFRSkbXjMa0XGpJS0NuIMbEo0dPULUSQrhXSRt9rihl2Jyfk9IKkco8j1DzYlOt7fppVleztMlqfVc8BFKkbYAuBzHO2WUewy4TK0LX92hlUNa2GZFaUVqDagTSiCHm1KZlckLlhEizUt7ud8QcEdrWaSmlWZRzpZaCEuj6DfMcscaRcm4tsapNurTCGw26oqhoo+9Vtlb800ioEkGqXkikINLadZ3vMNph9QonihCO5JqWZl1ZDP5CSolpmuitpZKhNmIeYwQgAbv9EasN2tSmXE4TQ78ik7DW0XUeo4UQIs61XKX3jpSWdmEERFMRDrsbPvrwN9jsV5jVlvVwAVphnaMgGCWUKhz3O2puzcJamgJstCHlGWprTM5k9NJWrHRFF+Ewzmy3ZxwOe/aHW1Z+hShFiYEcjnhrKVIouTCPM6BJCbJSPHj0AJQh50IMAaWFmirG9ijTMpy1gEgjrYigjEOUvS9tKiKUWsg0ApzyTE6VmGaQiALC4RqpEaU10zhxfRxJISKq4s8uGc7fRFvLYR7Z3TxbZoksplsx7nekUlmfPcRagwwrxuOhtTcbh1td0p89wRoLpVl+UzqSwpEw7jjurtl99C22b3yefJJETzjhhBNOOOGEHxDCZ5gR/Wyu5oQfEK/Pmqs6tElLCc9iN1xIXq2JXCrOGfKcKalilUF3Du9WxHkixsBUEjGOKFUW+ygk1fHw0Zso23N9c0Uthf1+j/MdSgvTJFxcrhAaYU0pcpxn+ppJObbpkJwI80wtlYvLc6wVclbLfVusj3cLJSJUrVsWzjlcv2azuYAKMUVSTuTStjqpihQD1g4oZe9znaIcok0jn862nU1rWK3WLXOnLc566tKKq5RG7nJ9d1lNbclxboU2KEIY2e2umOYDpbapjpwTpSRqXX7casZohQJWfYegsMaQa6KUlmVtVkzVblMKlXy/MVpr2zm9K1GiVnJq2U2Arh/ouwGRppw654lhJMy7ppTdZUhRWNvyqkUqkNFLM+80J5qK69oTXiox5SU3C6VmLs7O2nPvLN45lHA/5xJCuCdnojQZwfqBzeUjjLPcXn9ImG4gjYgyeKOYsgIUWrc/h1JUAWcUOUdEMjVHjG4W2ZILpeRFiTQoMYRpJs1HDrsbJEdymlteOeyxIiTpMLplLKngXEffr9DeYZ1vm6FKmgobjoy3zzDG0a8u8est1a7JFaaUKVXw3/863L1Pl3miWis5JmotOGMQMRAPaOdaMVStaO8wNbJPR8K8I46K7snnKcpw/vgJSGH/6hWrdc9qcwmiiGHk9vojjDHEmJZDA2Hl1ksuNZKmiIhBRCNmjXcb+s1D1hczYdy3n//Tp/8JJ5xwwgknnHDCjx1e33yL7VG67VuWWpqlc0GbLUnkXDBGY3TbEy21fdEOyrRGU7vm4Rtf5nh7xe7mOa7r6fwW5Qbm0Gy1YRrp+36ZzhB8NxDmkVcvP8RZsxA3w9XLV1RRbM/PcJ2naI0YhXd+mb0A7z3jOC5f+D8uQVIC2uhmQaQRl1JyU6O0RotqrbEI1naNsJU24aGUxrgO5zt812Nd31Qn6xshKRVtLNpacqooMcumJctGZMvtWQNlIQIhJPaHPYfDsT2fpZJy5K7ISEShtSbOkVgyx8NNKwhSBuc7rDbUFmUFJUvpkSzZWbmfkQEWrXcZa1EK4z+emVHGLSTU3JMk121w3hPmPfM0LZMzCmcthfa8iCqU2m4/50ampmlCi2B8RwwFSJydbeh7j/etLEktCq3Wmt1uRwgz1pj7Q45aoetWrM8v8f2azYNHXB7eZJwmdq/eZ/f8u2i/5uHnfw9zKujapmlEKcRYRCCEIyFkas6Eac/Qd1htmccjNRwxqy3eevrNwDiOGCmgFSVOSLwhvPx1Ysk8+fIfpLo103jg8uJyme0BKZE4pZYH1RpvDfN+hx4/ouaRwxXslEGtHmM3b1D0cN+oe/+mkPaKiFJIUSQRRDKlzGjVoZQjy4DgyfMeVUasVSjpMcqQ5CFq2FLR923NDx+/xXq9oet6DlNGRGGNw2hp5UUpEWMrJ5qnI842JTSnTK6BKoIzjsPcNmy1ahZvvWROTzjhhBNOOOGEE34gnHZEf8fj9bXmFigFigg5B5alyabeLPMm83HZBY2ZORQKiu3FY7Q4lDM4vULrti3anz0kpkwWIZTaLKxxJk874rhr0xiiaQbTjHce3/Xc3u6b8qfNvXKplcFqRdd15JRJeUah0SL0nSUthS+l1IVgVWIIpFzIBazR94phK38p1JLbdIgyVFqZUEgZq6AbBpRWraBIW4xxdL7Hd30jFjRbqTK1TWEs8ymtxbb9FNZaFoKaCSmy3wdSalMu1lqMaeVFOSes6dBacSRxe/2SfGxzKS2L6Bi2Z/h+WJpaE5U29aGUQi/TMOM0Qrvle5J5pxZDU4oFqGVp2a2q5RelUkWhTI+ohFZgtEYp4fZmTw63PH1w0bZHpaJUbfukWhNiwJSWZTSmqagpJaYwUeeKc47VsMJYg/Pu/jnSWpNre+ULlTju2d9+yLR7Thr35BRQklG1kPtHjLcvGc6fME8TLDldqXlRsDMlR3I4sN/fYrSgnFDGHWG+Je1fkFPG+4EiFusGxDrC7gXTq2+Tdt+jDhcUrSkpslqvcVYxTwElglaKKcxQ7+Z3KrZbUcMZNQglRzSQjrccpkq3fYLrzykIRZZN1+UdA4qqK0oLty+fU/IRZwc2Z49AexCH9op4nNHxgKmFLAlrN5huoErGKe5nXbrVFmpm6A3gEK1RulBzZTP01GEghplaMjk0Cz0Cztp2cKQL1qqmti9Nvi2OepJETzjhhN9G+JRxgZv/5U9+4mWs/OP/1Hvz7+C//Jf/m0+8zLv/1W98qusq4/SpLqc6/4mX+er/6Yuf6rr4w5/uYp8G/3j/05/qcu/8X7/5iZcpIXziZYDlhP6E3y6Q0v75rK7rhB8+XhsRzbnlKJ3zTCUsEyeCsQ7fdSgpHA63OF9QqhGSkCJhnHGDa5uXrQ2IikLpDqsKhjZlUuLIzdVHxPkAVRYSpJu7k0IqmTBHHj16Qkplydq19lChFb3IMkFCXay1QCqZlFtxTpssgePhyM3NDXNMGOt4/PAh2pj7ptnWIpvJNZOJzdaYC/vDgdVmtRDFZiXVutljG+FrkxpGN6uokvpxgVC9Uzdl+Xf7u7PUzBxmXry8amRRarONUu9vx+gOamE1rChhRJUMtMdorMFa0+Z1aGqgVq4VAVVBSkEZzTD0TfWs3JcD3RHvlNLyeNp9u3sO69I6W0Uvj2GxtCoAoR965nKk5NII60JsrW3ZXect1mmMUqxWPVo3Zde5fmmIpc2K1GZ3bY91aR6GlqulkmtZbMgWu1qRUodSBjOcc/bkc+jhQTslSUdysa34h4pzFq169rvINM90/YAo00qjdJvRIc+UcGTOI1U8zz58n899/suU3XPi4RVmuODJT/znSHfB9Yffht4y5YEcI9M4teKlrkPZltc1tsN2A0a/DWFPTDNFFFYsWhyFlimutVJibOVcWi17oBBSe20oBacVRlo+dn3+uB00oFG2J4U9aTygdYGUmF/t8GdvkN0GbTVaCcp1hDCRS0FJpKSZnIWSK6HM9P1A5x3T8dA2U0smpUiYBNf1AMvBiG8HDZX798wJJ5xwwgknnHDCCT9eeH2KaAqsVluQyu3tDUbr+4mKaQ5477HdBaqHXAp2zuTdNdMUERsX+25pgydaCCGQcqSkQi6JNO1I8dhyjXVRX4ggQqnc22u9M/ReN96R21wMteKMwphW1KOyUKSS7yynqhGgtLTq5ryUx9DUnpQK0Mht3zuUKEqJTMfD0gbbmmKVVJz1dN2ANQ7ve7zvUKIIYSZT8X0H+m5DNP07+cxS2vGNMYYU8pIXzex2O47HfbP/UkgpknNuzbsiFEkIjRRsNufE+bCQfYV1PVNMjYRqg1DJKVBz24iUWlHaopfJD6XaW+iOiN7dp7tJl1JaplQJlBxJuaBte4x3q6N3RUJD32NZN3K65Drvcqid98QYF8tnxljNZrNhniecsfdZTaUUNzc3rPqepBtpd9ZivGsZXOsw3UDvL9kfzym10olGRFOlcn1InKkD7/3ar3I83HD28C3Wm83SbrvC+Y7V5qwdbFDwXYfUptY3xbtgTfs9pQvna8c03eBWG8z6Z7h8+6tUu6KWgjWVeLzmmI4YbSDOiFvh/RrxPfvDFVcf/mp7/EbTed82OP2KIgYpijnMxOnQpodyJgFKOXRhyRcbirZoo6i5Wau1EXI84LqBWCK5gLI9lEiJE+SIqTOHF+/TP/4ikUoJGdcNSAEthiqCWU5rRBug7ZdqraHkpuGLIFqTc2LeXeM2Zxg/oLRp1uPaysW01T+8D54TTjjhhBNOOOFHAydr7u94vMaMqEVrQ0oRwTDNgf1hohbhwaMLFOBsT8qZqgvYjPUdsqhzzWaaGQ/XHPbXlBIQVRHa/maKAaN0yxrSlJdcKiIa3w9sz85ZDUthjKilJbYipdlNS07keEdiK6VWCi13B4XD4dAUN2PaNEahNb3m2v6bVvBTSqXUTC0Zaw0lRyqZ7WbFFCLn5xethKZfMQwDzntyyiitl9ZcMKYVFGklHxfvyMdZzVoreZk7CSFw9eoVOUe0UeRcmOep3ZecMdaSS0So6FpwziJ6BdJyf9o4VJ7Ii+Kq1GKaXhp6qaURDqMxxjVVc2nRbduo+fuKp5oSm3Mk5kjNgUZTNWiHNo6cJmqVhVyXe0fU3falSMVZi7MWTZuBadbnDLVijaWWxNlmxeFwoHMWqY18R9UmZ7LWWBG0sShlKFIJAlk8xjigzd6IFIwuKAJhvELlQg2JOE30fk1dlN/DOGHdgFJgnCGGCec7YhBYHkOKic52OG+Zp1tUt+Hh2z9BdWuUFCjC5sEb7F98F6ObyqucJ0l7HtMU0BVqmrCqkqfA1fVMwrF98AW2D540RfJ4BUCYHaIEsR7tesRvEN1jtEW5jjSsKSHg/YqQjuxuntGlDc53gCJWh7JbhB6pAVULq1VPOj6nHl+xu37OzWFiOH+bB+98hdoN7ecGlveJ4KwhpthyyyhAI1k3+7guKJGmylvblFip1PDx4cUJJ5xwwgknnHDCCT8+eG1EdJxmdvsDKaXli2hrNS21cHN9w6NHj3DOU+dAKTNSK1o7cp4XG2bm5uaGOO2oJQEgaPrVOVqE5x9+SKp5UQI7hr5lLlebM4xte4mVZkkVKcu/K1YrBEVainMKilzLfVGSLG2km/WA954KXL26QqnFJlxpJHZREAsVakLVCGlqEyiqUqqwWZ9x8fBNjPE4v8I5h7EObSr90KO0+Xj2hWaJLYuNUWuDUrnZXqUu2cXEeAxcXd8AmePxgEjbDVUoKBVywVmzbLXOlLoQXFoJkpK2TSqLwts2MuW+Bwfddj5LLsTSFLBWXNNmXZRtpUFaCwVQJVPiTIlzK/6h5RyVaZuocT5QckKhSWlE0xqBpTbSUnKCkpGiGIaOeZqWHdBmvz3bbnFGKAW2mw1CZbPqiTE2sl4bmc4I1li06bCmR5Sh6Pbap/tJG5AaKdNEno7tQGP3IcfR0aunkDqse4hznpILWtsl2awQ16NX5+g6YUvl9uoFNzevWK3OmcYdvXGEmNCSMKZtgSrfs3r4FnU+UEqk6oqkSgwjRQyIoVs9QvIB0QZjLDkmfLdiPBx48f43KKVZvbXxi73cgDL0Zw/ZPHgDZT2m6+jVY8I0tprzGNFKc9y9IIwdm/UDxK2I0WKHQtf1lJRQJOL1R4RX3+Owf04eM8cE60dv4P1ALmlpcm4HPaIVRmlSaPZnIwJVUbRt7/FujTauuQpEENE4K6SUf8ifPieccMIJJ5xwwu90SP0M51tOiuhrwWvcEZ3al1dp+c3Gr5ryZq2m1oQxgjYdKRsmlci5bUTeKW/DMCDDABRiDPjOobDUUnjj7S9w9eoZoi3bzXkjnouiNc8RbQTfuUZ0aCqe0rLYa4W6zIeAYKwmZ+5v93g4Mk0TFxdNsZzGEa013lucdayGjmmeYZlGMVJb6U9NlJrJpZAoXD7YYmyHcwPWDVjXYaxZyJ0sJT4aY9rLJEpRFwssgNaaEBIxBVKMTNPEs2ffI4TWRHxX1CPSioju8ptKWlOpMQZR3Let3uVP79pnG0FvjcVaK0Qa2dR6ya8u0zEtp9r+vLV2KQmqS/RTQdYUpZGaUbUiJSJ4jLUcK+06AC1tm1SLRi+PW1nDXXK3pNRmW0rh5ub6nhBuNyucs/R9x/F4WNTU9jw2S3PFiKC0xg89tuta0Q7S8qiLlTrXgohiijMoi6qRNF1jO8dxb1nbh5T5gFMd+5AwSpFLayVGObS/xClByGzthnk8YH3P2foCbTpiSBiTqVVxn+/VDjMYSmpNuSpndvsD/XqgVMXZk5/A+8XmnBNpviUnwSvN5dMv0Q2tXbmKoUg7KBkPR1CaOM+UUnE4rOsoBebxwIvrWwZX2KwvKAWq0mjTId0GRUGsRnSipAm3fYi1mt23EuIjen2J67bAQjpzpNa241oXRZ5SUNSWzVbNyuu8R6xd5n7k/nBDaYU5dRWdcMIJJ5xwwgkn/NjhtRHR72+V/f4VWectq5VHkVHaUkolLqqW1i3rmdKigC52S0TQ1oFACgmtDZvLh7hhIEyJlHKrINJCKoVCASJqmb4oBVjaXWstKNV2D6FlP+tyW9Cyj8OwIqXCOM5sNhveePomJcc2QyMsFsSBXOpSFJPbPI1qGctaKtYPrLcP8P1A51f0q761vbrW9ppS+3J/R6iUUhRAc2dbbWSwWZsBhBgj19evOI47NpuWtbyfWqntvnvvl/KldF/1VErCLwRSRNH3a+Z5ai2xCzlUeplGQd0XN2m9tAMv9tt5DqyGNd57YgzklDBGo61FtKKmSM2RGANiI9Y7rDVQyve179Z7MttOAirGaIau43A4YK1qMzHG8vLlSzrfU3Nme7am6yx3BO9+XkY3FVlEMEtWtIoQQ6CWgqiKNwIUiiiKCNgV737ppzje3DBPt3RDx2rdtkpFFsIsiSXK2l5zragYqjIYo3F+w2ZbGKcjYd4TEjDNIBpTO5Rq2eZKIeaMcT1nZw+hCqvLSKUyzwHbDWg7tB8OVRk2D9jdPGM+XIMUQhhJJVHFkis467C+a0qwVuSSCfOEyQarNXiP7zyH4y2rzTn9ZqAWML5Hu1U7XMgTdbG/K7vGDo94e/U2cZ7R3pG0hZxRRuN9R4yR3W6P956S89IwnEBpjPUY1Q4wSKGp3VpBbW3Qd8/fCSeccMIJJ5xwwg+GuqxHfEbXdcIPHa+NiArcMbxGCDR03rNer1A1UUteSGIlp0iKmXk8kNNMKWnJdSqklvtdzhgDMSXG40yV1rbqjadzjjAHcqkYN2CVYIwmplbuU0uh1Iw2CgrotgAKd4MfhbZ9aFqpjQU22y05NTKnRFFyIaWAUlCVWrJ3lTAHNLK4YivTOBMTPDjbsN5eYK1hte7p+w7v3VLSo+iMu89gt4wry4RKU0nbPicfky5R5JzZH3aEOGP0OZv1hsNhT4yRwfcYYwhhhloWhXPJw9bS1NbGV1Fi6LoVOUdCihjbsrAxRZRujcLfX5hUSsZZw9APiGhiDMzzdD/LYowmpQrWNmtvapnZWmKbYAmxZXBre8atNXjfQy0cDzuMqqQUW2FRiCgtGK0Qe5fXPdL1jsPh0J5DBBHIpanPxlhqhZQTc5gxSqGNWSzGiZQCKUykJMRaiCmxWa1556tfI6eZ4zhSc2x50FRYrR2X3pNCoOTcssGRVlqlIvvbF6gSKCmxP+zoup5+2OK6DuM9VDgej3hryXkm50Dbf53Qbt0UyQpGDGJNm+xZ3gmlaobVBTnNiBSUAqUNoFEVYkxLO3ShN+3wIcTAcXeLda20yHnHevUU5z1xDrz/nfd4+/NfYeUGfLei1I4cIyIGSmgHPLZH2X6xOoNV7RCDWul9xxxCyyGnRAiBOUwMXQei8J0GJcScmOeKTgljLSKmvf9OGdETTjjhhBNOOOGEHzu8NiKqaiJXKEXR9R2b7YppbBm2nBpBqLUVEsVQCPPMfNzTe0tKjeiIqNbEGgMpN5XUest2s2YOiaJoX+QLaOPZHY+LoRwkJGptJDQvhCSXQG8dnbXLzIsszaNQ9UL4akUbg0Xa9SgFtVltC6BVI1+1ZJwxZKNbGU9p+T7tzujPz3nw9AtY7/G+x1qLc77ZWEXohzVN4ZzbVEYpFIE7+6tIUz9DmAGaDbdWxmnEGM352VnbLVWKzlusFqyGlCMxNSXQVoPzhpIraEWitOZWEeoynVKklR2JKpQyU2tGS8uOikgjrVrDYoFt8dn2nGql0Armcc/u5kic53ZgUO8mVire9zx69AaHOOK8bm2qqWU7+74jhoj3HiExzSOrfrWQ6ZHVagXAZrOlZLi9vSHGjgcPHizKeUFUm7/JFYw0JbfkjBEF2lAo7SBDLLlGpjChlGIeJxSCdxZRaiH+zdq76leI1sQ4A5XeD0hVHKepbaHubpgONzBfYZSg9Artzyi6w9rWoJxyxmiDVgqKaQcAubC7foFfJZzvEDGY5bUuNbafGRSFBFLxw5aJTIoBSQVrDNZ2aN3s0qUK8zShVNtGu7NbU+Hs7KIpyi+e8+F730Snif3L7yBkiu7p+jUse7ElV8J8oJTUrNAFVIEsoJWQa0QjbDYb4hQJ0xFq5vziAVppYkzEXFFG3R/YlBIJMZJrJab0GZ5mnnDCCSeccMIJPy44ZUR/5+M1zrccEO05P3tI3684HG8YD3vSPCJ8f/YQam0trIfDgYerx5SyB2CaRqZpT5inj1UVJTx9+g6r9YqqWolNDgnnNOEmkGr7Ul5KRS+WwJb9rGgxKG3JtGxbDKGV/Vi70Ey48xG3jKXCOdsGjqUVvFDyso9ZiLERiKoUShyrTYfSHevLJ5xdPGJ7dnlvQ9Vat83OJQ/a7LgdMc7URYWKMaGUavZawNrW+BpTgFqYppEUA4rK4XDbNF0tCJCjoh9W9KtVI/cx4ryjlMw8jwu51EtLbttiTSk0i2UBZVQrxZFKsyzLYmIo1Ao55mVZGKwxeNtRcmYe96R5hpIaUaXdH5F2e6VkvLMM6wFJkQqkGFuhUowYdT/y0pqAtWIYhuXxW+ZpRmuLMZYQArc3O87Oz1GqKbPGCUgrelLWY+yiOtemJoJGOwcpg4pM88R2s2a9WlFLbk3KywFATpXjcWKz3eJdzzHG+8cv0tZK+25Aa2Hct+3Us4vPY7s1c5jYH0e6vqC1QURaXle1WZZa2+2kOBHmI1CXQ4rVYl8VUIpCK6YSrVHGg2g65zns96g837co372v2rxQXtR0teR7W+73vfc+4OWza95+uEFqYf/qGcYPzPsbimi8d6iSKPO+XddU0dYtLc1tv7dQgYhUh6Fw8+z9xd6bePDoKX5Yk6u0Q6cc7jPGSrVSLOM/eSj9hBNOOOGHCTH2U13u//HX//qnuNRnO0/17n/1G594mTJOn+ltlmn+xMt82oRF+Qztj//ko699qsuZ4/UnXqbmT1madzo4PeGEzxSvjYg+efIWogyiLYf9jv1uh9GCVlCrtG3MlICmbuXc9iinccJoxziO5BwppS7lL41QDt0aazqUdhTadEorR6mcrQdudzvIFavbtMtdsc16GLDaLConKGQpmAnElGmFRhpjVLOjVrBGY52mJKHWtq1IUdTaiHRKiXGaEGNRojHi6DZrzh885uzikq7vW6NtY0QYrdHGIAJd10hoSnJfutMIzHLZZTompYDRmnmaubl6Rc2pEZfS8q6lslhk1X1e1FqPtq7lFLH3WU9BloIZhVYWkcxqGDCNu7TnuDQrdJGWhy05Am16pWnIzWpdl5mUfljRdQNKFDEnkFY95KwD2qFAf9ahjCbm2J5/aYryaugxKlOra2JeTmglKKU/JlvSZmScb9nMw2Fkt9uz3a6bJTdnsILrO/r1OShFzgm7KHqFpggP/bAoupXOO6RmREkLECu5V86tVhijyamQckERoUKpiVpV24F1XfsikzNBDGE60nmHt4ZaC7WUZllWlq7rYCl/EkBVyCUxHvccd1dodfjYfq0NojV1aQNOMaOVRkwHKizFW+VeMTemEV5jDCF/fLDTHhf87t/zM9TyNa4++i5iNc46hn7g1avnrM8v0bplf3M4UqR5w0se8P2G4zRhfUc47DikkfOLN9ndvGC8fgnSiCeiGbaX+G6NVgbtBJ3Tva1bL4rv3f064YQTTjjhhBNO+NQ47Yj+jsdrI6LWeJTWzItatur7ZTqkFbgAiDQF5Y6wvvHWhinMTNdX5CLt15VB21Z+UmtltTqHZaqkUtAiVKVI4cD1s2+T5hGURoxDtENpT4ozV7trNttzzi4eUGl2W6UrogzTNJFSJsYAd91Kpd2vlAI1tQKiUhqZE9GkODKOeyrgnGUOkaocq7NLzi4v2W63OOvuLbR3ZTosxAFa9vLuS3orMWqEJedEzi3fepfXPBx2XF+/xHmL1KZK5hQbodYKlEIZi9KKGCMpJkoF0aptiipQQC7gvWU1rIghtNIo1QqJaq3EEO5Jdiv7KeScaGMtjchWMqVAEUVVepmxUSjV8pu1Vqooas143+4TtbXPyqL+5pzQIvTdilIqVhvCfCTFQAwz3vXEkDjb9jjnWpGQbtcdYyCEGe8dRqvWbpsyWhv8akOKgXEesao9N9YYUAIH4erVK3r7ENf3HxM3muVYamK/P9J1Dt+3Dc447nC+Z/ADKbVZG6MUuj+j5MxhGjnbrnBGUXJGiabrB4xRzPPI4bCj7zqs81BkKUPSrIY18zy3Flqagi+1YPQa0Y4kBWs6QIhZMH4FeSaXZo1OOXM8HPDOYq2h1sx+v6PrBqz1KC0cjjuMUVw+fgOphXkeuR0PuFXP7e6W7dk51ndMBwX5QKUwhwmjBO96Qmk251dXezq7Y7w90A8baqnUVNg/f45VDmc9Iok8j+z3V1itUAJVa4zvUbb74X3wnHDCCSeccMIJPxr4DK25JyL6evDaiGgpjdiVUhpZ8N19KVCzO1YaL0so41p+EEVVgnWOzWbbCJAS5jmglWW1ao2talG9cilo49DGYY1ivdrwYvcSURVy16yNJjUVsGR2Ny/wncf69X2Tp7GWToRSDGmeIEfCNHI4TCij2Z6fNWtqahZIEQjTnhiOKElNAUsKwdMPZzx4+ITNZkPXe7QyhCl/bP8tBWPtYu1tMy3GGMoyO3M3m9J+LdL3PSFMlFq5vbkixZmaclMKjZBUQd+VOmmNaEOphZziono2lVUrsEawetkS1dD1jsdPHzEe99TayHKtlRxn5mlimmfCNFPCYv0lt9dPtUxsoaKUpcjdgZUQUqIusy4izTqq1GKBqhVB3bcSO+cwSmO1aQSVineeaTySU8JvPMOwopamklqrsc5hneHVq5eM0x7rzgDbFFTaXuWgLdb2ON8Twsw8j8TjLVLhcHuNM4K3GqHeZ0MBcm5zJISZ97/5P/LVn/kDrLeXTEovszppKX9qirFWGpTmor/EaMh5otSIM3pRKkFryzQeub6+5sHlQ4wxy6QRiGi6rkdEMc9z+3wsiRSP9F2PNf19sZUoWZ6/3NTqWskpkcJMLZnpeCCmhJL2HBi7NBEPfbNl645uUXJjDGzWm5a/LhDCjPYr4uGAlIiqkfHwitX5G1htMXbNm+8MLctsO7RfY5Si5kJIkePNVSs4MsLuxfuQD5ATmoRVYFyHdiciesIJJ5xwwgknnPDjhtdGRGOMrVRlsYs2a2fLJ96xwDulMIRAlUDKhXGcGIaBzg+EMKJN2yN0tltsum0ypdaK1i3TKMpSquL88dukFHjx7D1UGSl5gmwRcUjVIFDijHRrai1L2Upcsp4VrYSPPviAOE3kBKlkNIX12TlVyf1W6Tzvm7WTRC2RaQr0m7d5+9138Z2/t8mGeWz7kYvNtC67n3cTNXe7nPM8txKdecY5d78jCtznPVOKSz60ULI05c0PzfarBKWaFXKeJ7xtczf9sEIZQ+cMm3VPZz21sOQZD9zubnj69On99YsIOc34YYUbZ6ZjIM6B8XiDhJkklbrkKVEaxNyTqloh5gxUuq5bsp+VkpbXvxSogu961usNSrXriTEsDcsFrYSh79jtw7J3qvDdwDSNKK2wztF5z3a75dWrl8yzx7uOKgptLNo6CoJSBqMNxnWUvuP21cx02EMJvPnGY2IILX9b678z3VNSQVJmun3J4dUzhss36FdbUmrtwnnJmNy1EferDa5bISUxja0dVrQG0SAtb7nenFNLZgyBQbdCnzujqiiFH3qKsKjThRRHwrzH6XMQcyfXAlDFtHkUwJoO362bimp7Uji23HOK9z9fdznV6qGKQmmHrorjHBFpEzkox3D2mEkL8+4FqsZmb06RKpViLAqIVLrNhvVmRU6JeZzwWiglMx5uGDZnXD58i+uX30HqjK6VnHZInajz6RjyhBNOOOGEE074AVE/w/mWU/73teA1KqL1/jWvtXI8HFmt1kxzQBtZpoFqa3Elk2Ih1dKshxWUMljrWKn23kmxWUTneSbMM8YY5vmIt4b15gGlQBLNo7e+wjwHrq7ew6o2h+HsGqN70MJ21bM6OyPE2PKCXdcaZGthvH1FToGaA1pa7vTm1UcM6x7X9YQQOB73qPagWlFMEbr1Q778Ez9Ld/aAtu/S2kvvmmeNMai7Tc767+5gAvilzEaJME8z1llKzkwpM00TAhzHluPr+65ZnGuBmlFat8ZSJaR5QmrCG8XF5YbLhw9b6y0ZaxRGaUoG79f03pJjpIaZ9XpNCHG57ICIaU3GMbPf78kfFWZRmJQptZEaRN2XTN15+HvjEANGNPN8AFVJUeNd36ynSuGsw1mHyJJFLaU13epmT1W6lTW1Q4zMNCW0ahZvUYphNbDdnGG0ZZ7D0npsEKUbWTze0vk1xlpUzRx2NyilGdYbNtszIDOKJiPklJZWZIjhSJpmpFSMc3z03W/yxYtLKgqhKeHG6KWZt5FQ228pYhAsvoOSK873zYJNQdUCuk215BJJOaGlYu4yu/Lx619KIYewtEpPlDSi9YCqGlHtfVTv3zJ3/99Id7daETWkFPBL42/OlvF4XFp5E6UEagWtDRnIFcIcoQoKi10/xfVnhP3Lljk2jlwrNUVSLWhrUM5SSytZ8sOGcTpS44xGGFZbpMJq+5Ay7knzDgzUEqn5k4swTjjhhBNOOOGEE0740cJrI6Lbx59n2j3j1Yvv0vk1SgsxzYhUlmlGYPkyTVM3cxZudwc67+7VPieenDIxj4RpxNkOqzVzPBLjgek4cRgPXF4+aQSnwjtf+AmGzZrDzUfM4w1KEk5nxFRKPhJTwftuyas21THEwObiggdPHvP8/W9jtSC6kc15uma1PmOaZgRB+w7ijHYrHr/7k3zla/8Z1nuO84gzFqNNU6oUxFrbF3popHth5yKCEqEultycC1Y3Ja3ERJgmUs7kNFNLJMwTq37AOwMlczzsKDXgS8d2u0UryHVm8Jq+W3G+WfHoYkspgTDNONeBaCKV1dmWPO949b0PSLfPORw7/PoMXXuKcihrsV7T9Qa1KI7X16843t6SQyPpJWdyToT5bj6kWYRDTASBmEaUrhjtub4JdM7TOY3WqpU2VSHXQuc7wnRECUzHCaMNnVuzXq8I88Qw9ExhRmtFCBPOO7zv6PsBYyzWWkS7licGSi4cj7umllNJKdxP0lQNVntW2rXSpQohBErNGLcmF03nDKuLB9QaKfFACHFpV24btZVl4sd2YHp0rVRJKBkYBkWYdxCvKXFCu55SgZLbNEqqhDiyWq+WFl5BqkKL4I1lShllbGvXPe4pNmFch2h3T2iXP9WaiblTdDV+2JKuXxHnA4VMyHsGCzFrqCybuEJR0mzTWqMkUWqzuVNg6NZM04E5B1ypzOPE4fYVxhSUsqzXD3D9wBzaa26twRiL7wthOmKMZp4CYSpszt5G1Ew4XjOPVz/Mj54TTjjhhBNOOOFHAKf5lt/5eG1EtEplCoGYItZGhJaFLKW0IpuyNLTCYmUFKDjn8bajVoWIIueZ/WFPTgmolBKY5pGUmn0z50IaD0iN1FQpNJJ0dvGAh5eX3F4/ZzzeEsOEzgmlYb311KLQuj09tVbM0nj65Z/4XXRGsbv+ACWVFBOqNiKx2gjaOASFc57Pf+WneftLX8P3AzUnXr14zjROaGWARfVcLKZ3edC7Btu729XqrhG4fdmXxf6ZYmQaj4Q4cnNzhZbCdtPjTMs3bjYdKc2sVmtKKhz3LxicxjuLdwpFRNeZcLhB6UrJCqUVXTfgnOH2+kNc3WO0Q6tEnQNFDxR9hrYd/fqMEBJd17F66y0uLy948ewjrl+9Is4zx8OBcTwsr2lGKYMSTSkFa9vcSsozocwo7VDSyni8syiaKuydQ0SYxgNaFM45vHNLljIvpU0ZJWop9amEeabvh3vie2fZtd7jvMN0/b3lWwHOtVInSobWeUWlNQI751hvVs0ua6QVU+VMSRHJI/Pxtr1fl/shuYKyUGA67ll3K6Btw6IEZR06O6bdC9K0x3UzSRm0ceQ5oLUhpUKYI33vaUZnFgeAum/AvX+PlEg8hpbNtI5qLCJ6sQZDkY9txULbyk1hJqaREg9ISmwvnmCcpVSBDJRAJlHsGqPckucVUoiUIjjXcTwk5pjohoGVt+QS7nO8tbSCq9vbWygJAXznKKIZNk+5eOIpJRNCBHqG4ZJK+iF84pxwwgknnHDCCSec8NsJr42ITjfPyfOMcz0FuV/a0lpTaqbUTFsqqaTUNhpFhGHo6X2PiGaajtzcXhPmmZQipRRUgZhmci7EmBAxrLqOOO2WZlqPd46Kpu/WrDcPyDkgkgjzxPr8SWuSFXXfFEstdFajtEfR8cWv/DSvXp7z4qMPqeMVIU4457DDirMLy3b7kCdvvsNq+wBtO7x1iKtcXJYlE9kyojFFvPf3u4p3j//eovt9z9edjfduL3UaR65fPmOaj+x21xhVECsoyThjMM6gdcd6fc5hd+Bwm/G9xxqDUJBaSVnjhwdEMsZ6qmi6fkWNN9R0w2bjkO6CzeYc689Q3ROKVrz/7JoPv/cRh/2RUhKPHj5kNfSsN2tySRxu91hn2W43lNwaXAFub3ateKdWRAlKyaI2j/TG4q3BGc3Q91Ar+/0eay0pFZQIXedx1mCtZp5bJnOaZs4vL7i+fsV+vyflTD+ssNa2TValEK3Q1izTOB+rzgVQ2rJerVs5FI3ElxyYp+NymNFIcdgfoITWMCzck2WtQIki5ZE07RGzQeyam5ffw3ce060RZUEUYhymW1NqBWVIFVzXY60l58I8RyrC4XCk1rLYwltxV0rpfsbn3rqdY7PriiYv7clGW5rEu2iiy3spTkd2N9fE+cA077ASUDlzc/WcTUkcbl6S53YgY1dbzt/+WYrqUVpAKhnafVCG7XpDnEd6bxjcGq2EcToyzxMlBzSVi7MBbRS65PazZzwl7MkYKkI/DIgSYsqk8in320444YQTTjjhhBO+Hycl83c0XhsRPbz4HgWNHwbQGl2blTClhNSIkqZ0gaC9ZZybUhTmQAqB84tLbnc3pJQIIfLwwSXf+/C73OxeLuqRohbBGcfQd8QwIiUjJXO+XZFywJiKsQ7UgGgYxKCVoda0TF7QSnoAa+92R8H6NW997nfz6OkX+bVv/jI319esN1uKsRg38OZbX2S9PUeJblbjEEklYZzHDc1aOc8zKram1/s46JIDLct/C63UqZX76NbMmxIpJ47HPbvdjlpjK44pESFhjWVY9axWG7RuxPLB5ROm4y05TlQqKcPZ5ROGsyckFIPv0dZSaKVJhoc473E6UlMiTTsOt6/Y+g2ue4yxM0Mv6KqZ08TNzTWdf8j5+RnUwjzOxApGWbLKWEfLGorm+vqqqYgFlGrKKK7S9Z7NdsN6vcZ7x363Z5yOlOKBRoKqM63kR2u6TjGOUyPwNfPg8pJnzz+ipMx+t+PRo0dLnrjtf8K9MHn/3+3fbXNzEaeX1ubcrLHSbNne+2anXsTNtnVb2u6nCCnM7J69h8rXmOEJ60c/jRbY37zC5cJ6c8miF6KMxw+KohXTeFhutG3Feido7wnzkWmemecJUa28qCJ455fm5OXaxKJtU+tFa0KObavVZpR2bbIHoeTEYXdNKUuzbxEyrW23UFA5M1+9z/Hq16ipErBYu2X15KugO4wxZGMayawFIxXrLXopT0oANaFVIafctmBRbbJHMiWOkAI1RXR3hrYr4jwyz9eQM85+uvH4E0444YQTTjjhhBN+dPDaiKgyHc47smo5tphTm3ExCkprji05Q9WIVxhjeHB5hlnKdcY44axBquNie8lqWHHY3zLPR8bxwDSPbDdnvPXmWzhroCSs82wuHgJwvH7ebI5GUZUil4pCoUVh3QqlIOUAtWK0QiuIpVAWJUy0wQ9nfPkn/7NmUe3XxCr4bku/2t5vfqKaeuVta8ttX9IrosEqS13UwjuVTomglUItX85DCNRciCmT81LINI3EGFifnaHI5NwynjkLznest5c40xHGG37tO7/Mg0eP+dJP/hTf/fZv0HmPcQMXj94E4xoJcp6+36CtR+m2YWrtinD1DQ6vvksJR6ia5/tb3OVT3n3np9kdE/vdyO3tDZXCat2RwoRWQt/1CJoUI4UCNbUCJaO4uLwgp0KKkVwa4RMFvhvwvkcby2Ecm1rsNIqMNmCNbZdXjpzBuTbfEmPAaM0wrDjbnKOVkGIrjRpWK5QIxizZU1nokVJApZZG7MNSitQ2U5syXVJmmkKz5648WjQpBZDaVEJAA0YrSr5BaUOeIYy39Aj95gLvPPO8o/YbMApRoKoGZSluwNbMcX/LBHTWYbQm5kKpcSmcSoRwxLoOrQzOuTYLQ6FSqKJxyhBjbAptLaSUyHHEd2uscUzTSAwzcdw3JVxp+n5NCEeM9yAwVY3pt8wfzpALuUTe/7f/He96R/fwC1DMUmRVyWlCq3ZgUsvd4i8gYLSjOkvKCdP1FGWpJcEgrfSpQEwJ0ZlcAuQZLRVVv1/7P+GEE074LYR88ufNf/1vvvGprqoT/YmXUa2+8BPxv//wv/hUl6shfooLlU++zA8A0Z/8ON3/8cVnepufBu/9929/qst9Yfzwky90akz9HYlTRvR3Pl4bETXDilJLy8GJoI2ilESplc71aBxhjog0lbHrO1KuS/5zxmghTrfcfPRtbkUQ2+H6gYcPH7NZn2Gs47A/sLu9QSvh8aMHPHzyBuJ7QFBSOR6uERKg76cwpHktQRVUBe0tRrfQnQCmClNo0xVt6iShjCEBtltx/uAJIpoUC9YanLWLPbJQS8sejtNIyQVRCmvd/T5ojPE+C6i1JoSAMR+/RCkFcm5tsc5Zum7NyxcfMs0Tb7z9OXJOaCmUHPnwg19lf/UM8sRH0xXOGX7yp38GJY5cYc4ZqwWtdJsyWayrWjdltBiP1h5jLDEJqgA1M716n7h+wMWDN3h4ccEcHrLb7Tkc9lQ067MzHj5+wvOPvsez770gxYk57Djs95RUePjgDZx3HHMipdgsqyj6rmNYDZRSOIxHhMLxeORss0ESOO+gFJRqkyDGaKzV1KpRypJSxvuOME9sNhtSjIQQ6KxtKnMpzNOEcW07tWVXWza0LEr0FJu92xiDNRatLSEkrq9v6fsOa3ugEVGpBUpinieqMpy/9bswaEJNYD2gyPMtVx/8Cqr+DNuHn6ek1gBNrdSaEaXoOocRsKo17uaaySkgaeb6e99Ga3CP38WsH2Ftm+5JpVLFtAMLxf3kD6WiRZFSYjzsCMu+acltz/Z+E7VWqrKMseC8Yy7CxdPP8/KDX6VOO5yuSNrz8jvf4N3zN4gopGRKGik5UpYiLUqlLBLz/fceEUqtzCHj1md0wwa3iUzj2N7/NSJVkJSoeFAV7fwP50PnhBNOOOGEE0740UGp7Z/P6rpO+KHjtRHRVBKCYK0FKlUUUg21JKaQEC3EqtisNoxzoDOCaChSMM7x8qMPePHhe3SqYLRtKlVKzOnA88MNRnc4N7Beb1ECKRV2+z2XmzOocDtPPPvg26Aq/eoB737+a2Ql9zMlIoIWQQtYre5LlFCalI9oq6AaBjbEHBAp1JJbeVGvWa1XbXIjJVKaqKUuW6mVlBO1VLQx9xuid9lFY8yyW9pUUmtsm6NZGnNjDNSa8d5xc3NFyYn1ZqB3lpwKzz78DlcvP4RywEgBo6ml8uL977A9ewPftSIlpQ3em4V46UZ4l1yh1Aw1Ybpzto+/zNXz71DmEa0yPoxcfe//yxRecvn0p7Bm4OJsw9D1TR0TyPMRlUaO188Ih4kaj5ACOQopJF7cPKOUGZFGxIf1GcMwcH52TolHjBRynqkUrq+vcF1PzhmjFDm3fOs0jRijqdW05lv9scX5bjtVpB0eKK2W4qoEsdw3+gp1sdrq+/mcEEJ73UrBWI3WgkglhLmpjgpiiKR8oPet+EjrAToo1aJLosZMqYkwvSIdPmK8fo/VcAbGk0ptSj+NyCoKQiWnTC0ZUm6Z5N4RdGa+/pAPn32D9aMvoT73u9Hdmqo0srxn7jLDWmmM0lRdyTaTli1aEZjnqT1/S0ZWa4U2jdS2/dZCVive/V3/OfvdS6RGcoSLN78ECKpEtFTmcCSFkblmrFYooZF4pT+eIEKRS8vfutparzEdvlfEeaYUQdD0G0s/bJnGW/LpGPKEE0444YQTTjjhxw6vjYjudrd0fY9ohTEahSyTLQZl22ak7zylQs6Fw3HP0A/0XUetsLu5wWqD61opjTIOEYvzPSmVllGriekYmKaJvu+YUyYWxYNHj3jy9hc4f/SElBKIBeUoKVJJOO9RUolhovWNKpRtJSspZYy1dL6jVIX1jnmeOY6B7eqcBxdtmzOEgAjk1OY9WgNw2yO9Uz3j3PYTWwFPs13GEJccX1lKbDK15paHXMiqiEJrYQ5H+qGjHxy3Lz7k29/6VWpqhFG0BQX9esuwfsijR+9ycflkIb4G0XKv1JYwUqxFu46SayOiaWZOBe8uePDWllomxsMt49X7SA1IihyvP4LujK7bYrRq25W1gtHE8YbBFcpKo4qBbDiUzHG6JYQ9UisxzuxrZb3a4ocV4j2DsxxTYD5GOuOJNNVyd/uKs+2WnA1SISZhtVqhlOJwOGCMQlTBLO22enmM2th71VRZ135dBKs0anmec2454M41C2qKEVUySG1kF0Xf9UtL8kicjo2kKtver8q24iMEjSJLxEqmmIH1o9/Fen3GfLhCuY6yHC6ICLkmSomLdatCTeSaUArGKXPx7k9xOH+I372k5sp3v/VvePzuV6luwNQerZt9u5Y7W3ddiGlTRlGaKjAMPTlGQkyke3aukKXRWJRijJH1xds8fPgFfLdpFm0Rpv0t4+0zjBKM8WgqSlVE6tLCG4kpsjtMhJgwxqJtx+psg+sHSl2yucbiRTPPR0qpWOdRpT1vJZ9ac0844YQTTjjhhB8Qy079Z3ZdJ/zQ8dqI6ObsjHE8UueJQQ/ty6rS7X1QC13f4V3HOE6ICNfX1zhrKbU5Z/N8xGkD/pkcAADwuklEQVRF77uWX1BtuiKX9iVZK4URhXcaazTTNPLq+XNqqczjAe0cZ5cPWK+3jMep7XMi9P3QSN7UrITGWHJtTaattXeF9z3WOFKppFIYx8BqtcX7jhgDFou1hhQD4/FAzk3h1LpNtRht7ndCRSCGQMmNRNwppHC37Qhd37OPkTDPaBFizszTiJChZpQSVhcXvPnFr7YNS1FcPnhANwz06zOUstSU2e9uOO53nJ2ds95uQClQiZIyYTrQaw0kUgikcERLRrRG645aNVvf063WTOOh7Vnarj3PWlONJsXFAkpTOs/PNhgpiMyEdCQkgMLF+UPCccexJpQYzh8+4eLREzbbNZ7KYX/FsNlQ5okYA9N0JKdAjBNarXDOQ4UwhyXWIaSUcN4idikSkqXgSCms7zFdjx9WKPVx66yoZeokJ6iFNO6xxrYDAiWQE6kUVusNxjWLtSFjjbQJHtEUFFXp5ZCgEfEirUCpikH35yjbsqy2pFZuFKfWfgtIbWVEaEGqRZVMDDeIUlT9AHv2Nv7iXUSE85Io1RCroFDU2g4Tcm6EktrKmfxiJzfGEEKiqIqxCu89WRzj8cA0R2q9ew40pUDJwnp9RkIRS2sEFqloMtZ6ChaxhvG4x2qNUoZ+NYBocpVlYgmUdaAdMUa04eNMVhFAI1I4jm2/tWIQ/do+hk444YQTTjjhhBNOeE14bd8Are/wXcfh9obxsMc6R9f3rRhIO4zRhNhskkq12YoYIyll8nwgHV/R2YzUjMLQ5l0URcq/EzqvtRXi9H2HDoGSAjVbpmPE+o6+Xy2Xq9SS6H1PGG/J84QoTRHB2A6jDdM8Y43H+65tSxbIc8AYj7HdfctuSZEwQYgzWjcr7F0rqyzbjs3SqSnU+x3Ru6Kcu4xoO+ipzOMRrRVTjNSaiWFiGg9YBd4Ivfdgeh4/eYrWDq0tSnFfcBRjIIwz1Erf92hl6PwK612bEimFmBLzdEArTQ4jOYwoBTnr5ZQoE1OkVoXv1mjr6YZ1UwOLQhmDVsJ0mLHG8O7nf4JXz56R0we47oKzh4ndMXF9OxGnhDEdujvj81/5CX7qd/8sKSWGocNQ2T58Qpn3XD/7HqkkEMHZpjxr5Ri6AWgW3HEcsdYSY0JpYaF3S2bUUUVhfIf1PUq3LVOlFtJIm8QRpRE02hrG44Tzvu3KSnv/TOMRUe3PHfY71oNHtEGUQYmlolrWtTFRUh6ZxwPeOwRDFYcyarFBq/siq7YT61CqlWDVODK++i63tx9h/UCeE9avSMYjtseYDqmCLu3wINXcMpmlLIcmi7VahFRiI8VUEI1zsHvxPVy/YdAKYwpZ96Ra0UoTYsQ5v2znCrlCyZG6ZGtjzHSrDbUq+tUZUqHkZv3W1kERamqWY+0ciJBTouS8/AwrtGqvDrVZie/yuMacWnNPOOGEE0444YQfDD+qZUXjOPJX/spf4e///b/Pe++9x+XlJX/kj/wR/uJf/Iu89dZbn+o6rq+v+Uf/6B/xi7/4i3z961/n/fffx3vP1772NX7+53+eP/En/sS98PU68dqIaC6CqMpqNZDDTEyBaR9x1oE2zLkpa3ekbBiaamq08Oz5d3BqghLISRaSoBGk0ZDva8WrtbQ90lpxrmX0pjnw9K23OLt8SMqlFb1QUTWSp8zh6hnWCMp4csnEEFHG8s7bn8MYR8mlkcSSmeYZrR3n5w/wXU+MhRxDU1SpeDegtGr5RGT5os99vk+0Wuy2zXKrlGpWS839/YoxMo4jKc6EeWKaR+bpwGZtcUajpGIQ4jiBy9heAIdCU4mt/ZeK7zrWQ0+MmRAnqrQCm5ILpVa01LZNmVMrLjJtPucuRylaKDlQcyvUGY8H+v4MUWrJVWYoBe8d2jj203cR27Hy51yu11wUxfb6wLOPPmIYtnzpyz/Bm2+/RZXCcXdo7blaIUTm3TXzsWUblVKEKRFTKyCapomu65bfa4QmxoA2gtIGUYpcC0a30iGWDyol6l4Rbfo395M5Ugt5OQzoe4+dFTk0ZVSUMB52OKtJ88hH189ZXzzm/PJRU5WRpk62QCq+7xupX6yzIbWzEasVJYPWBuvsYtE2pDyjSuHm1f/Eqw9+GW97rDbUaU8WjUJR0KScWg46TKwvnoCo+53Rtnn7sfgYYrO79v0arQxh9xF13nM43mCtIWfoH3weS9cma7whl8jhcNsyxEpBDqQwsVpvmqqfJvI84dZbstbEmjGmA+MXtTMzhwNpTqzXqzYxUzKltlxunPbtOReDUgalFSGEZo8/4YQTTjjhhBNO+DHHNE38oT/0h/j617/OG2+8wc/93M/xG7/xG/ydv/N3+If/8B/y9a9/nS9+8YufeD1/7a/9Nf7SX/pLiAi/9/f+Xv7AH/gDPH/+nF/6pV/iX/7Lf8k/+Af/gH/8j/8xwzD8EB7VfxivjYiW3Dy2Nzc3lDTjrEXQiPVNbdSqWf5yQVTlbLvCGsNH77+Hmq7RBGpKUEYqbSCyCFA1ikqpGa0NzvSgabukymD8GmU8Yc6EVLDWsVppYmgTKGm+xRqhSqaqgrGG4xg5P3+AaNNuwyhyhuvdjmkunJ21ttfbm2tCjKSlbGi73RJCQClDqRnvLUrp+4xoSq2wqZbSSF/JGGNJNVFzQSvFOB0JYSLEI+PxmnE8Mk7HxYZpMLqpxTc311Qqb7/7OUQsWSoxFRCw5YAqE53eoGrAd45K5njzEcebl4Q50G0e4NZrVIFSM523d1FCjAatBKmKLIZxnkGENO746KP3sf2G9aM3yUWhfI+xmv3NK3RJrLqO9cMHiNL4qgHDg0dP+cIXvoKzljnO1FLYbjbUWlBaKDxgWF1g/Ibjq+fcPP8Ou1Rw2pNKJs4tV6mNpvOW43TEOtMU5lowyizlTotapw2l3Cmg5T4GUGWxxdbIdDiSc8C6DsTQrTtSmJmOtxgqxlrmNCPGUIrw4sPvkGPi4tFbiHEtb1pboZRYS78+I6YI0rZx3aISppLRqh2OKGvxpmd3SEDC9xu0G0jTDVJGYniFHLcMF5/Dbt9AG0cOe9LxGn35lKrM8j5IVFqhkix2b2UcrusQZchF0KtL+oetUdl1fZuKSQooHPdXWLclhhlM5hhuObz6CIkHxsOBHHbE+QZSQeEYHrzNxed+suU8jW9Z2ZqpqpHsaZroy0CKM3E6UOJITTN1PhAOV1Q9sH3yZZJouq477YiecMIJJ5xwwgm/OfyITe/8wi/8Al//+tf5g3/wD/JP/sk/Yb1eA/DX//pf50//6T/NH//jf5x/9s/+2Sdez2q14s/8mT/Dn/yTf5J33333/te/8Y1v8If/8B/mX/yLf8Ev/MIv8Jf/8l/+rXoonwqvjYh2XYczCtleEMOIW5pbS6nENCGl2R1zqZRaKUoRZohhZJqOKOUoaIryVGWoaLRuhTu2q6zWG/r1GbUqxnnk9vo5u9tbVmcd280jRFnGcSalzMXZBmcUJUeyqEZe8kycWkPt5YMnDJttsxIuWcBxHJlDpF+t6YcBalMww9zIQNd1rTjmzoYrQs4ZkYhS+r7FtBUGLSUztSmlRrfm3JIL03hkOh6YxyP73Z6r61eUknn48BJrPNfXN8zzyKrTXFxskTKhjbDfT+g8s3v1HQ4vfx0niblb060ecf74XfAbjvOO7/5PX2ecdzx6+jUef+H3MGy3QLNlskx+NNtyU91AGsHJhefPn3P74hmlaFbbC/zqDKs1cRx5/tFHGAFlDJtVD6IJRRGzol+dk3MmLZupIZVFFatIhq4bWibSmqZilgRpQpXQbMBak3PEGkUtkVoqIbWpm1oqrjf4fkDUYjMtCUpuDbVVNfJfKzUHcprINbedWWvvG4xFFN73zOOOnBNeGzrvmKWircfkwv7Zd5lvXvL43S+jVucskiilFrQ1pBQXy2xr5L3bYsulKYRGR+KUyWFGaYXfvsHbX/uf8/I7/wPT9XfIhxtcEuowYiiUsGM6XGOcXyy1iiItn6pyxSynWsYYlNFtJiYnRBRVOTYP3yGlRAgRJFN2H7B/8W3CfEt3/g7FbplEuLh4RJoPhOMrrLIYf4ZWFi2t8fa4v6W+/+ucv/V5cnGgSpudKRmrNcUYqBXvHJJnSq7EMjJPVxjvOXv8LtUOGGmt2eVH7C+RE0444YQTTjjhhB8UIQT+5t/8mwD8rb/1t+5JKMCf+lN/ir/7d/8u//yf/3P+1b/6V/z+3//7/6PX9ef+3J/79/76V77yFf7qX/2r/PzP/zx/7+/9vR9fIuo7jwbs+owUPDnN5ByXaZPc1FGtWkERbbOwUjh78Jjt2Rk1ZaqAdq6Vo4hGtEGJkOOEd5aUYbc/oLQhzJH15oxHT9+k6I5coKbC8XAkxZHz7ZpaCgUhi6aqnv5sy+rsAdvzh6QUiTG2ttNS2Y8tQ+p8RwitSCiEALRdxzvf9Xq9JoZEKWkhno2wppSWfJwhpbRMtgi+fDxEHeaZeWqW3OtXr3j20YeEMHF+viXFmZfPdmzWHY8uB9adxVmFpBFqpYxXfPiN/xc63WBtR3UDNR2YbyKv4h63fUxnLdYr5jFw+/I9Vhdv0q86jG7bpikkUm2ZW0UrACpVKDVRDgeOr65QuVmY3WInHQ97vvf+d8khLOqkbhlZbUhTIsSAWRRjay1IoRTDcTrS7MMOrdsWZsyFfrPFSit6Ot6+RCGIak2rmkqKEWOE7dATY0KsUIqgjEVZ29phK9QUKLEddlSW12A6oCRRasYafz/hwvL7ogxd1zONN42UIyitGdZrDmmCGIi3L/neN6+4fOen8P0lVXewEGxrLY1et/dEre1QpQL1OPHye99F5ZmSR0SZZjHXmu7sLTYPvoBSipwmUoLd1XNSDvh+zfnDtwnKtSbmHGhLQmbJQwvTNDUrtfp4e9bajmB7htUKUxLp9jlX3/1lTN5jyIy3HZefe5MUMyllHr/1k1y9/JBweNmcBLYVRNVSWHdrYk7cXr2k2wpKOlQF5y1GW3rfbNNSaTutaSSO10xh5PGTL6K6C3L52J5+wgknnHDCCSec8IPiRy0j+ku/9Evc3NzwpS99iZ/92Z/9//v9P/bH/hi/8iu/wi/+4i9+IhH9j+FnfuZnAPjggw9+09fxWeH1lRWZRhDmMLecpVREGawSatGUCtb69gX4TsGqlewcITQ1swCiPc52oFoOc54OzMcdIwXXrbDWsr/dIVU4v3iA7ztyEY6HI7UKw9Bzvu3RUpnSsbXyuhUPHr/Dan2xqJcFkcy8zK3Mc+R4OLJaN/Ww0pputV72OAHvfSOkxpBiuv+9nPMy7SL3Ft078lNr4Xg83ucfY5hJYeZ42POd73ybZx+9z9B3XFysWA2GTeewBqypOKuwpuUlC4HOVgZrSVmgZGytWN0RSiUcrpjGmbMHT/H+nDJUDtPI1Yvvsbm4RK9bqYxzjnmeSHGmlIz3Pdo6VBE+evmSFALKOM7efIwdOsI48f5778EyIRJzZXW2Am2aDVYpYgjYc0PX+TYdk5ftVSUftwXnhFWC6TrmWjCbc0QZ+s1DRDJaCzkGpt0NcRrpFrPt5qxvpT+Ath39ZoP1PUjleNyR8oS1puUqqRgqpTbCrJxDlGCMRSkhzAEkkVNAKCAFhW4Krdashg1TnnESSeMVV7/2r3CrR2ze+Ar4NaWklvGtBREFpbT8qtFQC1YbvHOUeULXQklHCqqpxK6nJEA0xm/x6461VsQU0LYniUNQqLzj+bf+P1QqDz//0+j+fFHXIynODH3XDiVyxrqMtUKaDsT9S66/8yvodE0pkaI6uvUFbnVGVxW5JOaSOHv0BteimXfXGOtb9rpkQHCmvbedVOabjzC6knAUM7BabdGq7bYqEcI0kUtlffkmqjsnF4GSqCKUeiKiJ5xwwg8P6lPkof7mr/+Xn+q6/lc//X/7xMv8H773v/hU1/Vrf/Irn+pypH/zyZf5jF0m+umTT7zM//3L/+1nepufBo/+9af7+6Pm/Ft8T054bfgRm2/55V/+ZQB+3+/7ff/e37/79V/5lV/5T7qdb33rWwA8ffr0P+l6Pgu8NiKqqxDTxOF4izUWZRSCotZMTQGpbXMzxoBRhlIruWbiopQpo1G0TFopQImkFEi5cvnwKTlFwhxw1vL0jUc8vNyC0vRegVjiPJJKpfcWpzXzdCCnQNevefj0LawdmqVxKeDJOaJUU7aOx1viNFL7gRwjRRRKWguuc01N9N7f2zyHVU8pzX5aSmEcRzabTZvwELlXRo+HIyEEcm6kN84H4jxxdXXFN7/5a2y2PW+99RbvvPkG1hSsyhgNxlicH1oOshZKDmjlMG6NqjumOFHCS0qZsO6cWIX1wzfpL99Af/ARgsM6x5tvPmW7GiilFcjMqTWvSm2Kre3P0LZDkkLcwObJQLfasHn0kKura25fXmGUJqTM4TDSb1acPXzQspkVUs4orXHWEeeJFAN5UcSUUstmaiKVSlHtOey6FVoJ3bBqhTylMocZrWB1MXL74jlSA3E6sH7wCL/eMseA0gbrHaLNYo1NUGam2x2iFZuzDZRmfe5XW6zv0MZTa8talhIZxwNpPqD1XbZUYZRQtKI/u6DkmTpmJEckj8z7D7DHc1arFVY5VDWMux25JES1Ki2tHSVDrIVufUYxijgKxkQKLHZ0gEzOmTIWwnSEHLFW0W0NqUS01PazMr8gp5n98y3nb2847m8R63FdTy4V63qUbrljCROSA/nwAlcmCpUkFrt9m4s3vkJFU5ZJoq7bYGzH03fWvPjwPa5fvcQajVmmakTabI8mkW5/g/3tdymlozt/k0F/FXRPRuhXa7x5l5QzRd8dKgWQSi3lvmDshBNOOOGEE0444bcLvvnNb/7Af+bRo0c8fvz4N32b7733HgBvv/32v/f3737929/+9m/6NgD+xt/4GwD83M/93H/S9XwWeG1EdLr6iPe+86tYY3nw8A20GhBpA/dKpJW/kMg5kLKiVBpBqa1FV+ul9AfVSIa0nGWurWHU9x5tAjHN5JSIyxRMyZluGHj0YEMuYJTCaEVQmm5YsVmfwTw3G6JxpAI5NStp3/eIKG5ubpjnmd1uj4+1bY9+3z7lXebz7t9aa1JqCtkHH3yAUoqLi4t7Ympta1BVWpHmmXE8UGolhkCu8PLVFecXD3jn7ae8+87nuTgf0CpRS4KalsKiZm9WZFI8YMyKrl8xBo0VC1UoKTOajF89YXP5NqlWVtsNfnvOervl7OwBcT6S4uF+WgPRVAHnOzZn52RRKAY+95M/S82Zedrz8uVzSsqIaJJU1mdb3vrcO5i+oyKknNtJUykMXY9RmsNu/31NwdKabnOb/whzwA7DfY4WrfHL/5dcwHoqBTtscP0Z8+GK4+4ZuI6zx28x50QIE7UWrBKUVECTQmB/uGW12lALaFHkFKmFNrOy7IDWWhZ7cMCadsDQ6mjv9ncgiebs4RtMuw5le1SdKQiu27SipFrRNHXXO8t4vMHj0XpNldayK4DvekRgf/uKkgJiNFqbZk+vIICWTI57bl5ckWKiv3wHZWoj7qVgpZIO11x959/yjf/x36LXj/k9f+C/aFukd626AiEcifsrdBybqiyVXHsuLt5BaX//nqVUUpyptW37nj18Src+Z397w3S8hZJZDT3YZnMeVpfk2++hOTJffYudFGz/ALt5SNY92q3QOaMWJVruJnZohzwnInrCCSeccMIJJ/ygkFrbdN5ndF3fjz/6R//oD3wdf/7P/3n+wl/4C7/p+7Df7wH+g022q1WbnNztdr/p2/jbf/tv80//6T/l/PycP/tn/+xv+no+K7w2Ivqv/7v/lmk6gMB3taNfXfDWu1/k8uEjcknE0hSoXCqpaKwf0Mq2QqOmHS2W1nKf31QKtuuBWsKyYZiIcUZqxXsLpVJKYjrckMKx7YP6Du9XpPT/Y+9PYm1b7+te7Pf/qjnnqnZxylvzUqwk0jJdP9tx/IA82wEcw1AQuJE0ng3jNQLbUCvp2IAVGY56NmCoZ0BQOmq5oU4SwHCegfjBggFDUEEVlEiKl7c61a7XWrP4in8a39z7iO850KV9rUOKaxAb59x91p57rlkszvGN8R/Doymxv3xKv72kWW44evQOQzGQJrz3qCpnL15wcXGBqtCq3qXf+taTc1WxbjtP74J+5q8XL17w/vvv84UvfIH9fn9XM2NvQ2xyppTEFEdyLrP6l1gfHfPZz32upgAHS6IwjpHWe7purvrQhLWKpkwaevxmwebBW+hcB2O9w/oF3fr+rEwZdvsLVq+9zWq5IY174tRjVHEUVAqaRsR6FKGooBiMWoahp+s6xhg5vzqf034drllwb7EghIYpVlV10S2JeSIXZRonuqZl7Id6fkq+U5HbtqWoMsVI1zaEEDDznKUag4ityqZTrCs1kRZDu17QbY5ZHZ0wxIliPAaLc6USu7vuVkPTtmw2bxOTMmWha1rWxw8ROyfrqtaZ5FJJ/m0FjHUNYi251MTdRbdg2PcMMWLEEdo1KutZ8VwgCinGmm6cE6ICZU8aeqQksB7vOobtJf31BY/ffBvfLRn226oSp4ilpkWnUpiS4puOzf0GRcn7C6Q5AtJcneNYLBb81td/FWzH57/0Y3OtTa3/EQFUamKzr4sDsrGE1WssFvdwm4dVscw9uShCQWVEaSi2oxiHCw3ro1NW6xUlJ5quJeYaAhUWD2jWDxmvPiL4gC/X7D7+kG7/kLR+SHv0OuI6TI6ovrx33aw0/1FLvDvggAMOOOCAAw74fsO/+3f/jp/8yZ9ERPi5n/s5Xn/99Ve9S6+OiO63Z9XiZ2t9iaYBKROXZx+gxhG6Y5rVCW2zQsWw32+rQjX3JSqQSiHGkVISy66jCY5x2BHjVAN2rGO1OqlKaElojlX1KRmhMIw7iia8d7VnEkhxh8Yr8qBYCq31YD0KjEPkw48+YJpG2qZDc2IaBpb3lvP70Fk0k7sgFiOGaYqM08BvfO3XOT46pvEezQUzK7slJcaiUAoOg0Vw3jBOmd3ums+88wabzREvzvdgheVSuD7fzl2lBWsijTOkWNACodsQU2S/j6xO30XFYIxyffUxH339P9SgnlKw7RFf+mN/kWmKxHGPywnEAhlNmUJVKzOGKVZ7srMW7zwxRnIubI7vgRZqR6qlCQ2lKM4GYokM/Y4XT18wjpliPMuVZxyvMNYQnCelOj+7323x3rNermnbpoYSiaB5rj/xbZ0RzglyxszqZBGwxrM4eYOmTMRcyZkxjkIlPmJr52sIFus9xhummJlKxojHSU3h7fc9W1XW6xW5FIxvwNTkXxsCGiNWqhXcCOTUk6eeaaypt9Y5+qsEUvBdR0zVkov15JQxRFKcUBcwXWazXjHtrvjwO9/gtbe/QNNs8D7ijJDGHiFVIRnFWgNFKDlTNLNohJiUZDxtd8z7H52jds2X/9x/y/L4QQ3eSrWL14jDlEhKEesXtOv7YOpMcyq3x7Qq95aJuL9k118iYticvoXp7tVryFk015ocLaBJEWNItmNx70cQtZTxjGl/hiUyXX9IHnoIG1znsC8F5VoNpIKKnad6DzjggAMOOOCAA74HlPnr09rWK8ZtSu5+v/9P/vtutwNgvV5/z9v+2te+xt/8m3+TaZr4F//iX/ATP/ET//k7+inilRFR5xuctRhv8M2Gdz77x1huThnGHd36hGZ5D/ErFEOadmgpte5FE6iSFaSAtxYTHCUndtd9TXQtNeE15cJaBB86xmlArEMpSCmoZpw1dG1HHMdKIsvENFwhOqBliapirTJOkRgT73/nA54/fUbbduQYGfqee8sNOSZSTLRdS0oRZ91cRwJFCillnj17zuXFBY8fPsJZV4lqzrRNw9APd6qo9Z6VOyLGkavzS2wpnKzXtI3jwirrtuXqo9+m5D3L9T1Ey2ylVBSDuIau2ZBKRtSz321RlFImpuGG9bJFcyahhNUpaCGnRE4JK1ITZQtkhSIWKQ5xgfVmQYqFGAecc0xTJKequFXFNzGMPfvtlvXmqCbH2mqHPb845/nzSx48fpMQ0hz2s7gbDI8xoiXjnCWEuT5FhRQTqopv25oQC1hj2N7sa2WOMRRVrEARQ1gs0bGvfZrG4rxHtKbq5jRhTE19Nc7QNpUEi7HElBinCe8DIoZSFDEWHxY0weCtwzpPKZCmhLeBJiy4unxO6S/J056shewX+MV9xnEiqcX7hn6/wydluXpI7M8p6QaTEmko6Gg52SzY7SNn509YHT/AUAVCMUCOaBwZxx5Bcc5XJmcM4zZhQsP9t7+IsWvkaOTdozXN+gFiHM5QF2k0Vhu7c3hrSTFxs+/xrnabxliTqg2Gzif2V0/IY+1OVS1cPvsm60ce1x5TRDBGKLncKf5jTOz2e0waaMKapg14k4lpR1aD6V7DhSWXl1dsNiusEZyRu3lf0ErWDzjggAMOOOCAA75P8Iu/+It87nOf+55+5sGDB/9Fv/O27/ODDz74T/777fffeeed72m7v/d7v8df/at/lYuLC37qp36Kf/AP/sF/0X5+mnhlRDS0glHLevOAtz/7FfzmHrshYdpTbHcf06xRDLHfEfsbdNqTNNb0MxHEWpxxhMUGMY5+v6VpLFMcca5WgyhwfXOF4FgsljRtwzAMiIWSIgYhT4kyWwbj/oK4u0A1sTzZMOVMmrYYkUqoXpwhRSkpM6UB5zxpGolTolssCNbXOdWUyKU+rIcQGMaB58+f0TYd69UaVWUYhlmpLbVn1No6F1qqlfPq8pzri2c8uHdEsEqME11QQtny4uk3ub5+jtJy+ugtChYVy5vvfvalmmw9Wp7g84j3nnEQKB0mWJxriGKxiweoC7TG460hjX1NnVVFradbnWL9AmM9ahxTysQ4MY4jOWeMqbO81WFcK0uKFsahZ7laM0yJYeh59uwZL15ccXT6EOtqTYmq0oRmTpZVrDcMQ08IDlTomu5OLdVS5iAjZRxH+r6nWywxtqYEq8BiuSDmWAPUita02FLwRvHWEowHFNEMRbDeUtQAQtM08zwvpJTqvK4xiJnnRsUyjZE0JUqMbPd7QPDdij7uQQupGJbLx/jVfZJY+n6gbSxhVumX6xOC96SdQdKI5sI47djHAePg6PgRLnTsrs5onKXkiTz2OEl0YU6XLcM881mDvmTy0CyRdsG91x+RxNR0YpWaAn1zTWgsaCDbGjBkvWdhLWnsSWM99nGYaJqW3fUF4+4SsQLS4INDysT28mM29zqKtVAyKUaGYWC32zGVjJaMJTOhXKfEowdvsDheUazl2Ysr9HKPsQZVARVy1ru50IMWesABBxxwwAEH/Ofh05sR/Z8/kXzuc5/jy1/+8qe07U+G21qVX/7lX/5P/vvt93/8x3/8E2/z448/5q/8lb/Cxx9/zE/+5E/yj//xP/4v39FPEa+MiGoSQuPpd1c8ffI+jxZH2MYj6kEs09gzDXvyuCWON8TxhpJGrBi8C5QiTDHhmwVgsMZhjXC6XoEqV1dXxJRouo7l4pjr6y0xF3LJ3Ds9od9tgTInd0KOPePNGV4UuzhhdfyI4jtS6UkxE6fEbruthGgYmGJimuqMoAsdXdsxjQO51AlW52rty263pR/2HB0dMdz0WOvYbrfcXF9jnbsLK4oxYp1jipF+e8X77/02Zfuco7Dm6vkFq/VbdM5z8ezbXF0+xfuAdY4QPJuTR/TjnlImVCzX+x7fwKRKyoAYijqsb+qcoirOtxStdsu4GxA1uNCS84SIwduAXxyhxs3zg7VOJs/kWbUGDd2F+CBVRTS+LiBMmeAD2nV1nnR6RgiBJgSwljY0iNT6mhA8w7Blt7tBNdM0LUbsd83OMlfd9H0/zwZXMuOdxzqPCuQyp/I2gThOGGNAa+eoM9RwJAOglBSxxpFSRky11Qo1CbZoTXRtnCPFnpvdlnEYidNY5yc107QLlkenLI7voyUy9CMpWiYxGAvWmbkjNdYqF1GwHaZ7g+ubG3KO+LBG7EBolITDSoGyBw0smoYooGUCCoaC5omSR4oqxkrtSdWMxh7VNd4EpGSEyIsX7zFsn6PTAg0rfLfCGFvVx6JIKUzTgPee482GlOFizKRiiX0iLATbBKQIcdzT78/w3QmqsO97tjc35Fw9ypZqs8UE1CjvffQRb3/mR2nbI9pOiVM9L8Y4jFBV+JIQqee3KqMHHHDAAQcccMABP7z4i3/xL3J0dMQ3v/lNfuVXfoWvfvWr3/Xv/+pf/SsA/sbf+BufaHsXFxf8tb/21/jmN7/J3/k7f4d//s//+ae9y//FeHUzovuM5hFvIe3OyMMeFie4xhGnnhwjWia0RNK0Z9kETNuCMie7TjjrYbyhjz2hWVI0cH21o/Weo1WHa5YU4xnHhAktapQYE1c3e3Sc6BYNcUxM/TW7q4+xZU/TdWzuv027OiHPD84Te8ahUHRiGgemSRmGxPGJYeyrX/vm5oqUJ5quoQwZt1oR48B+3+N94OHpPZ5/9JTd9Q2gDP1AyplpqIqlDx4UUpz4+KNvcfbkG3zm8QoXz3j+zd9hOvoW9979cxzde4jlT1Y10np8t6Bdr5E9bM8vWTxYzQmpPb5ZU/C0TcvQ35B2iViuiMMNZZpYnG7QceTs7AnGgGjBGMF5T2sd1jkSlhIzcRxQck2gtYpVB7YG4RgxoNXWa4ypqjAZj63bC4GUDIjDuBp80y2XpBjnWpuID2GuWzE472kWHaqVhArMxFcYhgFrDSUXrMl4H3ChhaIYNaRcSU3WjKYeLSMeA04QZzGmzvFWKpswUiCBzGRIVGmswzpLzAnFEVMlv95XO/DJvWPEWTRnShxJ48jY39A0HUYyqMGahNU6SyouYETJKfLiamAflZJnC3BpODItj09P0NKj2w+JAsU3eH9USbLvEOuIcY/kUAOPjMxhRvV8FS2UlIDEcPEe59/6VQyJ3K0pm/tI7mu1T8kE73GhYblYkjBgFGOEdrlhG0eCN7RtR9IJTA2q6vdX+GbB1fVAPw4Y53EOimZK0qqai9QUalGur55hmwVHx0eUqEStXbExZ3LK5HlGWmYF94ADDjjggAMOOOB7wh+xHtEQAn//7/99/uk//af8vb/39/jX//pf3yXl/rN/9s/4tV/7Nf7yX/7L/Kk/9afufuZnf/Zn+dmf/Vl+4id+gp/5mZ+5+/5+v+ev//W/zq//+q/zt/7W3+Jf/st/eReS+v2EV0ZEMZ5hTBQLOZeqiuXCMAyggndVEXOhxeYVmnqQxJQSUTNYRwgdTetQMbStZxxGcqlBRTmBWo8NnilGQDBi6RYL8jSx6AKx3zP1V5w9ew/KyMlmjV+c0m0ekVTZb28IzrHwAdN5Wm/44OwMkYZxTDx48IBxHOE2EMYIIkrTLeh3O3a7HYrSrlfEKSIC/bCj5MzFxSUxThznE5qmwVrLNE1McaAJjoenJ3ib2azXkHacX3wL/Io3fuy/YX3/j0GKxLFnjHVWcPvht9hf7bn31hdJlbqRUwIcw5iJyTAmQzILnBesCTSLFaVkurbFOYNoJqdIzpHtbotf7BgLUDLeQmgaVKrKOF5fM40jhYTzLcYGrHWIFvr9ntW9U3LJbLc7rq6usdYQgsdag3O2WnyHARHBe8/u5oamqWm5IQS8r3/2fc84jijgXJ299b69m1F0OSG5JvCGEEhpJKZCmiZMnvAeYCKljMHjbYe1QqlxR7NCWIgx4axBUErOCJapH7m+uUIBMQ5UCaJcPn9CjDtyipSYMCjWCtO4RYxFKKDVMmytoVs9vOvMnFLE2oCxlVyXnLjej7TXO+4dL7DNBpmeYGMkpZ4iHuyabvUA55dkV9VctFCkzvEa36LWgVpII/vLCxpjSNNEvn7BfndNbNagIKYwNoJfnLA6fp39CC4kfNOyXJ2wXh0zTQOlKOPUozqhKTKlzH4YaLu2dvjOdTspRxJ1MUJzpiiE0NF1S0yJvPjw2ywWR4TNA/b7fbU8S+3NFQMl3w7EHnDAAQf8IeAT2Pj8Pz/9RJv6y//nv/sHvua1//sne8ySX/6tT/Q6/T5NGf/jv/Tff6LX/eqf/3/8ga/54v/nf/hE2/rR/+nbn+h16RO96oAfSNTevU9vW98H+Ef/6B/xb/7Nv+Hf//t/z+c//3n+0l/6S7z33nv8h//wH3jw4AE/93M/912vf/HiBV//+tf5+OOPv+v7//Af/kN+6Zd+qXIp5/i7f/c//Xn18z//8/+13sonwquz5mJQzJzSWm2sprFYX/9e1RtIKLZp2Y17nDUsju9jfMfYj3hnSBoJ3QZVh+qA1QIlI84gxs6VKGV++A0AJOp8Zhwv2V+8D+MVm5MHuO6YxfGbZAIaE94aNO158fxD+t0WqxHvLP1Ya1qmaUJVCaGp5DcmBsrLoKJSCE3D1dUF435kt9vijLLfbfnoow9ou5ZhvCKXjDW1xuQz777DV778Z8j9Ff/xl/5ffPjsOafrI3K/ZXn8EOcbcpzIaWQoE7FkGhRjDKFb4hZryjQyDiOoxTe+nmgfaNsF3v0Ii+WaXDJZhDTeYLY3NaUWA8aDaTk6fUjTrWgUXjz9kLPrCx49eoOj9YaPP3iP3/6V/y8lJ/Atn/ncj9Ft7rPenGKtY7Ne1dTWkpnGSL8f2GyWtG1DyhP7q76uyqjSNA3OGfb7Pev1ms1mQynMdTyGzWbD9fV1JaNauzNv+1dzrkS3KTqv8pS55iUQnGPcV0Llmkowc5zIrpJAkTl1eZrqedYEuYbxaFGmMWNVOV4HklrUBKQU+osXXDz9DsYp1lhAMMaRxTExYoyA1D5UO/fh2mxYnryBmMB6vWQ/5LnGRFBxiIV+PzC0DeuHX+Hm4z0abzBiMc6CEVLuuXjxgqPTxzTre6SYmFJkudjgFxsiBlOEYhbce/ePk/afZbo5Q3fnjLsrhuGSOPbgHOv2IctugwOWTsl5i8lKRigmYNolUoSuXVFKQhCKZtqmo2lqINdut6PveywW2zRoKaRSWG9O6bpQF1b211x89HV2tuXRZ75KWGyIKQO1TkcAa2un6gEHHHDAAQcccMAPO9q25d/+23/Lz/zMz/ALv/AL/OIv/iKnp6f87b/9t/kn/+Sf8Oabb36i7VxcXADVWfgLv/AL/39f90NLRL1rGMZCP0Yuzs85vbrg5K2HGOeZxoio1qCaXFDbcvzoCGcdKpZiDMumQBnZ3zzHInijiDNkDFYKzhqcC2z7sc4ShgZjXSWO1jP15/Q3z8njJV0bCO2Go4fv0C1PSQreOoomnj17yu7mOU4t906OOb8aGV/c4L2j3+9x6xXjOBJCZN/vK68W8CGgKNYFppj58L33ub65pnn3M/TjnlQiHz89w0hmuWx5+OA+n/vcm3z5Kz+GCx1+s+Yrf+p/xTd+4z8QVvd56+QdHr3zJbb9CJpJKogEGg/OeFQsONAS8Qb2pTBOE6rgrKdbLGi7JcO453q/JTiHcQFLQ9euGIYtIopzDUenr2OaVe14TBPGWF57/Dqr9ZpcFNWMNRHJCWcCedxCOapqrumY4sQYJ8QIu92ecRjYHJ0gQL/fIWIRM89jllxVw8USMZYpRpaLFaiZbbiW9XrNOI41HGccqqKa0jxbOzHdJJbLJf1+h7EeMVrPnzXoeMN+6EmxZ318QnBHxFQrULKCMVLP5dSjUrtSUcVQ62pSHLB+QdOtuLm65uzph5ipx5gWRDDiQGtIkxiYYiSp4kODCjhjsChoXbzYrDrWC6k9qzkTp0jjF7VTsxQm19Hd+yK7J79GQ0KkKqsYR0oTlxdPOelOydLgmg7vGjTVJGJiwhiH+o6wbnCLDZpep0mJozJw/fHXsdqzWJ6SUqaUa1THek5LQhrBesGYFWotRUE0o7kmJMecYJoI3rFcLLDGkHINECu5YBdLmqYDUy27vt2w2jyi9Gc8/eA3OH79i9h2RSqC5Fwrb6yrs8YHHHDAAQcccMAB3wNE69enta3vF3Rdx0//9E/z0z/903/ga3/qp36Kn/qpn/pffP/nf/7nXznJ/CR4ZUQ0xYyRBsQw9ont+RnrByM2SE3Aqk/fuOAJbYeYOoeIyDyX1pNLhByZxoH9VNUo7xyXN1f4dmJJYIqF0CxxPtRAm5TxBobdObG/wriO9b032Dx6F3ELxhjrLFvJOGM5PX3MZr1CEB4az6O3Ps8HHzzhO+9/RN8P5DKy3yd2+xuGYaBpGpIqbbdijBOII42Jm/MLdv0lT556jDG89uZrvGVfZ7nwtI1h0TW0rSXlCU9LKvDgjXcB2N1c8Oidd8k4cpmYpgiSKVkRFMqWoQjiAyVGjA2oVmU2ZaHXxH4cCc5ycnxEjglraxCP84HjR28x9PtqdQ0NYhcgBqMTicTDx2+gJTPFHZTM6b1jvvLVv8THH3yHy8vnXJ0/Z3H8ANU6QzqlBMbR7/acnZ1VgjVFtjdblsslzoFqwQVf+zhjTW2tYU2ZkDLemkoQ+z0lNyyXKy4vLtleXeGsY7XZkHIhuEDUmuSKc7UTUwplGmr9SY6gPcRLrp9eErpTzOIelIG467HeQvRINhSraM6YuboEAz4X8vYFGSEsNrz++S+zvzpnHEesq4m+lNsKG0Ws4gSMtahkigC2LoKIAikSvKdtu2oLVmD+WahKbemOMKvXydsPcSWDQFHHo3f+ONM4suszN/uetYfzF7/D7uIJrm3IUVjcf4vHP/pnUDEUBHUNajpEjunuJYaz30SnKzAO27WzfdjV0CEjpJIo00RoO6JWS3KZa4xSrjVAmgtGam2OtQ6smwOsag2XMxZjqhK/ufc6T771lNi/x7g757XP/0nC6iEpUWdkRWpH6gEHHHDAAQcccMABP1R4ZUTUULsrvfN063uY9RGTJlweMJgaVDNFrDWIqw/u1jhKUeI4kuNAjnuMFUqxNG3HOI6Y9pj76wcgDUkarE0YMfjgieOI0cg4XbPbX7E5us/9+2/RrE7I1mG4fSBWxjgyFDDG0rQnhKatqaxuIOZIzAPf/r33URUuLl4QQlvJSInYrqUfbjBGOFov+dKf+BNsb6742td+maYNHB0fsVi0c29mwVuh8Q0onD19wttvL0GVfX9F0y45Pn1Mlvqg3y03LFZrtttLXDBoSZy/eIGqUEq1ruY8smg7SoFxTKSilFwoBlKqNuApxhraI0IWR7s6xdlATJmsBSuw215gyo7Y79EUMcQ6aymOpmn5/Je+zNXFc7757ffIObNoW9puxflVTxFIqVCyYoxjHGrtymq1IudM49uaagvzDKhB5mTcGCM55hri5H1NykVIKXFzc81quSbHREkZaVsQ6PuexWKBAKVMkCbSsEX0BjRj7BLxNeAHK4z9wHe+/ZvkFHnn7R+l6xaUXC21GK1znqVQpkjqLyhpZPH4i7QnD9gc32c/jCiCzCTM1P9CUYzWny9pJKURa4QiBjBorpZicRYjZg57krsB8qyKasfy9B32eSRNFzgDWQx2cZ/Clv76ms4axvMPOPvwt/CMpJ3ijWWXR8bX3sEu7ldi7BraRa1CMs7VAKftd9DxipyuKWIQv8A2FmmW5FSJtbUGaxtSVoxYMEpMcb47mAmkpcy1OsaYOudvBeM8dl4MWZ0+ojt7necffR1vPma/7/nCn/rvaJYnc2hRRL+fliEPOOCAAw444IAfDPwRnBH9YcMrI6KbB59ht73g9MF9Xn/3c/jVPVK+nR8T2rabw1ASKQ2kZFB7WyOyhTLVh2AceW4QMcHgmpZpjLx4/hGbkweErsMZReOAphEtE13b8Nkf+VGW3QoXAmVWtJACqrP6Cv2Yca7BeENMkZISpigPT+/Rtg1xygzDyMNHD+kWHaFpaELDcnWKs4ZpvMSawJvv/giI8uDhCc+ffUSMA9YZjo82LLuAkGdlE6CgcU8qyri9qsm1OSE2EJpltXxqJmVl2F7R7/dMMWGsx4a2Wl1R1GSCE1CLThmwtG3LOEyUUkASuSTu32uAarfth2tSKTTdCi01KXfor2i8pwmBNE54qxQyMWX6ccB1R3zpx/8MLrQsV8tKQEsha8G7OpdqjOVmt7/rAgWYpqkSe2NqCFFJOO/r33PCibvr9Gyahpvt7q5/9ebyigcPHxE1MU0TzaJDRMg5E2y17aaYaVdHdLYjhJa2a4l5ZIwjYexZr+7z+T/2F7DGYCWwu7nCGTCAlpGSJ+Jux7R7QZq2YAdYXRAwSBxp/aJW26D4EO4sHZVQViKq3mKioaiC9eRxws7Xv5aCqqlpxVJDrlRBVDBSSHgW97/A7uw9EkIRoRjFhUDjhPHptzh775eRsieLQYxQAO3PefL1/8jpu38CDR2+c3TLFrGeElq865iujkjn70E8h9KTh8uaUi0W26wpmtlvJ9rFMUgA6+fZW6DUezArCNU+f9uHK2I4v7hhcyRYm7HG40LLOz/6Zxm351x//JuExaL6X/KEs7Z2+Zb4h/a5c8ABBxxwwAEHHHDA9wdeGRH98l/436Al4YJDjMNbT04jpRRWyyXGWuI00fc9cRqYNANCTtWWqiWBGFQUcR6xFqct+/3I04+eMvSZy6uJ9WbBo8ePEOlJOdM2jmW7QEokpZGcJ9BZkbK1C1N0VlCzklJPnjLjMOB9g3OeaYosV2v+9J/9c6SitG3gNhHZGEtjV3gvfPDBb2Ntg3UC4jm995Cri2d03rNatiyWDU5ekhe0JuLtti/Y9RMilmaxgtCwWD/EmAYQUp5wbcQMPcEHjjcbJI9MuXDx4gmnp/dQBC2ZxaLDecsUM5ba37hoO0K3rDOh3lNyplhBrKH1AaOKinB0/zU2vIa1FiOZPAyMY60yaVDaZoGRgpaenBWjiZIL90+PuLoZSVOq9lNRcp6Yxn1Vra0w7aodd7FaMk5Trf+4tagqhM4RU2KKI85ZxIJxMs9VTvQ3NyyONtVCLA7jIKWB7bBle3PFw4envPboDawR0Mzu+gWJRJKCxkjwhkKLdR7EEJZrTOop045xt2XaXTDtzompx/klm/tv0S435Lhjd/6M8eaKIo6j1z9Hsi3BNzhra7upVOuxQdBiQBQrBmd8DTMyUlVEVbQoxQhCvQaNrT2kmi0xKs3JZ5jGPTErmmqFzGrZ8/HT3yGmXa3TSRPemUoYNTKcfYMrW1g+/grjqIRmSbM8wlhLaTdY+wV8ex927zOc/S46XULeE3cXtG6BWiGniXGwWL9Cc6kW5nqVklIGa7ACOhNppNb3PH32nG9/5zt89at/nJIjJUIU4ejNz1NMZnXygGHc4UumaL0vmmb5Sj6DDjjggAMOOOCAH1zIrCF9Wts64A8fr4yIro82L/tsFCiJnAvO1eCXNMZZZYGSE1NOc0dlpusappgpBcpsjzTWY6xjHM5pG4/FMo6F68tLprFnvV5yfHICmkGFnCMlJ2QmgsZarIRqMRQz2w1h7Af6vnaBtk1DKlWVarsFTbMCY+ob0DLPySklR7b7PUhNhRWpGlm7WPPGm28z7V7gZMQyYNUh80EopSb8FqDfbQntEgUW7aqSUHEgghUhtCtSuKQxBbTl5vKM5WpFtz6uhEgVIeGsx6A4kykpoSiLtsF5R9ZMTgUjFmt8TbqNA+osIqHaSaWQNGPFYsKCNrQYAkYmUn/NcP4+25sneOcJyxNs2LAKC+gM52Ok5Ago1sD52TNWyyWL9YpxGKoL1hiariV4R4oRZy3WOcrcaRrjxLZkUimsNiucdwiZ3fUlU5xYnx7Tk1kslozDwAfvf6u+bxNxJnF8clRnMtenlLyhiIAJqAm4UgnhVArGryjGkwu4Za4qo2twOfLw0du4ZokW2O+2GLFI6tlte+6/8wUwlsurq1pJIjLvY11U0JxxwVW12xpkPjdlnuEVIxgxs023klGhHoOYMqlYku0Yp4F+GlivG7JvCKsHyNRjck/MBcXfXT/WWvLVe0xhiRy9w9XzxCK/gW+WON/iwoIEZCzBHpG3HzPuzxAbqtKphuDqdW0opByJJSG2JgqjiveuqsfU9F9nqvL7xc+/y/X1DUZr/2uaaoDS+uQRofVcXlwwXN+QyxWn91/HOk/mkJp7wAEHHHDAAQcc8MOGV9cjWnINHpprOPr9DdO0x/tAmoYaAjMLhd7VOTyApmnJuZBSQQuoGKyrYTAhtPgTwaoy7gaCLyQN7Pdbzl7sSXHg/oNTJtvMnZ9QbakKWKoxs9aY5FKVn5wLy+UK5xzWWawYpt9nMa1/ltpJeSeLFm6bcUspTMOAX1T1bXP6iDEI/fUTchowRmtliNbKGmMDKhYfWk5OT4k5Mw091q4QR91PBSu1Z3UaE2maCO2S5dG9mipcEuSMKYWpH2pNSU6IKrvtDieF45N7WB/IWi26pjim4Zq4e4qJOxab13DtGrEOTE2GhWrJZNrRX/wecX+GMdA5QUyCdIMKqEZW7Yqh85X4lzo/eXVxweX6GO8dQ7+viqkIm80GSiHlxD5FQgjVIj1fH6rKOE2EEOjalrPLF9y/f8rXf+s32JweE7qWzWbN+cULShl4441HNI1le3PNxcULTu/dY7FY0nYLjHjENag4rNhaTXK7gFBaVDzJBo5PHpGHazRPiO3IOZHGLV4Ky5MTwmJFSErxS4zCarWi73vu3buHGiGXQs6w70emosSo5JIpeWTRNXRNqBPJopRy28gs1UauwhQLw5SZYqqEFkcuWhVQ8XzmK3+R/cW7PP29r2EuP8Iy1aAmqNfuNDAMezaPW3b9jnzxlNXmHnSKCx3Wd5S2EEuGlRLaFZomsrjZnl4TmDX2+NBSVMhZMMZgfE07RnhJnqWGjBngZLOq1T4CIOQ8YUzAd/fYSMd+e03OGRPWqHHEQ33LAQcccMABBxzwveIwI/oDj1dGRId+j7GGaRpBwVBogwOUkqe7Ls4aaGNJKdF1HQAp59pDaIWm7XC+gTmsp+nWdMuJOEacKgaL32xIeSLGkY8/+pAHD+6xXC5pbO0ZtcbUCo4q7hDTxJQhJWiaJdYahmHP9c2WbrFCrEXaWcecbwKduzyBqlpax3J5RPBLnHd45ytpXqxJsWexzpQ4ICWBFhStD+7i8e2SsDRV2Yoj++0lKSXa1THWdWgRVBPWOayxdX6waK230br/u5tLUp6JLbUuRYDlomMat2xvhM29RxgDZ+fXCHs2Swtxx/WHv8bNR7+MtQtMs2Fx8hZh9RgTWpx1kPcIkW65phTF0KI61eNiBJ1V7M16yRuvv8aLZxfstjtSzLx4dsbR5piSMvthy3q9BlWmOLLd3hB8wMia6PzctQkgs0034kPg6YsXLJcbdvueD588Rcm89vpDjk9a3njzIffv1aofKxbUc3n2gsuLM0LjWS03dKtTluv7qFQrrbV2VjAdZbbyxmnL+Pw9EGg299k9f0LcPUc0MbiOxfoe3fIeYh03V9d4N6vpKOAoOMR7GrvEqEEpTGPP5eULrE2sV0sa72e1XEkpsdtuMVrY70f2Y0KMw0i9xmKMSGkp44imyFAMLB/z2lcesHv6TS7e+3XKeD1fyxDx3Hv8Lt3RA4q5qqQyjzXIiYaiQuMDk7XshoFhd4G3WlX32rpaVVoseWewrqVpl1jX1Xu0KBhBtCq5Zg6aUrEYK2iuizilJIwVRMscmNTSHQWs84hp5+WaAxE94IADDjjggAO+R9yu439a2zrgDx2vjIheX1/X2T+jOGPIKdb/FgExOCfEmJimhLWKcwYxpXYbOoPB4F0gNAvA1ORSY7A+0DR7jGREJtAGrOCNQ9VT8sj583Mav8R3ARtANZNRiJkca9Ko9ZaUR3LOlJIIoSU0LcbYOg+XCvj6sH47FwjcKZsiBhHPRxeXSLninTctJrSk2JPjRBGL7VZYlbu0VUEoKrWTUqo227gGysi4vUSMIYRYSXOeSMMeaw2mDcQ4VYUVob85Z9heU0hY3+HcCoMFqdZQsZ7rmwu69QbvF2iuv7/vI+vNa6Sbj5H9k3r84hXD85Hx+gXqljjfEULAmhYVwTaCaEJKxAZXQ3NUGEsmx4mHD0740hffJceRF+fX7Pc79pfXDPtrMsJuu2R9vMb7hjhMmKIk74lpwtq6AIHUQJz9zQ0fffBtLl58yDfGEesbjBc2qzWPH9/j3R95k+P1EmdNtZACWMexX9e5RlEgM+yvKDmxWN/D2DCTdKGIRaQldErcJ2x3TBpv0Gki7q+xUtVroz3j9Qcw7Dk6eRPnLNthx9F6RZ6VfBdaVALOBhAlTRM6TjRNR9tYzj/4Jl5qoNLy+BQtgCamfsdwcwNhRc5QSGyWLe3RGu8s0zgyTHm2GHtSsXSPv0R3/x22H30dGzO2cZimo13ew1vPcrFGc4KSyWPPpIqIJatBRGnalnEHWvJ8HdZ+1XpV1JRk8sC0S5Spx/qAcQ2m6RCElCLGUGeJTU0HrmS2MKWCFUMqE2JC7a4VqYFgxlGUWRE+4IADDvivj7Lf/4Gvaf/db36ibb3x7/7g1+g0faJt6bz4/v2I9OFHf+Br3v2/fLLHyS/+3/6HP/A1P/p//fATbSs9ffaJXndQug444PsXr4yIhlkNsq7OPMZ8OyPqZouruft7SpEQFgh2ttQaRAzOBaxxTDGSc2a5rLUnUTM3N2focIMNyxriYgIiFs0j05TwTcuUMl4sqkJMmULtv/ROCd5Rxh5DfcDW2bILVVlMcWQ0wExirK1KUi6FUjI5ZcYp8ov/z/8R51r+j//7ls4HVDMxDjjvEfG1PqXoHfFUZqVJ5kqMUgg+4KzDS2G4OZ+dCAWRhBXqQz3CNE40IdQwoRAYxon+5pzlWnFmiWIxppKM1WqFiCHGyH7oadol236kbRqa9WsMcUfrGwQhpoLzDpwFW7skc1ZynucDvaXtOmzbYYxQCuQpocbifcNnPvMW+/2eMX2Li/Md3/nwfY5XS5xzXF284PjkCLf2aFbGYURLQaxgpM5wZoRp6Dl79iEff+c3kXTDujviz/z5P49fLDAGFouGk5MVXQgIzAsIpabFqkOk/p+8dwExDmug311ijJ3VPIMNC1zoMK7ac5vVI8R2CDXN2YhhjjZGKEyxRzTSrjZcXN+w2/VYv8Q3DYqlKFgxdYHECt4Hhj1YLFIS+5unDNcvuDp/im3XtMtlDeyK+zrX2i1qoFPjMK6qwkVLDTj6fePVY0wIgXs/8t/QGEPWTCmRWKDv91DyPHsK5EjsJ4pmHA5D7csNoSGOCUyer3OZk6QFNNXfJIUYE1kzrQvkVGoNjbF31vnbwKmapKt393LwHmy9n1UVkZdVSaKfUtLAAQcccMABBxzwQwNRkE9poeEQVvRq8MqIqBTFWMEipBjvZi5zzlhbEzrvugrnmg7vOxaLJXWkrJLCVHKt7QhhVuiVEFY8fu1tnr7/W2wvP8ZKwfkWNQtyKUizJCmYsKRYV1NzTQEyYgyxKJILRQVVcMZCEYyxWOuqVXfs2fe1x7TOh9palaLUzk5NGBtAG3bXA0YhxQHVwna3peuWCFqtqHPQy61FV2ZVKpeMFUhprDOqAqIJLRnn7DxVKBjxOFurbnKuqrLxDQvn8H4gTiPTGFmu17g54dR6D6pcX1/TNA7vDda3qDE0R29iBPL+gjINFHTu2JwIoUNMDXNyDnJO5DRxc7kjDg0n9x+DbbAuImKYpkRR5Y033mB3c8PZ0w/54PnI8gtfZhp7xu0Zq/WaJnSUHLm+ugQKTRsopeCs42Y3sLu+ZHv5IYwXnCwCni1eBj7/2S8yTD3WGpyXag2eE2gt4FxLKQXvC2hV2VMeKfnlJ04pOs+JwmJ9QrdY4YyjuI5m5Um756im2nepilJVb2Wgv/iA9t7nOD46BgzGd/h2DeLRuYdWjEEUnDNYCzFO+MUR1ha88+Bbim0oGMJyw3q1QtVgTcEwUuJIzpZUDKoWaz0x5dkGzF2Vys1+hy47jEBRGIeepmEOf6pBQ0ZqvQ95oMwVPdPUozlijEclf9d9aqQuwOjcjSqi5DRiavMrKHcztgAppTtSWrido4YYa6+qc24m/joHh9V9P+CAAw444IADDjjghwuvjIgayUiBfjcC4NvmrgtSCrPqWe19xjpyUhbLFSG05FIffmNK5Jjqw77A9vwZ26fvc/X8PXLcY8OCtz/341xfPOH8xTNC22JDoFtvUPE0q/vVHljAohStSbpaMmDoOgeayHEClKKKFcU5R6MtYxwA0JLY9z2LxQbvWjITqRTGGDGl5yf+t/9ruuBIGomTsF4e470npUxKU630UCiaEVNVX+cMgjLuz8lqwS4wKeGcI8b555yZyXKZFSgoBYxxVakSiw9VZev7LUO/x6WEDS1JI80SluslbYrkUgOQnEkUPO36DfbTSOq3M7Go790glXQbg2quc4dxi45bhl45T4nu9E2KePbDgDGBxXLJcrmh9RnSC379Nz4g5pFnZ08oZWJ9dERME01ouLx4wTj0ZJ0QhWW3Yr+74Ghl+eI7G+RNTxHP6ugY6yYoA85AzoUxK9YLGIsRMwcGTXOojqHkTEpp7iPdYxQM8yIAlRhdvRjY+YZusSD4Sib73SWimVJy/fOWAurA5cff4OHyAcdHJ4hpUL/A+jrHa7zFWYugTCXirNK1DqcJ3BJxDjEG4yzOGIxmUs4YA5onyKmq9EBKliKegqFQ+26L1IUSxVRinBLjGOmCYxr2SInkJKg0WFs7WhGI0wSlMPXXaJnIaULQasHF3KnHqjr3ndbfI6aqooriXag9qnnCe8c4juTMrHRK7YB1tbpGjMHZwBgHpmmqiyg1aawuuxzCig444IADDjjggO8Vh7CiH3i8MiK6vzm7U1HEGIomuq7FGUgpYm2DijLlBMbCHKrSjwlRIcZxTvZMJDKXz57y9Hd/HY09VqpalFRw3Qn3Nq9x+nqPCy0YyGoIzQrrAgXLnJaElQZXH+uxrtaelLTn5uoFaQ4WymUipQlrTE09LVBUWHRLgm+r8qqZkiJd2/J/+j/871ivAiKC14C4TIwJLYqdA4Rui5BMUbQkRCxSDNP2nCdf/39z9PhLPPzsnyWmqoF6H5jihM6qXiqJUpSuW1AUmuWqkkUqmZAUWPimWjStw4UFxgWur2+YcqYNDV3TYIxy8fG3WTqDXz3EhFNSuSBO5zRhBUZJU49tFhhnkFyQcU/ZP4e0I5WJWBJmcUzSAK5huToCY8glsz464cs/9sdYre/z7LLHhVO21zcwDnz4e9/i8aNTQrnG2RGIIIb1YsE7b7zN49cf8eabb6BFsN7SdB3OB3IBSsb7BmM9s1cZEIzeBS/XlNdZvbYCmhOac7UB36a/qq3nvAzsbnpkfYwTKLHW3qjGesK1qn2lRFLecnP1nIVdEdoWZz0yJ/6KFlIa0Tgx7K6Z+mucFpS6cOBCW5OjAfKElpFAQVOGcvu7KmFLudCsj3EmQMwM456M3C3Y5KI4Y+nahjjs0JKqTTpHrK1KsbNNPV6AlHrlJ62qbdFCEeaKJFOvlar9zgP8Bs2AKfX+NA6/XDD0hRjnxRSY7bjV6izGoCXXbWvB+TArybnWMpVapaSHGdEDDjjggAMOOOCAHzq8OmuuCN77OsunhThsSeOeo6MjpnHi5N4JYj0uRVSUYYjs9iM57YFqTXXWEKxFksGHI17/7FeI0w5nDUUMoV0T1icohqGU2plpDcZaxjhgph7vF5W4zF+VRNQVlpQj5Pqw7ExVa0FIRVF7O1tYk0MVpcyzedWqWBDNtMHNM3FCCIG2sQzDeJcKXK3HdbuKUlKtWUklYyw4kzBa6Id0F+YkIjhbj139u0FCg9GJPA606wcwkx3UYE1Amw5SpJ8mpilSpkTTtKxWDQaq6quJhp7tB7/Ftllz/MZX2Tz8Ea6eVfIvGIydrZUlo3kixS0x7clpX62locM2HU04QsXWKpyUEU2glvXpY764uccXsVy9+Ij3v/Xb7LZbHh033D+1HG3eoPWGhTdgPTQnHD/8DE3bMI07Sq6KpJU6p9nvt/iwwFqHGvuSBMkcmjNX6hSt9lExEUpBZQ6/KglranoyeUKpKqkYSHnAuIZm/YjQNgz7S6yxOONJZcQgbJanmO5B7dPUjCGSYwatKuW+vyGPdU7TOyq3VDvbm+u+5RzxVojThJY9zoCVWwoNUwLj1zTdmv2Y7mZ8r3c7co71vQsslw3IPIMsde54Xq2pAVsIWQtiqqI9pWo/bxdHGFPPq2ohpYFxGMgp1hnpW8UYRbT2nu62lxx3C0LwOGcZxxGZ+3dvrbpWDJlcST91X7xzGBPqfW+UfT8xjvEP6VPngAMOOOCAAw74I4PaoPjpbeuAP3S8MiI6TdPLGdBYMMYyxcjF5SWhXTDGkWAsznvGlHhxfknwLWIsRSMWpfWeaATrG2yzZnG04cm3f5tsLN3yFNetMb5BVAntEtUMqjTtgn4cuLl6zmZ9H+vbGtBiDEKBXBh3V6Rpj6YJKRmcqzOcRargxss5VmMMMRemOKCiFKWS4TjifagKE1WdLFRrbynlzopsRCha1SixdX7OiIDzHD/8Kra7j5Apc0KNiMz2xrpN7wO5jPzu1/4n8vXHLE/e5K0f/fNzQJPM1skbzl48Ixc4efiQxWKJtZ6SFRXFikdVCO2aUQwMTzl//z9y9OiLHD98l3HYosZhjEVztQaX2BOLUkxLVEEWp6wefolil4xTrXPBKleXZ+Q0Yq3n+PQRAWXaXvJoE1i+tWK3V1y34vj0Me1qTrJNE2m4AR/ovDDcPKPkHaKFgqNQGPcFLRHx7ctgHal9sKrVQixzpY5RpWiZK10ylEya+nle1uGCJ6dqdRbxtRnWORIW7QKyOGVxnDAUcswsrcE2S4wRotbAnX53w77vEd/gQof3jrYL2MZViyuFkiJ5qgsRRmras3MtQib4jrQfSDmTpVp0C0KWlvXRYyatiq9oRLSwWi4Yx4lxGADFOU+MQw1esvN7l7p4gK09pCE0JLVo8SydoxTFulCtt1TlNBhldQQlRYbdljjsSGlCKVitIVI31+csjk7JM+ms94HQNIEYY7VAx4jXSMoJChhrQR1iHcE41HvGyTDG79+0yAMOOOCAAw444IAD/uvg1aXmdgtSTtWqZwwhNCzWDcMwMGVlYR1iPM4FxjSgWGKptkowTCniPTjbUMTijMVRg1SGsce3S1b+GG8DpWR8qIqTc462XWB9IE2RcdgSSp1byzky7q8rAZ32aEkE52pwaG7xYUnMGYoi7uVcmzEGDxRVNE2zgmrIudoyLQ3edJSUCY1Fs5Kma5aLI7L1pJSQlIjDDTlOTNNAzhHB4tkw9TtMeg+HIOKwYcFyc1rVyTn8xUnD2+/8KO997Qnbp7/B79yc8/jzf5KTh59HJRP3Lwjjh5j2lEXjSXGAUskQpSaZlpjAH2HWrxPPf4cynXP+0dc4PvkM69PXmZKQSiKXgnUty80DNsv1HMQ0sR/2uHYFxqJSNd5x2KIlQkkMw464XGF9DTzKwyU+7nlwckJ772384h5qW5AGI0BzRc4jMd4gOuGso4jFi4Aow7jHtwuKzKlpWaFkrAHEohREzWx/rSFNpSS0FEqqCyHeVbJeSkaco7EBG1bYsEKsrwFHJaI5Y8jkaSDlc1Qs1jmSJkwplFwYrs9YHD3A+VoJY8Qi1mCro5pSMurDXPnTk1Ocf3ft5bRYwvoBQiYON8Q0kRDW9x6TbUO+tdFSw72MQhsczrQYazE4jGtxXc1fvk0DNmJI1DnPgmBcqG5028y1Mco47qFMGKY6KzovEq2OjnHHx/TDnmkcSdNU7ctimaYBneeHZa4GwijG1FAkMFASEncIBZVCwjBhsS6AbbF4rByI6AEHHHDAAQcc8L1BVD/F1NyDJPoq8MqI6ObkhHGsQUXWOISqlGy6I1xoabsl1lSVTo2jbTv6MSK2XizGOhBLypnGOayz7PdbcB2rZYfzLSFUNVRLRkq1TRqxGCyN9wQj9PGS8eo5N/0VmntEC0YLzjeAQXKstkZRtLTU6UC56y29VYOqRbdaa03oII9cPPkGptxgbMD5hsXmIe7eW4SwpKzXiISaxJsmnnznlynjlmkcUIWc65wqBmKKIA7jAotuw9Hp63TtErUBoc7XjvvnqEYevvFZPv72b3H5/JtcXD3ly39S8F65eO9XMBScbYk5IkqtADEGowaK1vAhcTx4+6t8Z39J2n9EV/b0l99k6s9YHb/FYv2IsL4PvsU3gZxqoq6REaYJtPbCTqVag33wjIMhZiXGyMcffcCDR2+xXK7pzwZgj1u8QVjdx7bHYByCrd2kdoWJjkyalfACYnF+VRcpGlDjAUtJiUIhpoixlehYH9CcZqsuoErOCTThnaWk2zTgGvhEVoZxwheh9R3WLlAMVh0SgByrep0dZu7PzFoIKfL8m7/LzeUZb36ppYQFzniqxsgdKRdTj7PYOaRI5pRfMWSUmAxJLd41uGVLyVO1adMgpVp1tRRyrjUq3nlELL5xiBhyjGhJlJxomlDDtVBKTnURxztkDmi6Vd/nCGqc9+yunlKGa0xoULFMSdjeJLwL+BBoFw3a1Y5cRPChwZIps0UcmTtwteC9gVLwFoY8gNbzkLOSsaRpRN2IsR2L5g/70+eAAw444IADDviBxyGs6Acer4yIZlWcbxFxtcLBWI6O7iHWo2IARaUmmjrn2BwdsXvyrNoHxdZ4WJ17O7UGyKgqy+URq9WqJsjGiSlVpSfHkWHa0i6O8dZhrLK/fM64u6JMO8p0Q56u5uAWQbojfNNhxZJLYZoKi/YEaxyq5rv6En9/9YyqkqZE4z3rzZrp4hyHqYmmN2dosqwfvE7oVuSkmJJ48fE32J19AycWEJxpaHzAuEARKFkxrmFz/wGLdsk4jVyeP639q+MNw/CCuD9HUqRxntde/yz5Q+H8/Ft88zf+R95696ssH3yeZnWCbZeUnPHGIqXMYT63RSClHs9wzBtf/O949q1fQm9+D6MOk264evorcLHm9PWvEI7eJiVHSQP9sGOKkRRLnX2kzuHuxwnRQrtYslyt2N407PY9ITiMgRgzvjuh3bwBfkMx7VyHU/tHjAZwgrMCdiLGgaQFYzwJh2vXZLWI7ebFDAUSuSSmKaFjjw8dVmAae0rOtG2H8/W1bRMY+j0U6j6JIl5Iecd4k0jjHjGhknUXIGck3WDivlp/g6Ax8nu/+Wukqyekkpn6S6awYOlbpBSMnfs4qTObtwGxZU4NKqUg3tO2y5rYTL32C2BCba2dUsGUiPeWmPLdbHCaSW3T1E5WQ+3kzXFiigOlTDWcK02kGDFuDgeTmixcCboFMaREtcSPV8RprAsfoWW1OUaMB2CKkWnKrFYdbdvU450yxszdoFTHgrUGZzw5RvqrLXkasaZO31KElDNTyiSzx/sRzG2n6AEHHHDAAQcccMABPyx4ZUS0BsnY+pCrkDWx7XtCU/sgqdEqNcAnZ5oQsHbuztRb9YXZAjuHo1hLVsUYS3BCij1aImN/Q4k9TbfAG0eeRnbTFdPVGc5onV0LHeIqCRAj2NDgQlPn3XLBtzUQR8QDhpSnuznP20oQa2uYkBUw4Qi/+Qw5KqZUS3A2Sh6vePH+jvtvfwHvHB99+1d5/v6vVtunghNB80BKI2W8IZvA0cPPcPrgHdRAHHu6BUzjJSVNkCdMnmh9oEgNTRJreefdL7E+OuXJs/f54Dvf5I//hb+BW56yv3mBm/sgzazU1fHKesxLnoixR6zl4Wf/NOfvWYgXpDxW8j/tOPvo66xzxjcbchlqEJNtCG5B8IFUCtbY2u2qhZISwzBgQmBhbE1xFWjuvYtByO0pznUolpymed4T6t5ZkAYbOop4tGQigrENTVjimxXGWUSV7fVFfR+qBBdq+JQUNCeuzp6wvb5mjIXFasXJvXs4W1N5Sy5MQ8QasCTQgkiPpi1ZPUUBY7Eocfs+vgw0q4egganfsjt/QsMeUYj9HtqRUjIlF7Kp5PC247YUJeVcw6zgjriLb6pdtdRFgXo+aoCVs4U0bBn6gZwLZu6BVeNYro5xvqMoTHHEeE+3PGIhMPQ7hExOI1kv7vo96/ZNVWbFVku5GNS12MUJZdgTmg7xHWrd3WJLyvXaKqWQxgFrlBxHbq25RuoikTH17sVarPfsriNoDYVCLGI91rm6eKETh/KWAw444PsJZb9/1bvw/YVPoBSlb3/nE23q8//9H/y6dFCmDvikOCiiP/B4hYqo1P5P4xBjyVG4vr4ihJ7FYjUX33NHZFrvWHQN2/2I9x5RmFLCNgFUEQrdYkXwntsYLaGGwYRmhes2NdTIhjrTmVtS64njDnLBYrGuAU21kzNFsA7nl4S2gaajNjbmWcCZ5ypLRnPEWU9wnkQlYWmKFOno7r9LvHkGGhFNqEswDlw/+RbD/ornH/0OknvEFHSunQFBjKENHcv7b7J5/AXEtKARI2BFSamQsqeUgMYG8gTSk+MWiEwpsjx6yFc/86f58MkH/Nav/Ht+/M/9ZYK7VULrDOHdsZKamAo1pMY6JWJZvfUn0TjAeMHu8j1KLnh/hC8Qt88w3uGaDbiGpt3gQmCcJoZpwskc0mQsxQWmnBinyOV2x5uvvY57+EXKbDXNRZmm3dyh6rCic5Kxsh/2QCX6xloUg3MdbbdBrGccB7Y355ScMDXbF2fqAoH1DcU4sC3NymByRBXOzs9I44Bo5v7JEatFi288xRScKRgEFVtV9yyIsbTdAvQaM1yQdY8pmSasWN27T756grOW/eUzVsePieOEMR5KrTGRKtmjRdFc5vAircqqZnJKWNdi7HfHv9WkX4hFyXHCWoO1Bi1grMf7DrG1g7dzoR7PIjUd2reogvNLmmIY9jtEBVssxehdh2fOgpUW11pGoLiWQnUpGKn3ac4Z5zzr1Yph2JNL5paAzntayeftn1KTik3T0R7f4+L5B0gcMNYRuiXWtjVyqkRinP7rftgccMABBxxwwAEHHPB9h1dGRIsarHEY6zFGEGlqDYUW+n6LasFagzHgXcAZw3rRcnN9hVgI3nF6ckoTGkpOWFGMNeSh0LWBooWcE8Y1CB60vAwToj7gG9uRnWKd3HWGlqJ435CykrPDOof3HVEN4/4GSiKmiV2/R4yhCc2slurc62nAQkojXbdEcHhj0eGa8eY5lAnJkbjfc/nkGzTUsKYCeGNwxoL1iG/olsdY60j9FW7ZoCUTvK8EUmoHZtaESp0/tGGu7xgyygRhxfFrn2fz2hd48p3f4jvf+l0evfEm1sisuM2pv78vtbftOoZxYukczkA2UCQg7gH3Tx/iraHEQk4D4zaRstBgWHYLUskM/R4VKCURi9C2bQ1joi42GWu5ublhd9wTvCcDMSfEOLz3dzZnnQN+EENoAmkaSXEi58yUlXbpGFK1po7jgFBrTawJuGDqNWVrGrNBadquprPaUBVFa6viPdxwfX3BuC2Ebsn9+yekUhBxGFtnSw1gjUXUYE1H5gydeqJ5gV163vzCj/Lx1yN5f0G6eU66OsM3J5SmQdShNU2pLqqU+qdqmWt7DG0zhzNRZtLK3XFAQLTOdA7TSJYCFILv8N1iPvf1dWZ+XYVi7W06M4i4O9W7dsjU+VURg/HVpqvF4MKCKVXF1lk/W94TKSUWiwVFtYYtlVRt8/N1dJvgDNwpr1mVYjzN6oRlylw9fwK5wJQ4OmqxoSPlzG57/V/pU+aAAw444IADDvgji8KnV9/yaW3ngO8Jr4yItt0Rzs3q5dy8US2bMvcS9sQ00bYNqoVh2KF54K3XH9AtlnjrKUnZ9T0iStSCswY3q2Y5prlSAkrKpBixrj6x39o3revY9wMEjxhPszzBSEEUXJ7q/oljOyXGqdp8tVRVarla1QdyVaZxx35/AVg2x48xpSBmTtN1Heob1AWG/ZaxP0cR1qePaLfXTP0WEBbBQ0mVIDUBFehvXnD97NsUPN3RY3Ix7Lc3LFYbjk4fsFifYsSRTSUFNdCowwYhT5HHb/wY6mpv6cO3vkDcX1XiYaot95a0yPxlraEUGOPEQpuaPpsnnIEhTuTSYp0lSUFtQ3f0CADrA7GkOjs7n1/vLXHIxBirwukc3Uw0Sym0XUvJmZgTGSWlNHdMGqR6hivMPIurFhWhAN1ySdMuEddgvCW0gZIm7Ewelds6kdqh2fc9Lnhc8GxWa442G7rFAhFh6LdcvXjC9dkzxu0ONcLb73yWmCClqSqZoojxxJIxboX4E3yzwDRHSLsm4WnXG3a75zgS09kHNKt7RG8xsqS4+TosNWyolFSvxVLIOROnkdbVPtfbty2/j+CB0jSB0Rr6/a52febCwrfkZkLE18RavnuBQUTvyKF3gUEMqpmSBWMt3jrqqKqCZoSaIqzeMY3jrG/WfWzbdq4dyjVgaU7b1flU3YZ2vVxIuCXSQsGzPn5M06whD2gaCV5omjWTtvjN40/zo+WAAw444IADDjjggB8AvDIiam1NFb2dndNS2ehtGJG7rTVRmR9wIYQGVeXJxx+SYkazVMUyOLw1LBctIVR7ogClRPrtDdM40DYNbdPNFR5VKes2K/qpZ4wT0RiyOBaLFmcskgIJZRhGxikiRRATCMs1bdtiTD10KWVM7Mm7K8AitgVraZsW6ysByyoUVRb3HlPKDdYF1vc/w3LzJmnYMfbnPP/ga+TpkuvxhlLi3HlaH/JzKcT+khQTUypcnTs+frLh+NHn+Mxnv4QtgTj1lBxJWYk0hONHSHuMiqMJQox78A1GC4Y8E5XbNFkwAg5DEaVp2qqblQJaraVdZ3DWIZlKQMikVGb7aQ0JUjVzomrdrnOGGOt8rLUWHxqMeUmwhnHCWlcDoFJCEYzUflaVSmZyUXIu+G7Jqu2YpoiqYGzAWF97Mh14H2Y7LTATUQCDsjCWEBru3bt/F4akWnAusFwes1ysWR0/4PzJ+4xp4sXVQEo1Rfh4s6LM3aWQmLLB+XtEZzDSQi5EHaotWBVjYNp+RN4/ZJKEMw+RdoXO5LqUXMmcKoKSUgQMOSfEzpUn34WZ0BmLDUs8jpwivq1BWrurK5ou0yyWNen5tk+VuU5Ia0iSAJoTMWaySUgqlJRxoQVuyWS5S90tJTOOA6VkDIJ4T8qxpgznghYlk+t9pmVOALYwn7fKQ6vqWk+KoVmcQOnZX7/g/OICYweOHn8W364/tc+VAw444IADDjjghwPCp1jfwqeznQO+N7wyIqolzop6JYaUgnW2JmhqVbOccYz9wMX5Gc9fnBNTRqzBB4ezHmMcwQfsKARv2KzaquJpYhy29PsdKUVWywVdE0hpoMaoVLI09rBeLrm4TGzWGzabY25uLkklY5xnGkdiLrNyWJW2fjcx9AkXGrx3pJQIoePR49M655gF61t802GsI4vW9+Msk/EsTt4m7q/4+Nu/jRHP5viIj771q0zbD7HBUjU/pW0f0DRr9v2W/uaMZnnK8vQNlsf3WRzdwy9W2OAAIaeC9StSGslNYuU78A0p9qQkTEPPogskBN80uNkmWsW2ynZFwBrBjBGDYRoTi67BuVpT4m1TeyjnGg6DICRCaCglkXKCeRFBcJWMoPMsITShnX9fTRfOJSNYnK0hVEPeU7ROeELtA02p1MRga2tia8wIleR6alerEY/M4UcqOh+/mQLNgUfOmVoFVIQU413oztj3eOdRhMXyiO6zC3bbLSLC0vtKLCUjDqxzKAFnPFpG4tRjjcVYgSz4sCA5j6rBMZCGG8Zs6ZqADw7Vmk5LyUgp1J0zWOvA1B7TnCJQZzLnu6SSVqkVRtJ0tRNUDNthomsXeGNrmvD1xGK5xjcNYKlTwDMhzYl+d0WOPc4ImqstWMtIGrd1MWi2C49TvUeCbykxYkyh69aINaQcax1OYb4WFDPPFasAUv3UCqjYl9fXvKJSNCLiOHnwDif3lXFMFDF4e/t+DzjggAMOOOCAAw74YcErI6L99hrnPcz1EdZanLGznZK7CpcnT57Q767wVubUXIP3DavVGu8btBS8szTB4r1jv71ku73ASKFrOjarJUIhxx2Qb4sxMMaTs+Jcw/HRht1+TynVAtw0Dc6Fu55TEYMauVP5an8oxFQVw/1+wHuPc56mbavyJ7Xb8XZ9RcTiXINqxnbQmoA1QrELjh9/juHcomnL1c1zVBxZW8ziM9x//IBHQfDNkmZ5UpVM6gxmrfGoD/FFCsVCPyZM47FFa4qqMVxdXjBslc3pw9qvaaDkWNOG5z2sSnSl6dM0segavHMIELy9S3lF7BxqVBBj8T4wjmVmIjPhKJXk1oAbR9e1GGu42e1o25acM4ilaRqMMd9VgaNaU5JzSVjjaBpfCa0WUorENM0kWKqqbgyomX+13tmMEb0TE+u7KqAZZ4Rh6Dl7eoU1yuZoRdMumabCvh+wxuJDqNv3dTPWGIxtEOPQRtEyIS4wjhMmKs46Skkvf+9sUe26QBx3TENbrznnQYQQAjFNaJFK20ohTmOtoJl/9jY11xhz970QQq198ZacBrb9yKJtq4aqmd32DDs0tN0KF9p6PjVR0kTJPXYOHtJ5TlRViXONDFowRgi+zsIGKWQdKSkx7TO+aTEyn6syXzN3iqvcnTudFVljTQ1m0pfn9jZlepxGrFgEoaREHPtP74PlgAMOOOCAAw744UBdSf/0tnXAHzpeGRF1RiEnMAVrA87a+sBvzF0fpzGWJgSMHGOsqxUTc+CN9w3WOFwwLNqq3OWcGfobxv6So80KZwsl7u5qShDmh3ClZEgkFMVKQ9sGbrY3IJncJ6yZXl7bwh3hU6CozPbTqroJtRNTxbLowmzb/X2lkdSAF+87+mlicfw61jUUMiVGXLegXx+xPXuftr3P6uhNjk4+A22LmLbaOTXVFNfb9FXVOVSn/p6MUMTQrdYohlgUxJIVlken5DSy7ScQw6K7rcyp85p38TYzqTg+PiZ4h5Z0Zw+ulttZ9DJS5wxF5uqcmeDMpKPoHLozV9vcJqt678k510UH38xJvS8JaIxxrsAB5wJGzMuuy1IoAkYUJ0pOkZxiDRGSeRty60qVuaME4NYKW9CS0QJd0/DwwT222wvOnj/lzbd/hHEYGIcR1cJuV89b03gWi0DjLdaZOhMswlQKrlmTGYnThBHBhYZRDEUNxXhsu2KxOsa6gGg9f2lKdN0C5xxQiOk2SEhxRmqPqOrLwCEBg7kbHK19nZUwtu2COPWM00QwgjN1DjVPe676Hd1iiRVDihNxGihpxPu2kmkpOFMVS2stOcZKhtOEIJhSmK7PmPYX2GBIMTO6BucbMo5uuUGMUMpEVjPvW71vU84UoGnBuvByVvTlrfTynrCgJTHux//MT5EDDjjggAMOOOCHFof6lh94vLr6ljTNM58F0VxtkEWrAhNHYpooKdF0C8TlmdiYuU7iZQ9i2zZYK5SSGKeRrvWYDGW6okiqibXUdFWDVKFMhDJXZ5RcBTXBcXxyj6urS/p+YLlo7giB3tkLq/JW5uoNgVkdLfTjyNK3eN9QbonEPAcoUoW7FDM6/4eY2d7qLWihOX6bcPwmm1TAOLKtRE6KYqxgVBAtsyJq5r7GQj/UmhCZ62O0ysnV6lkisSTiOOCtZdEGRAz7fQ2iSTlRimJEMfOwaGhaumZBzvPMpjFzqu6sdmlC8lwzI5ZcqtVZi86dmFrJqlIJuRbGcaRbVDJZFxJcPQ9zknGZ60xu+2Jn5otKbZIVVXKphNe6gNV6HEtJFBIWf2cBre9nDvtRUCmoZrRkcozkVMB5XAjcu/eY9eoUzUIcI1pSDeKZP4zGaWIcR6zxLJaRxWKBzHOeYh1Ns8CYmky7evg21rfcXJ2xXm948MZnKXNSbV1YuTOFA3Xe2ViHd6H2ihYllwkb4jxHa+f9n1VekdkKbJimaZ4HDVWJtZXQxykjVtE4Md6M8yGpiqdvG3yzmM9XtcK7eW63WMs47GuIFRbyxMXZh0jeEhZLtCj9/pJSLGoaULDe8/EHv4O1ns3xKZujU8QEjK0JvEUVKbneH2QoglWZVesCUhdnghOm2b59wAEHHHDAAQcccMAPD14ZEa0q1qygZSVnQdUyTbFaTu8mSCuhyTmS8nA3y+itYIplSANJPM4KQmScLihxizNCHhNYh/UB1KDiUTEUZO5IDPjQUdTN4UfKanXEYrGuxCWnO3UWqRZM5yqZq8qd3HVxTind2Q6btqqnt/OeopBzpO+3lJIY+i3Wxjpn2nS44HApsL25wlhX60xkrvwoqYbBAFBnJFMqpDRxs71msToil4yZSeHtHF8qEyVGcMLpZnOn6FbrQQ2VsdbgbP2mSJ1HzLkm3cLL8b5b827RSgrLbL+01sy1L5ZgHWkmfDUICRSHMXWbOaWZmNf/lZwpCiklUoo4axE3p7WaSkJFzFwFc9vZWlh0HZpuFwNqhc3tHqrezoi+JJNomb8U5z1JCznFGsykFhHLdrslzrU+L20eOiuGDqVws9ux3e0IztJ1HdnmOUW2kt+Co3v4Nvfe+SJa8qxu1o7UouUuhAsts0o8z4IaD6nMCxqK5og4D4bbd8KtjmjEsugWxGmqgUXeo6nMVldlihPPnz8jOMf901Oc85SZiKZhSxpvCO0KYzu0JIyzd0TVOktO9RoAcCHQ+WOMbZiGHY1vsK6jiCOmSLde15nS3RXP+ysuzz5ksTpifXSftlujqboOrGuZcp6nVgXmWVS5VXcBZ17qpAcccMABB/wA4qAmHfAqcFBEf+Dx6npEizJNESMW42CcBqxxs5ImIFVZCQ3oELHG0TaOlCKlTAz73UyYMsF3bDb32Rwd1aTSboXkQkk9ibESzWxxrq2WWpXZauqJGXb7HSnV+hdrLc45nHMY83JGD6O0bYd3DTnXOdPb2ca5MIN+6JlS5FSY5yDr90Wk2h6lErV+t2VzFFh0Lda1QGI37KoyqEpOE2SZ039nMo7O9TGVmNcU2AfEVEilsN9d4KzFOUPuLyj7M8QvOTp9l8XiiJLiXb9jmZVJ76tiJ1rIpaqjORdS6u/etzH1mNwSpRTTnQXXiEExpFJwTSCIJ5d815cpYsnOMU0Tdg6kEbGklO7Si7XUWJ2u68g58+LFC05OjmsCLlq7Zt3vs/Ai4BpKjHh7+/1bsjaH5fy+ucWaqlMreYwRrCRuLp9j3QLXrgFhGAZKrirry/0UxFSrrxGDnUO0Uo7EOOH9sp5jql3YUt/LMCUMpVqBb1Xk/8Xggc67Vb/vnCPGOJPyjPcBMfZO1b2FCFgxLNqW3W4gloiUgkGJU8Rbxxuvv8vzZ085O7/g9ddeq2Q5ZzT3XJ8/xYcFm5PXCM2iEkBVDEqwhmwE5xzTVOoxLxNjihgyzncUM5Nn74glI9KQ84gTyNPIzfkztpfnONfRdhuaxZqT+2+CCmLBqKAqDEMl/bfH2tr/eVLwAQcccMABBxxwwAF/1PEKiajh4vyC6+stYiwxJ4xxLBZLjk5P7p6/r64uuDx7wnKx5OTkAYtuAWpI1nF0eh9rPd41VenSTFg/AluwWbGaUEkYa2ZVLZPjWPtBFaQUrDW0jcMuffXozhAUOz+YgyIGUopVgVPB2nA3j+isxwVD2xX2+x05DfM2ZsKLmW21EJxFfKDkxPb6Cu9H0Mw47Ai2pvlaY0glzwE4ApoxgG+WiFis9dWCaipRjzkRvBJcIMaB82fX2JRxTnGakZLvSFGpHtE7ImTmTsjKUW/3dxYGy3cHzdwSR6SqoIggxuDm5NnbuV5wGFuVQu/9rHIXnKvE92XgzUzEjCGn2oW6XC4pRQn+tgbGUGabbiXQBetbWhdwRpmmnmkaCU3AOXOnft5BM+SMlsR+d8X1+YeUFAnLR6ybFWIMq/WakjP7YXg5n2wtxtiXM6oCSsHOs5C3wTu3pDdj0FxQau/qd8eA33Z8VnKpdzJzPQ5Fy93xNRSG3Y6wMrOdnDs1mKxoThgEb+v8r1LPq3OGq/Nz3vzMF1gsOy7Ons6zxPOv8guOj98kjQOX58/xzZLlak3TdjVYKo0IhTQNWGtIaWR/8xGh6fDhCOcWGNdUi7zzINAtjxj2N2CkLiIVUI2ktOfqagfXzxjjxMM33q3qeUz1lGhhGAbarr0LyTrggAMOOOCAAw74nlDmr09rWwf8oePVEVHjGFVZHG0QY1Hs3Xzerh/vqj58WHL/0RssmpZlt6whOupmIgkxT8QUZ5VS8b7DqEGtkDBoFiQVckqIZLJmSspQqjqZkpBVCH6Dc4FIwluHUWUa+jmdNuJ9oKSBy8tnaOxZNEdkKTTr+5jlQ6wxaEosG4MpiorSD3uMcXRhSbCeOG9LpFTVE2GYj4eZ3/sYJ1LKLJcr7J01VbFktETE3/Z2FmKJaMp471mt1kxjj5HCG2+9iTW1CieLEGNPUaGoINZRUiZ4f0dwnLW1p1ICaL5T4sQIeje/p5XAEyiq5JTIOeEMFAxxmrBG6zAslcQa5zHGs+wanLfkkuq8phqsszhrX5KwmSh3XYexc0gRVTUr1KoQMW6eza1kMeWXCug0DsQoUDLuVsqc05enoWe3u0TzgHUtoTthc/Im4po6J6uK9Z6lqwE+t6TwVvEUUbzzWFPpZYqx2oldvX1y1jloS5jGiZFc1dd5FlhwiCiiufLiclsJVK9xLQWKYhCMUUpODLstbduCrdd6GoeqoJZqvbYitWIlQ5zquTg+XvPkw2+wWq1onCXnSvyssZRcrc+N72gKjNOey7MnNG0gNEtUCjn2pP2W1dEDjh58louSMa6BsMa0a8R61NQpT+cajh484ur8Y6Zxj+sWiBEyIMw9qSpcPPuAcZp48+13UQq5FHCWUgpTrCquHJjoAQcccMABBxxwwA8dXhkRbRvPg3unNYDI2EpGZ/ID1RppbpN058CW3XZHQXDeU0qhH4aalPr7bKQ5JYqpXM1aQ84ROyeT1k7L2itpsOQyVXUrJeK4xXUNBihlJE4jKUXEgPOGmHakGAnWIOIR3RJ3N/h2hWgmDQlrhTT2ZNmz2Zyw6E4YhmqJvSWUt0ojvCSfUEU8EaHxga4RVBMpg3VKMHs09RALsX/CbphYbN4miZJiZNEeY3LCpJE0XlJ0JGpBrKdZnGCsYzfVXk6ViBFDvJ2rRIllAFWs1MvBzjyOYu7mRm8VQOdCVdBKwVjPlIUXZxdM48Drj04JwWKtQZwwpURJmSLKNCWgME0jbbsE9WiphBNVCrfW4EwaIqGxGBuoRTDm7vejSomR3W4PGJq2qaqkgaZxcxSOIc9zl6UkfFhwr13OtS+mLkYw99fO26yqov+usKLb1N8a2GRw1lYCbF1VDW9Tfq3MVSWKcw7vu7v9fTlHzDz7zJ3dWbXOD8tsUTXGMExDXXBIme11jzO+VqFoYowFe2sDVohaahqz8yTAmsJ641CFcejnmh1YLBYUYxHxs5Vc8aHDecc47Jn2e0LjkVyDrna7OkvaHb9NtzzCWo8Yjyp19rhE+r7HWsPm9BHnZx8zlULbtjS2qSpncRjncW3LcnVCysBtmBVVBS9AzHleWDnggAMOOOCAAw74XlCzSz6tbR3wh49XRkQNBaMZI8zK0RxmIpWM3oYBQanqZIw4J4TGzx2ViVjyPMt5m24LmFKrSXLBu0AsmaKzQmNqhYizVekzasAomMw0bGmbBU27YpwiOU9VcsWSYiKmQsmFxgXyONAPF5QiLBdrckxYV5NbpyliTSHFCTAEHygqZC2UGIk5oaXcEZG63/r7iMlc40HCamY6/4jdi98lDVcIlin2pJx4/Nn/lvbeW4i1jP2eYh3OeiQsKsHTgg2hJhOLw4XAdt8TU6a6b8udrVSMxeUR0YRvFhSdA4ZymefAq4025UIudaZRtRL7KUFMieVyWdXLklksujoXmzPeO0qeiKnHUBU9KWGeEX1J1OIUyaUG+ey31xhNnN5/RLYNxtQFidvZ1DQmzs8uuN71HJ+cIgrDsOfx40c4W8kz87yn83PHZa5ztrmMWFNrVjBK4aVlOMaJXMo8JzmRUsJ7Twih7qezpLkCZopjfb+5YGSOFPp9pLPOyNbFkNtZ4pwzZj7PL+dLhZzq8fbeM+wndKr27tXpPUQNcXfFB9/+bYwITWiIqXDy6HVCt0KNoaRM17ZkdRRNeG/nvte5JdbW2h8RQ0l7REeMbfChQ1zASL2fjPN0zZJcam+uXxwxAWUaCTU/qSYLz8c2pcTJg7dZbO7hDLRtTREW0boAoYohkqeeMl7juxVFDGoCKoKZ52rLITX3gAMOOOCAAw444IcOr4yIxn5XH0RzQkzBiJ2TbA3G+LtwHBBKmpiGnsV6jbWGfhjp+wHvPEak1lvM4S4iQk6RUiK71FNSom38XeCOkdsMoXlmD8Wo4CwM/RViA5oBLDmX+bW27qNG+t0V0+6MNO1Ybx4wbS8wncU3HVoK3WJDyZGUITOBJJwJpALWepRhVmYrya1zhtRU2dsZTVU03nD+nV9h9+L3MEwo9biI1jTe/e6a5lTvVNWYI+qEpltQkkXyhKKkNGCcYIxDNeNmW+R8CABFitLfPCOP1zTrB4jpZpVOYVZJRWpvaMqRlCNNaDAGvC2cHi9Ydu1smS0Mw56u62gaj+ZCLAXVNHeuKmid2ZxXHVBhtuwWQnDQtVw9+wjdHCHOECVRSrXAXl5dkZKC8aQysB8m1t2CEFqmGFE1xGnEOo9z9m77OaVamyN1jrEm+DY0oaVpW2KMVatLif8fe3/SbEman3div/87ufsZ7hgRGZGZkZlVWVVAYSAAskmKre5Gs7WRzFpSr7Tpjcxkpo2+h76BdtJSZr2Q1GrTZJSx1UaKE0gBJAYChRoyq3KKOe50zvHhHf5avH5vZHVrQcgSFRj8KQuLtIg7HT8ep87zPpO1jq7zlJzp+wPFFpyzpCmScuLq6pLhsOfk+JjVeg0wH3bUSR6dp3ByybUdWm+vH3fTMqpQckacvztMUa0W7N3NSw77S8Qox+ePsUZZ0dOIQh5ZrbacblvUOIzAzeGSm+GKsD7HWIc1jn2aiLn27lqjrNq6y1ryQH/zlDJFfHvE+vQ+NnSg9UChzP9z1uFMoBihzxlrqG3McUC10DQt3q3AWFzjEa05Uc0jz7/6lN3umuAbmK7ZXXyFAs3qnG5zyoP3P2YSz3440PiGzWbzC3rVWbBgwYIFCxb8lcHSmvuXHm+NiHpjMNay212TSaAOaz3WhVrsUyoJ/HoDqiBMY2QYeo6Otqy6DVdXVzWPd7tfWGqxj7GGnBPB+bv1RjP/1zD2xDgS5iIilUroUhoocULE1ZZQ8ay6VVUASyangdSuSEf3iP0Nedyxu35K51b4vMGIA9Ng1TBNcd6wLJQihPaEbr3GhUIqStZEydXaabCUrFAS03Rg21mefvr/4frlD0Fnki7VriuqJK3TKSI1VysitQ5Hq2JpbQASmicoESQgBqwTuO3gvVNjC1oGxv4Cm27I46rGEotFXC0MUq2fJ8bgTLW0jtOAIATv2bQWdN6/FOpER6ktxHl+Hp2tJUxNWM9EONN162oxndLcTlt/dauOvD3i8sULzh+9R8qGq5s9ry92d5MuORe8twTvELH4EBiniSasCE2DDwHrPahQ8oSTUrOLZn488/fy1pJjQotgrZut4tXuGqeJEcM0DgRj7yZzSoo4C/v9DSrQdh2FiMFiTKgHLJK5a4VSUBUKCVGPlUqQmUt/yjxpArfdv4auaTlcvuLs7CF+fUK7OkKma8QZjFH2Vy/ZnrxDTgXNPZoUjSPGO9BM1wXGmx7rPFoKh8NA9q6qrf2eYDKHV8+5uvwp9977PtvtCevgESlgdL7fCrkIUiDYxBhHynRDng5IbOk298FUO/TtZM+wv+Ly6Q847HtuesE5pXMFUmQavmT/+ivG3QXvfOtX2DRrXOiY4m1SesGCBQsWLFiw4N8RRb+5eM8SE3oreHvW3Hl/c71e0/c9xhQKkYxgHdVLObeSlpQQLcRxYEoJawxxHLlJGTOX2hgKeTow7i7Y729wbcfpvXcQrQQOmJtjhSZ0OOvJZazTFlr3E1OcGKc9TXuMGocLjiKm7pBKwWtDDltymujNUy5unlU1VqkEaf46JUWmNCHWYpzDti1htSWJJXTH2LCi5AlKIsaJdrUieMfVyxfsXj4Bt2e6flonQGAmbjqXwBiyXbPant5dR2MMFigpM02RrrEY51HNdcm0VA+9d77mTu2cVxTBWk+KStedkXYDpkRyGWsOtvhq4zXmzjo/68/zlEmm5Mxms8FaS2hctalqJbiUSlpTTvP2qieEhmHY472jDoIKkGtmUASh2paPT0948tnnTGNPsS3X1zc8ffqEX/sbv8o0jrx8+Yq22RDEzUQ3kHOkFMNqtcY4ByIYzRQsWjKqmXHYoQhN11FK4tDvZqV0trKWQsoZay1NCDgH45S4vHpFHCe893jvOUwjLlj2/R7rPY3zoAXRkbjvGYc9TdOCqQTehhUZKLlQ50QzxliKvtlINcbQrrdM/RHXr66YDtc8++LHPPzol9icPWS6MVVhFSH2e3r7mq5dQZqQIlgRnBqygrPKurH044h3Ae9ammB4/eqSPN5AWNOsTzk9OmLdeayOGM2zcl0Ju2h9nlsRShxprBCJqPbE/Q1SRrqTx6jKnM1Vmq5jc/KQ9XrCXF6z292QtebAQ3DE8cDrZ59wffmMk4ff4sH7v8p+2P0CX3kWLFiwYMGCBQsW/EXAWyOiVZmqJLBtG95sQRamsZ/VKak2VgVrhByr6qaiVXXLGS/K/uo1r19+yf76NVYnQhsI23vcu/eoBtuoDaKFgsEBlViVEqvig8W4jlWzRcUhXvCzTdg5ixWt8hmCGIcJDuScOHzANA5sjk7JWqdbVKfa5psyxgneBpzvGMvtGijVFmwNhYntyTGihTLPktw7O+fpj/6EOB64XSiFSiYRIeMJmwesTx/x+mrHer0mhHBHZkrOiHiMaSimzrWkpEz5QFGDUkuhbhtfRQSso9meMeyeQh6w4QjjO8Q1iAlvDgRKVWctBXGGEBrapmYoq71UUeqGaE6FlPSuPVaxCBZU8d7hrGAtTDlijdB0LTEmxnFErMWGwNn5KS+efcWDx9/i4cN7GOt4/eqCi4sLUjxgNoUyJZr1ms1mU9t/i1Ly3FrrHIjBIWgc0DzQhYAaV3PGmuo0jvF11uZuoiYzpREnyjgc6Pt9ze1qnfBp24715ogx1j8bxkTrG/JwydMnP+Fw9RTN4+2dTsJxdPIOzeaY1fF97OZ0noapt2dB7qy5RqA7OkVJPB9HXl68Zsw/4PH77zGOG7RMtX1XM32/Y9V1eOtICs7C4eWPYBpR6zB+RWcaumZN0zpUhKbdsj/s0dCxPb9Xm25LRsgUTfWAhLnVVw1Fqc9lAedanHPkCUQyabjG5J5+X+3muWSsM9x75wOcEc4ejQz9xNXFBfv9BXH3mpQzcRqwYnj99DO29x5Xq/WCBQsWLFiwYMGfBco3aM39Zr7Mgj8b3p41t+kw8xt9kcJ+tyOliPcN1oB3tfTk1avPsdSMozEesQHfrekPr3n9/ClpnJimAUOi8g5Fwpp3P/gOmFr8IxTMPBMSy20DrMG4UPdAjWCNxc5ZPd80VeEsOhfQfM3SKrUhtdCwPvsIN05kDDH25DJRSoJSyArWGQqGfiwkPSA2YVwlIKSIl2pbtbfWXwTXrFjfe5/p6Q4Zr+vHGgumoLnQbB5y/t7foJ8K/XjAz0SwqJKwOBvABJImkjhSzqgIGa0bnNbe2S5LyehcWjRlYfvgl+pcjTiyCZRSCbt3DijVgolFjMO5mhmlFLxzpDSR75p4hZQK/ZRoQ4MxnlQKERDNOFcJtjGGtqkE1IpFrRKN4p3grCOcnTKOB6T0rFbHPHhwTkzK8ekZz59/yf7qmsBEYULTQLda4+yKnASkYJ1BjMXnxOXzH5DGa1x3zObsXUxzRiqQS8aUqlqaea/VGsU4x2F3w9jvsBSMEWJKVRwumVxcPcSwllKUOF7x8rM/or9+gWji9hWtaAGduHz5M8pzuPfe9+g2x+SUQaod/W69RJRcEuI9Zw/e42jTMMXE7ubAMBVOzj9g2L9iGK5x1rDanKDisMYijcPLxOef/h7x+jlFHE3YkovBWIPp1tz/8Nc5uvc+6+0Zxvp6aJD72QZfMChCnVIqubY7l5zqYxBHEcE3a6bDNcIIWuhvnhN8w5QjUgpMhSFPaDEULG235uhb3wYjvP7qp7x8+jPMul7n00cfc3z6gMurq1/cC8+CBQsWLFiwYMGCvxB4ezuipba3emcBYbVqSUmqClMi4zjifUtKlmLqdMY4jYhEUhy5evUUmSasFhpbKNTWUxXh/Xc/xPmGlCc0R7Rk4jSRsuJDW2c6RCrptbO11VVbaS2VMYi1qJkbY2fyeds8qoU6A4LBNy3AXEwjlAJOHOujDUkNYyqV3GEpcURNJXCmZFQyF+OOzWZTraqiSHPE2Ud/m+P73+Zwc0me9ly9/JRh/wRttnz7e/8x7ug+U9ljjUVT4uKrT3BW6E7fw/gOxZBVSaWQi2KcnaOKSspzKRJaFbBSbsVeivGocYBDbIsRxzROABgxNds4E6ycS53jECVmRdWQtczkTpiS0EfD1WHAGaXxHgukOTvqvcfamuE0LmDEIaUSPjE6837l+HjD1fUrjldHrFzh8tXnnJzc5+Tb32I3RPY3e8o0MI4H7Jjp1gEx7ZyhZVbOE1Yj3gt5es3uyY6wvoff3Mf7di40qsVJqFIK7Pc9cYpzOZSiJbMKdf7ksLvGNhuyOKQorTe8+OKH7C++mgux6qEFgMFUxVMqCadkTJkYxwPBtUBAjIf52qoRKBCnieuLC5wzPHz4Dr5Zk2Jm3QacVHLfNIFpnsdJJZJHg2SlDR1JCzHuAYuokIeB55//Wz48Ocevt+RMVdJTi8Y9uQzIPIWE1oMKRBADoqnuPItBbIs2x5QYsTqShmvyZAmhIUvNwoqpUq+WQuyvGA7XFAy+2/Duh9+FODH0O45P70MpbLdHv6BXnQULFixYsGDBXxksZUV/6fH2iGgaabqGApSc8E7Jd4qToiivLq5x4bRaR7uG4zbgjPLii59gSs0d5ikzjCOHccRYx+b0nNXmiJgi01iJaGhatidHKBa4nbVQxNYNRzG3w5kVqormcrdPCrVApvbJFKYpkrMiYrHWMk39TELrduRqtWG1PkKN48Xrq1lRMuSSmeOedMEgFIZhj5F68xsRinHYZku7vk9zL1FK4uiD32Qar9BSMOsHZAoiK46OLE3/lM9+9E8JZ4/Z3HvMmJSUY72OKqRSSP0BubU6Z6WUapk1Ru4edlVJM0WF0HaotBQMxgbiOIJAyhlDxn5tBzMrXO8OWFutn4cps+sjhzGjphYBOTJdI5xvO8R5FAXxqNaCKSMGIxZjMjlDKXMsVQxi56mY8cB4c8nh+Y+INz+lO3rI8dlHnDx8QJxqgVVME87IrOwp1tRyKtWxknxxWGNwquT+JWm4woQOv7lHWN0nIxStii4idXrGKCUXKGCl1PmdHIljTzEeYz3r1QmDqaVTtVjq63d6tdsWrYpyTpE07Uj9FQVFjcc2x4T2CKybLbtCmjI5R9I0cG2Ek/MHHK5fEi9/iikjBQNn38asTkkYYs4M0yVoQo3BKGiO9WYzdQJHiIzjDatuy5Qi3faY7DwZYTwkiim16EvMPINUUDJZq7optsU0a7ah4yYm9PAUGNEMU5rqoYxfkadq4xYKRnQuxIqUdIAyUWKm5IHnX/wxYX3O+bu//Of6WrNgwYIFCxYsWLDgLx7eGhHNOdZmVjI5jaQ4gOaa5UwGtYXr3TUhdJR8TZwChlOKs+z7geHQc3XVk1K1ttpQ38g/ev/bTCkjVlittoitapNadzdXoqXM9tlcW1ULmFJwRvB+Jqqqd629dWoDSlZyLpRbRbTUN+s5ZcZxRBU2mw3B+TubpbUGsZ6CJcdMTJFxHCCDbYSjzdG8yZhxPhC6U0K3etOmWgq+OyHwkDQlpniNyeDmmtnLlz9hc/8e7aPvEUtV7xIWUyqJzXP5TFXoLM4axJu7a3Hb7FpnTWr2No0HXDA4F5hyqdMwKhhTFT7vPVDNunUexKF1vpXQBFx2BKHmNDshWHAm3wqOxJSY4oC3DmsdITQ4b+9aa1NWjDU42+BD4eT8AYoy7C7xbaDp1uj0mt2Ta7qTx6zuPWa1OmKaEqqCs1WB1JxAI8PhAu8VKw7BIKqoGFBHyiPD1c/ob56zPn4fG1ZY24D3lKIUA1OJNMEhZEiR7brlZqgq6en5OZvNhv74jOvndRuzztLYWuskzHM99fr6EADFmIzkiLeC6kCaPLbdYIA8z/iIKNNwQwyO/Y0jBMuYIkEUGzzxcIlvT7Buy2rdcfXkB1jnyGog19xqjBPMueiwPqHdnhK8p2hhHAeasELCpt7jcaCUQi6pPlEFjHEUdRgX8O0RuBYVw9G73+PqaaLsn2K0tiCP08RqfY43HX2/R+cm5ZIjIvqmu9oYcIWU9vQ3T7i4OP1FvewsWLBgwYIFC/7K4BtURJeQ6FvBWy0rSrmQ08jLV88QgZPtcbVkel9nULBYqek1xDLGTCqwOXvIfszYPDANA75tERc4v3+P43sPwFqs9xjjZxWUaq+9+946E8WRHBNGIDhbFcQ0kzOYC3p4s7GY50IlsSQtiKkWRsWy2ZzQdg05J1Ip1X4rQvAWaQJqGswwkfYTohON7zg9OcaKMI6R1dERTbdB3Io3Ix5gFfA6l/xM+MnS765IOuEksH38tzFSiGUu3AFK0dkZKrPttrbRVmJha+ntXRGSYEi1wVYVg0CZiOMVTK4mB1Miz4T+MI3VNessWYH62RQVnA80zrMVT5gSBoOzgrcQnDCON0xZEQzW+NpUC8RhYNJCyZEpZxwgEinGoDbgrcUocwnSgZIcIbQgcNg94RAHQntKaNb4doNYU6dr8g3p8Bo5PANRUuqRWYW1xmGtqRXCRUlx4PDyx6hd0d3/COdP0FxndkzjoWRKAuc8OddN0of379M2K2LMhNAixaCSUeOq+Vl1nkoVjFqKJKyxOONJYmtLdBwxJVOmiRwnfLNB3LqqkqJoSeyvnhOngUePv0uzfYd49TOcOqwBh8MebbFFiWNPWB1Tb+HaopsOrwjtFlXYnr2HdR0xZbCOOEWMRKxxSFhhXcApiCiSenaXX5Jyplud4Ndbim8pt9Mz9ojj93+N6y8gXn7FqrFMEsBtcN2GkxPh5vUzpv1r7LypWqjt0mILFoOqp1mdsDl558/xlWbBggULFixYsGDBX0S8NSKqWFIREMfp+QO8qYVEWpQpGfoh47zHOkvWqpZBfVPfbc743q+8RwiGw+4FKY2kUhBx1U5rHGgtoBHMPH9SUfcOzTzVYSlzG2xGsd7MlluhqDL1w3zQUm21xs7FMLd7paa2zzZA2zYULaRU/1xzqnnReeM0C7TeEK0SupaH5/cwQD8OdOsjQrvC2AaZrZn/neulihbwUkhxIEcQVUTWjONuVjPT3O5rqUzUoHqb67y9BuZOAb19JuRWGZ3riCpJr9VDWrdGyCliLXjvESOUXEil0K02WOcZY82eIkLTNDgX6m6rKFoiMY7kVDDG0q02hKad87iFOA3ENJLjhKqyPxzQbgVUG7HzHm8d29MH6HRFTj3FOVzYEExDtp6UMmPaca9dA0K8+AITEmW8wZRdJY8l12IjhTiTNYzD+GZ++AntL7h8bjl6tMaIwzjPeOiZxom2bVmvj4k5s11t6boN05TqpJAL9V+T6txwPDcS3+7gSr0PFAO2wTVromTS2ONyqtZhhD5FJCS6dovYFdvTdynTDVeXL5kePKY7ecR+uqaUgZL62tIsFs0jcRgoU8GGMN/DQtOdUUyg255wfHafMu6gFIwNeNNgtNTnSQ2ZesDixDLuXnDxxR9AjLwSh9884Oj8Q1b33ofmGJWEdR3Hj3+Dlwo3/TVHR+9gmi34FcY4Th+t2V1sOLz6HKd7sigK9RDCBNQ3tNv7+NX6G3xlWbBgwYIFCxb8tcCyI/qXHm+NiP7g9/4xH370XbrVBuct3rcY70mpYBFS7vFNg/UGT800qkKaEjmN+BNHKbVYx5hE4z1N2CJSFdDZZYqY283SitqCW4mo9S0xjmjO9WuXys6sqWU/KpaUa7OtsR7rHM6G2kSrWsuCSgFRxmkgpYlSMs7OjbvM2Ueo+cvY0zplc3RE4wPDMNGsj2hW22qnVHOr3/4cGdVb24EIqSgFwYcGLbkW9aSMM1I3U4siRlFctcKWVDcrSYjUHVVR6sfefYc6WVIJU830CVKzpFZom8AwRuI0sWq7qgqWQhsCIQR8aAitkAtzblRqU6vWjGCKiX3fE0Jgs9kSmhbrPEaUHMe692lMff7jhHMe6xoAvDGoQlZoju7zaLPl4vmnxGlP7TSyYDxiLCUVYprQ/Uv2rz9j8/4H9IeRNB5IOVWR2Oicba/k0PkO1x0zxQTjgUSmWW3BWIoIFGFzfEqKEUVwqy3eeoxtyEURZ2f1e0PTnTLtX0BJYN3PPY+3BxrGWtR5DEeVAGZF04RoRNMBjFKKB3NEaI+Y+kua5pTVRjn0Paf33sUdvcd09QUOpaSeIMJu95ppf42iHMaBrmtxboXS8uC9DwirjjhcMw0H4nSAHCkYxHh8d0xY38c0R/Uoooz0/R6MRSWDEXL/ktefP+f10z9gdfZtTh/9EtkHSlbOH32XXEotiTJCVkGxRLV0J+/hfcfV859R4g3GpGrotgFjV5RcuH752Tf62rJgwYIFCxYs+GsArRG5b+xrLfiF460R0Vdf/gHPPvu3eL9mc3TG+cMPOD5/yPrkFOM6MIZutUEErIx1I7MkDoce5xxiM2I9Tdty8fqK9XoLajDyRjmtN2gtIrolBGb+3c6WTBGHUlXRNMVKVK1FpM6U+FAVREXJWYGq/BlTK3FEailMSmMlQapIqCRLxVWlLSdSTFXJy4mr60tULJvNKdK0NU9YfYs/f5Hka/+h1RYcp1j/0zq8q/bUkOp25hQnlInVakNouloQk6evzSwphTI3wQI6f0tRSqn5Suv8nG00WCu1WCgn7O3opSoxjVgb8L4B6tYkIlhXrbYGU6+RKpondsMeYy2+bfFNh1gDmklxAo2I5pqtFAGF1XqDEU8tLK79xAVQ2xBty/a9X+Owe8G0vwG1ZJWqDhthGAamq+eY0CHNGZhrUnlZDwIqz55tyYK4FbY5p9u+Sx4Gsr7GhEBz/G4t5xEwNlRbqViKChmLtQ02rGvLb717UIH3f+nv8uWPfo/h6ilGc526UamEdr4uNS9swLQYJ9hQSOUSyRGhICRSHIhxZL05pj9ckYynO34ECOMQCdv75GlP3F8h4wETB/rrl6S4Q42nW62JpVBwnD18RFityNNESQNSIk1oUFpyipQcycMrsjV43yK+o0yR1fodNqsjUooMUwQb5sZpQz+NjMOADD2qqSrAWjBuT3ErrGtIc77U+Ibm5BH3VseMF0/YXT+lpAO5ZMLqmP7mkieff8Lf/oZfXxYsWLBgwYIFCxb8xcbbKyvSETGekgeuXz/j6vKah9+Cj8/eIRWtxMW3WOPJzhPjyMXFa1LOtN0K1JFjYRoHnA0422KNw1tLigduri5xzZrV9l5VNKWSxtruau6mKXJMTDnjb9c7BGJSQuPvpj/q3qbc2YNVM6VkgrfE4YY07chxIviADS2pZCwZ5y1pOqD7S5yskBBIJdBttqzWJzi/qtMh1FzebeD6TkWrvIzbvyo5U+JESRMRhwsOH1ZsXYMC49gzDod5lsZjnGOKB8gC1EIhBLKmSrLmHGqmElErFmtqBpZy2/KbEXV443HeI86SyoT3G5xvMCbUTCZv2matsVVVLTD2A8Y4VkdbrBhKrKSzlEzJI6X0UAqihZwLRuxsma0XQDBkTThXf9qYa/FSs75P050xTSNjisSYybnQjyMn73yXtmmIAs3xQ8bDUyyRIlVdFevxocP6LQXLMCYwHWb1DpvNCb47JuW6ocmcBUbrDE6MCTGF6GYrMtQbx3fI9h0ef//v8OQH/5LD9XOUjJpS1VeddzkxaKmzN2od4ju8TqR+V2dTUgKZSNOBUS2r9THTdKiFUApTnPDths29D3k9/IRp6DH9DTYNGKNk4zHtCaenDzl7/2PUwNWzn3K87khTIQ4JZxzGeowNdSc0T1ASff+ao+59YrL0caBxhm5zQiMNGegPN+yur2hWJ1hjGfsrHBMWKCXSX1+T1dBuHyDNKUrDOE7onJP1x485PXnI/uoZOQ00qxN8d8ph/EW96ixYsGDBggUL/srgjdLyzXytBb9wvDUi2m3vMfQRaDB2xQff+00++v5v1IzoOGKMIfgAGKwNqBZWqzUxxjettiKs1lu61RpUyLlwefkczSPjOKBi72ytpdR84pRKnXaJiZQzu/2BNB24f3o0z35I3RlFa65QM6KFKSaKy6QYcc7WNlxGhv1L0AlRmIaRbdPQWsvN1VPIPV998idcv3rKux//PR79yt9js32Aa1cYG1Cxs71Ua9GQ6JxhfWPnlLt8Z2Iab+j7K0qesC6gudA0zV32sG0M1EJgxnHEmNm6St0orU28WsmlVHXUoPPEhpLySAG67hjnPIdhQEWxPuBsmK9jZNWuaZsNKZeqmhYlayVcxjlE6wRISQem/gZxgTQdwFqyKLkIpiiaIqmMOCfzPIpQ4sDl9SXdakPTdqjz9SBAq/YoYoi5YG3AOEPTbGhmBVYLpJIRY5g0YwAftkh7jPYZZ0DF03ZrEEdByCWSpwN+dUY0Dc3mnCIOoVp5S07EFKvSO+eOrfM46+7KoYyZCalzSBN4/Kv/AU8/+yE3rz8nj9dIiTUrqrc2cVsfixHUNGQCWW0lqyKklLGlsDk6od9fYZ2vz3FRRJUcM2oCm3sfMA09EjpkfUJ7+gEPPvg+7em7mHZFKoIzBeO3HPZDLX2SAFqt5zUPXCd6KInYv2YazvBNi+yrLTemAWMS1jrWq5bU70hjT7daEcuI5BtKiWhOhJLoh54BoQsbMKEeLEg9WxhzJE4jTXvGOngO/UBxKx5+9+/+gl51FixYsGDBggULFvxFwVsjov/ef/I/ZxzHOytstzrBGM8w3HB1fcl2e4x1lpKFkmu3q/eBaZpqnnDOeU5Trm/qAec9rgRiyazWJ5zef0QRTymFvu8Zp8QwJYzz9Q0ylYCt1x2QcQIpxUoGcp3cMFpQLXOrbv29FEWsQJrQ2CMyUlmeZep3XO2uEFW+/Om/4fKrP8YYi5Jo1+f47ghM3dysCiJV+ZNbNVTviKjMKppqZhx23Fy/JMUDOSVyzqC1LMmaWtJUSKSUCN7XayQG0QZvDc7ZmdiCGqnqXI5oyeQS0XIgM5GS0ihIqRlYZy1ODHG64fWrJ2gaODp7TPfeKcY7cs6oqbbcqmBWm2xOmWF3zbj/nOA6Us7EPJLyAAg2dLTdEYhnSjD1O64vXvP0yy+Iw0DwHt91hG7Fh9/5ZbKpGVTrA027xrhAmRVYYwwiBhRs3dlB00hJE0Ud7eZBzZHmEUwt6qn9wIIVOMQDsW9pTx4hthJ8QaqCOmdlrXOIWLwPhNCAtdwmj2spFjXzWgzl6B0eff+Ud/pfYvf6Kfur50yHHfuLlxjX1SZn0bpNi6+/jKtEvJS5rblQshKaDc4FGu8pcULSATm8ZIwjYh2r7pTQHtO+d8TJe9/D+g1qPcwHDKjh/sOPuX79JcPhiqIWq2Y2JytGDZh635dSD3O6sMI1KxoTKWlC84TRDLbj6N4jFENKPeQ95D2ax3pWUgzGBWyzQoy7O1wxxoIRvNRfcRq4uLjE+IbQtZUIL1iwYMGCBQsW/Fmg32BZ0Tc2A7Pgz4K3RkQxDevNCustRQ3MCtkwDKSYGPqetlmBsVAE6xwmW8RattstUxzJKWGswVqHEUOKGTGerJGcqMVFUu2MU0oYawlNtXgWFYwKrTNsG4uXQp52TMMNptvgXIdQyQcqZMk0LlDI5LnUyDtfs40lznZd0HLEyYMPkVz49Ie/h/VVybPOYkNLMZV8Vsp2Owdza8fVO7UXrY2xIoU8HRhuXpOHni40SGiJKaMlMo170ISIZRpHbOVmWKk0yToPaiiaef7sGf2h5/TsPtvjY8TVWZKgBXxAy1Rzj2ViODynxJo9fb27rKVCJFRhXJ+y31/gXYvYQEqF/pDpum3t6ClCvH7O7tN/xZef/xOCCYjWFl9rqiVUV6c8+vhv4kLL7vI1L578FFQ53qxJjSenjOZIf/2Sz3/8A87uPWR7fo9Cg50JqBhXH+zXorUiBjGCuMLVzRWvb15xtl7h3AQURKoSrnOLcAba9gzpTrC+IaeI5nr4gGZSjmQtkMF7S9EM6N0Byu1YqDITYSmIyahRYu5Y33uPzckph8vn3Hv4PuHoDGeUSQURixFfdzxzIsc9RalZ25LJJRHaFSV7hILRHbvLz5Hck/OAGBhvAkUdvl2zPvuI4DaknBimoeZ9rcO4huMHHyNSiHEgTeO8M1tAtVqtUZwNGL9iytRrMY5YqpW75IgpBes6Eg4puRLMUq3cxjmM7ZCwRcJxVfuROmeUI6FpQDzG2Fr85XNtLLYBFf+Lec1ZsGDBggULFixY8BcGb29H1FFVsmHA4Ai+lsIcbbd4ZxjHif1+R7feYpyFrBhrOD46wgfPOCZq1HO2sVLJnRGh61ZcXFxwc3ON3DbxGlOVSc2kktFcSV7rEpJHjDNMqUfLQBwK0hREPGApWZnyBKEjphFxDd3qGEokjnskX81Ko8M5ncmQxTctB1FMyVw/+4SyfwXGYv3q51txf/7K3BEENBOnnmF3SYkDrfPYuRU4OKFIXWsZxwNxTBhjaZoGLWUm+BmZdbsYqxJ8/52T2lQc6txMjgOqCVv27F59Rhl2xMMlOU+sVsfkYjCphkunXKA5YXN0n3y44NBf0p49RuwGK4Y0DaCWEicun/wpTz/75wQz4aXFGIfYSr5zKVgR8rjnyedP2F+9xhipxMZWq6h1cxGUFso08sWnP6J9+YwPvvsr2JVHSiWDKmbO15ZaPlVmK3PJNN7RHB+BZnI4QsWS454ce5zJWOtIOeFawYY1qqa242pNvOYSKSXjg8caM+8mlzdzN7O9tZYqlUpu1VaDd87o1WsOFz/j5uJz8jBhnEf9J6y299m8822KX2OMRQiIbdHY44PDFEfUMhdJQcwRSEyHKxovGOtrY6+pDcglJ1zcsX/6hwyHS47f/bVqkZ6dA3XD02CsAdNggsPMGWEQdOqJ48BNP3HSVHLtQsPuamTlq0qvAkUTmiPiHIhFbQsoxoOIRyXgmg3ZdvNhT0GMIafMOAw0jaGSeO4KxIxYshgWLFiwYMGCBQv+TFD95pTMRRF9K3h7O6KHK1bbc8Yhksae690VwQdCE1itAj44cpGa9cwZ1YIPHpGa95TZxnhr0zVSt0adqzuc95yrb55zmnOfjlrumdA44YzQ+sw0XM+drC3rVUvXeKaYiHEi5xHnA9ZYvFXG4YLd/obtycNqSdWMCWtIVHustagEclasaXn0+FcY+leYMjANey6efsLaNrRrg9i2anL/nRtfKTmR00RJPcPhhhx7jKm5y1t4Hwhth7WGnDN7OTAMA8+fP+fs7Iy2bTEis7paMJq4d3aGisMFj7UecqKUCR1ecvniT6F/SeMDrmnw4bwST1WmvCOJ4+TBd9jce4+iDqYrUtwhRNI0YK2l6Sw316+xZeLm2Y/wZCxbjHigYNUSVWi2D/Dr+/z4T3+EQfBi5umcqgpbO5f7YDDGI1LoWodOmWc/+zHvfefXKbkQutupnoLM+52qGc21IRYmnJsPIAgYTmk0kac9w/6CKQ6kUhiHnuPjQtEyNwnX+yUNIyklrLUYYyi5HmggdSrndjNU5lmWrAWNIxJvuP7qj7j64t8yXb+klBFnOyQ0mJwZxyvazSm6DfNOqwVb7bnjOCBW6v2fM5LineBrrAWdVUUCIrczQgUrBpN7hv1X6PghvjmtbcwzaVYrs1XWYp2DUluWrTU412CNZd/XfO7R9phgGoxvSDrgxQD1+mgeMS5gfIf3AahTPYIFnRVqLKKKA3Iu9VqmRN8f8N4iRhHD3XVNealMX7BgwYIFCxYs+OuGt0ZEd0//LV6+x2FINV8m0Pd7Doc6exLaDdY1GGPJmfrmf1YLzZw7U1VSyohkxAqqhZjre+GshRQTzrs5K1m3hlI8oDnWhGAplOmAbxss9efIqaA50/gVxZtq6x0HnIGSeigTjXNYA0U8J49+laKKE1O3IIswxQFM5vjBh5zd3FDKQBsaxhyRm9eUXGjXp1Uh+1qZUk4Zo9XqO417hv4GNGGtwYjOxUPgva8NvrleOKNC13Z452jblhA849jjnMV7xzRMXL9+wfn9h8SSq80Sg9NCnG4YXn+CnS4wziPhCGs7muaEq5dPmfLExIbzd79HszknKXgnxNJwfP9jjGlRSm1RPRwwFNJwTdw9pzFNzVxKRslkMWzufY+zD3+TMSrhekSnA9YGnLWUXMg5s+sPlfz7pmZbxdcGZQFy5HDzGhNWYAzWNah5k03VEslpqITJKM4bkALFoKXuWxK2NH5DI3VPVLV+b5WaC7bOoaqM4/CGhBZlmibGKaJ4jAs45/C+2kpTymgpxKsvefmT32F4/hOsxNq4WzLYSE4KqWZqRaXea6aS7qKC2ABppBRFU8IUJaeCtTU/mmwAurnQCoxEcoqInXOq2eHSwOHVT1m/01Cs5fnTrxiHHi3KerOlaTqcC9jQ4a1DtJCyomI4Pj3n5uaS/X4PTUu33jLcHPB6ewCiIIVcEjYEjHHg/Ew+HVLMrLLOzdIp1nztLeFMiZRi3d0dB1arFp1LrhYsWLBgwYIFC/5MWBTRv/R4a0R0GPfcXD4jSYeYwHp1RMrT3NxaiNOhtofaBt/WN6z9ODKOI9YajrZbnAukaaJIJGndGb2Vj5SqhqpRjGtJcaJoxkohxgM5KQlH40MtjpFcS2o0k1JBfC2o6WzgUG7o+2vysMe4FnGelGvW0FpHt2oxAqXUzVEH5BIJXcfRg8e8evklk23w7RarmcPVC3Qc2GxPSCTG3QtE58Id5yglkfpr4s1rbg6vuf/wexTTYkVo2w5jhEJBgTQP8BoBb2f7aJ6gwDRM5OTo2jX3Hn1EyRF0IE+CmogViP0ryniJ9w2+OWK1vU/fT3z56glXu57NyQMePvqQ0J3WXK0IbQhYgUKhzO20WmLNwoqpRUTbe9C/nklGxGhge/YxRx/+DUpY4V3k4fsf8tkP/7hao40DLEYcTbCkmJiGyKgTNjhAkKYlJeXll1/x/sffI8UDw7ina6uttpRE0VqsYxCMuJonndc+q0u5kvmsoKVaqEspQEGJ5FRIaUREsQJNaBDjsNbTNBtuK2BzzhzGARGh8Y7cXzLtnnH5s9+lXH8GZWAqipgGEHLSak8Wi7iAXW3pY1/VeuNQFYoJ4Fd1f1Ug50jXrdCiDNOI9StsaFBNeO9xVpmGHi0ZYwwORYcLct4z3jzjcn/g4tUTGt/gXcf+4sBlyrMzwGKdp1tvOL13n2a1AdNxukn0168otmBsVVBjyfOO7DwcnRM5DtjmCEq1iCuZPOzI/WvadoP4Nc7PBxGq9H2PasFYQ4wRax1xGlARxCxlRQsWLFiwYMGCBX/d8NaIqF+d0U/C+f37GN9gjVD6jEalIPgQcL4FqXbFvu+Baps0xlQrqK9totM0kmKs0xhSy2Ru1VItircBa5TGGowqsWTaVYdYixWH6kRMBdVIigljO4z1NesnhvXmiKbriMOBdrUF69CiOGfrxEspOO9J5XaGpe6V5gJn9x6xOToDcbTdETn1lDIxjDeM055p94w//lf/FUedpw0bskBJSooj4+GCPu14ef+X+c6v/X1WZ+/j3EyclMo+ZxQEMRbjPDknhIKWQo6RgbnAB6VyrkRJGayha1ZkYymaOIzXvN7tuNlnVkf3+eiXfgXfbsE4wGA0k8Y9Gk4wxtZiJMqsKmbyrVXVd5w+/GVeffo7WB1I0nLy7i/jT95HfYfmjJDYbDo22xUXT55hjIc5V9t1a6yx5FKIMZJTou9viHlEDJRcuHz1gtN3HnJ1fQma8a6pDa1S/95bmXOnVc1Tc6vU/beKoqiTNs7UNuRSUqWu1rJerWsRjwnVdj3vVZVSuDWTTtNEjgPXX/wp/Zf/ChefUogUrSTUWjer+YZCnajRUrACGsfanuznfVNxILWYqP5gmZLrdEolsW5+/CNjAlzArVdoTvPe7YjvTrEKUxowkuiapjoC5kMC8XYmotWqe3N9zaEfePzhRxjpuX7yE376x7/L5uScB+8/ZrU5IiVAEqYIAhSN5GEPOCSswdZG6ZJGxoun5GYLqzPa7Tl2Vv1XqxWaA+M44qwhaXUr5BTvGqMXLFiwYMGCBQv+nbEoon/p8daIaGjeoVsd4V1bi3FRrAvYxmBudyHF0I894zDeWSC9d6zajmZuBI2pFsrU2YsCJd3ZXSmZ1gfQjLPw+tVzcs507ZrQbjAWUhygFHKZSCly2PdsT47JCkM/0HUbgvcE61mtj2nalkPfE+M451OFNE04Y2hCYL/fMU0j3oc5t+oJRxuQgALW1rbUFBO5FFLK9Nevide7WgBDbby1RlFTJ1wun/yQH2bDr/yd/5TT03fqpqbqXXawPl6Lzk2uYjxqcrXaiqm7qKJM46GW4wiUPP99XnFIFrRQTKDdPuDROw8JzRrxFhFfc5IMxP0Vl1fPabqW0K4hp7mgpwplRoSskSKB1fljxtdP6Q9PODp+l83xAy5ffgLlCnFrnGtZNR3f+vAxXpQXTy9q+Y/Ux2ZMbUg2jccaS4wTxle77q06fm4sJ0dHxJIR5hIhrdlD5y3OGpy53WStNmYRS1Ylj/HuXjS2klURRXPCOIuWRI5Q1KBErA8gZi5/uqOKcxkQHL/zMS5e0D+fKNOrStZSohAJTUeMtXwol0zRxDhFhFK3WJ1H5xZdNb7ew7mQ4kTKVT0EmRV4e1f0MwwDIoqzBm8FsR7N9WNNzqy7wLA7IGSsZFQAdQgGtXPOtYrovHj+jPVmxbMvP0eHHS8+ecaTT3/A0b0HfPfXfwu72pBTRkptEb45XJOvD5w8eIzxoeZq2w3dw19GUKacubm5psaMla7taJpA07SklEAMKce582n883uhWbBgwYIFCxb81URRqsLyDX2tBb9wvDUierw54mZ3ydXFzyhlpMSMCRtOH30L69eM08Ruv0eBtutQrVm5tmkQgb7vsUZIJaEwF6coMeZq1y2ZzeYY7zylJHaHG4ZpZLM5ol0d4ZsVRSMaEzlPiFItg97TdluKWF68vKRbbfAhVHJsDMM41pyb1uIaLZmclP6QyaEBrZm4nDJZpGZXc530EFMJRGhaxEzoNOK8pxRDspYqdxWMEVQyxniMbCilwYQGHywqOs+92LvZF0GqmoapcT0U9VKtqjlTEKzm+bCntq5iqkLXNMdsHv461lp8swVbr6NmUI219Kdk9rvX5OEKR5oLaDwxj6gYMgUxFqg5XZVCso6z7/5N8jQQd8949eTfkMdLxv2PUQlY4/G+Hgi8c7rhZPuQi8sdV7ue3X4g+JZ2s2W1XmOdx9ia3RTjiClzdHKOqsHagLWFXCrRK6qsuhZrBWtq+RGAqpl/bkMpmUpK684luaAo3loIDmtq4/A43MzE1zKmCTF+VsrnwiIzP17NRGPZfvDr7K5fMh72FLWocagGyA1FFCMGxbI6eRffdEwFcuorGTW+arTG1wOGkuZirsw41kMPa2A4XCFlwALOat3TTUoUUDFYCWAc1gRSUtp2w3B4RdGESMAag1hPLiAq9b6ei78O48Tpux9x8eorkuwg73n59BMOw8i/99v/Y2wQxumGrMrRyTE3+7H+f0BKoErjA4rn5vqKIlKJuwhFC/v9jv2+Fhd1bYeZK4G1pFmdXrBgwYIFCxYsWPDXCW+NiP7oT/8ROdUSIGs81gaYIvAxY8oc+mEmGRaxNZtobbVPOmdJWotQMNTflVpO4wJOLMYJIXRQhN1uT9HCdnufplvhmw7jPFYC3jaMvaOkA2l3QdOtEdPgrOHx48eI1DxgFhAtc4GNwdpKGkUTqMGagFCbdKX+MGAMOpPG27UWncmidR5iZEgjkUJnPVY9KrUJWEVQOcZtHnF+/wMePv4OpUBMEetabnOPyjxhIxbB3BXZqGgdyQQ0K0UhxkO1Q2LAeCBRcAyTpWlbumZDzgVNI6LKNO7RNBCHPZp7jIyoQNutEOPwTUucJkpWVquOaYoUCqKzuug74v41Ny8/IRQlN0cY61AxKAmRSE47Sq7lQqfbNadn99gPsDtEXOto122dIbGmqpIY1mFNaFrQ2WA7K8giUoukrK3NuzLTUK3EPmvBUDdVxdSNVM11SsbMX8MEV1t4s+KkkOMeZxsES1FlGiawVd1G6lSMiGJEkOaY93/1P2G4foEYg2+6mkGdd0ettcSU8O2WIg4bWta+ZkhLKZSc5+Iiy5QjofEIs+21aN0fHW7I/SsaMtmUOhWERaxDXINtV6htUGlQDNuTE3bXL7ElYl3BurrlKgKx6JyTNRgBHSdsd8z5u9/m2ScH4rRDVNisjzBhjZZEpEWsgbBl5Y4pYsnThBHYT/uqZntbr6mxNfcM5JzBKDlnrm6ua0GSFkpOrNrmF/jKs2DBggULFiz4q4Fv0Jq7HIq/Fbw1ImriAUrB25bgmjpf0XQ47xmGSNM0d29kxVSCpVo3CLWANZbbNRPrAFWMJPI0gUSsGCgRxLNab/FNi2tWc0awQClYHxDjKKUwUmg354gYUsm16VQghNqOilhijKhSrYWqFK0NvlBIcSSlqlzVn7VSRSvCGxaqX3scNb94dPKI1ekHDK/+lCaAisPIlrY75qPv/y3OHn1ENZ7WIqSSEt7XAp5yK/jJ7dyJzvukUpUqnTc1dWTaP+Xm1eecnj/GhCOKVjNrn3pAcK5aidUoh/4GQySPe+JwTRMsQiEOGcTgpBCH4VbAxRhL3w/1+xudr41QijIedjhbt1MREONAHGrrdbJiqiJpLEk8WYXN6Rn3H58zxIFx6ik5YTTU+RoRxNSZFhF797pxS/SMqzM6zAU6tQRK5udFGYa+5kBNvQ+ck/k5mXdIZyIvBsQ7imZKiZQ04ZtazDPFhHWmKpyq9fGZ+vxIt2XdbtGiUCa0TLURuSgxFZxfManDYTDW0YaAM7XAJ6ZEjJlSUj1QsWEmclIPX+JE1x0zph7JNxgDzghCoZSBMo704zXFevzqPqY9Qayn25xwePWE4C2S+3rtjMMUQA2aDFocYj2xhwfvfY9Xz74iDld4FWyzRk0gI7j2GOcDxgamMTEOw51DYN11OFP/fdz+WxXR2WoNisx5V48rStJMjsK+P/wiX3oWLFiwYMGCBQsW/AXAWyOiuUwgBusb1G9YnT3Erc/oY6oWWVN3Qb33lbzMEOq0RSk6l85UHmZFORwOxP6Cabgil8zx2Qe063MqX8mkNIAWNPWUNHJ0+oAUCzEOxJJZHT1gOBzY93s2qy1IVXCstYCSUrVL5pzrNqfM+UxV8jw9oqr40GBsoJDQUhBTP66+ITdzkVLNKIoJ/Orf/I/53X/0khIajs7e5933vsf9hx/QrTvECiVnxmGgkJkmRYzgwxpjmqqJSr0yMKu3pZA1kYcrGF5xuPyK/uJTcn/J81c/5ejd73N8/2MUz5Qzx8endN2qFh2NibG/Yepf0VjwkinTiNGElJFcEpcvfobYDRGLD+2ch3UodZ7j7jq4htXp++zJ9LsXGO2xWIyEO1VWbUC8x69OCL5BbIMNTbXYth1N2eIAN5c/xVyVzZwT1s53xExGa1Z2bskVRdXMDbdvdmerGgp1M6iWWd3am2/Li+ZKHip1KlgD2AJlpA0NjfHIvFt7a6lNOSJkUJljBplCxJiCw5I1k2MmloHQOYx4VttjjBhKylgsYgtGIjFOeB/miRY7559rZlr8mvXRO8SDwbp62CGlzv5oUZRCBBAlpYmpCPffeZ/n4wEtI2iGPCBaiWhOWm3V6ijZIa6lJ/HO+x/zZX9JGa6xzldyr/XfQpxgmKptPpc5R5sL+31P17qam825FnkJmNmSXjO28yGNEbxajHX005u87oIFCxYsWLBgwb8TlrKiv/R4a0R0dXwfGza0q4dsTx9DaImlltSEMMw5PiXHSNOGmg8UQymFISWyFrw4jAhGlZwGDvtXWCkYY/HNiqZbc9jvePnsKY+/9S28NUzjFbuLZ6AT/eGCplnhfUtjaklS17YUW62utw2sfT+QqXlQa6qNE6NYUQRFiuCspxQ7kyFlHPaoOpzzGOvmRlbF2vr3qrDf9/gQWJ8+5m/+9n/Oan3E0ckpqomcBzIJidW+qGnkMPY07ZqSK+Hr1qcghlIqESo5V2vtTIT2F18wvPwxPvd4HVBuGG9e8OKTVzhxrO99hPemqqGuNqvm4RJN1wSniBY0R6QkprgDEqYU+usnqDvn6OwRYoRpODAOI2McqnXZOQShCS1Hx2es73+HuLnP5Ys/xanQtid0mzN8u6YUU9uRbS2jsgI+OMQYQHFisTORjzGiw0gaD+CEUnJVDFUx1pBLhjiRc5oVUOZDgupRrm3CiimRIopoVb5vM596m6/VavW+paZi6vJLjCMlay0RtkLTerpuRYyJmIUUI7nUe6LkRIoTziqiE6IZK4lhiKhAt25qU3CtWK5zMYAJig49lxev2W42mNDijME5Qzpck0vGhQ4pR3MeN6N5wlAwmskooraql74SSxM6Tu6/z9WLz5A83G4bYTEYWwuUVEGMUGJPzJZ3PviYm5sLbp7+iJurV6Q4oCYw9GOdr51nbOb6ZlQsY450VLdCsbyZUxK5U3YruReyKiUX0jSw3+1+Aa84CxYsWLBgwYIFC/4i4a0R0fNv/YdgDGJaIp7gHHVNULDOkvJIjpGUM14zVt/kLK3zxBRJuWCcATK73WV942sK1rest/c49CNf/OynaIqUODBOl1xffIU1Ft80SD4QDwfGUjDWohJo2zXN6oRiKllEhP1+YHfoWa1aTo+PiNM8DyK1LlaMxRh7194LYIynHyPj2GN0zrEijGPBWoP3nqYJdF2Hc46j7ceoQCmFOMY6sZKrTbOkRE4TgnJzfcX2+JQcB8bDFWIsY0xghFwyRhwhtOSSaZqGaEByppCIWqdxyvSKz3/4D3lf/z7N6YekvEfHPa219FdfItMNRur8i5KrxVlHUs6YcIxrz1idPEIRXjz/Ys7Rlqpwm9p5bK3Bu/n2EoeGNe3591h5jw0bCgLOEGwlY03XEZytcx6z5doABplLiArWCMEZSrZMccKHpqrM8ycopSqTaVY2RX5uGqSUUnOf4w6jEbGzxVkrUbr9yGq5Nhhjbz8RLQlrhVQy1tZrnXOk73dVGZwV8VIKpZQ5c+rRkuvBQolMcQJjwRhC02DEUbT8HFlTVUKz4vS0WorX2021i6cRBFLOkAvWdeSpr18ORym5WtmNxagnrI8wrm70xpgwzZr10T32r7/CaEaMnS3JszVZHIowTpHNyQnrkzO+/at/ix9NPTElpnHAt/V6F32jHZtZfTamThn1hxuGlDg6v0cq7u6aihGyVsU463xP58wnn33G/+O//if8Z/+r//U39tqyYMGCBQsWLPhrgJpD++a+1oJfON4aEbU2VCUKQDLWddy+vc25FtAUrZMs0zSgqljjEFMnUVarzZzrGzkcrumH/VyAKrTbYw6TMhz2QFVUSx549fSHWD3Qbe+h2SKqiBS01KmNnAoSt6RpoDt6iLiGokJWkHm7tBKNTJzJiqjBe4diMNbifFVtY0yIyVVtyqlmXef8oZnbVruuu7PrDsNQ1S5fM6uaMpojaJqVpXKXL7XWYowwDbtqjTWGbISYCtYpOUeQAiqs12uGy2tUDd6f0DSOlCfG/gUvfvoveXd1jPhjhESabsj71xjt0ZJrHZIUSqlTM5mO5ugxq5PHqA1cXz6tap9xGHtrkZU7G7J1t2U1irWebn1ac5WmbnR65+btV4ACojhnalnQHEC/VSerklb/3iUDWok/YhAzK9Fya6vlzlJdZ1u4m9pBlZwS4+EVwdcGWZF6kKDG1skdzPzc1oytaqmWbgRjDd5Z8lSLqVLKM4Gkks1xIhelbVqsaxCt94JxDdNhYr3e4Jv1rKDnOddrqHOnNetbcv0c1UJoW7z3c95XMMajWjDGkU0ka673na0Z2Iyg2XLoYYx7Li++Yuh3nJwcc//0lG5zj/7meVWGqZe22rtrIVLOibZt6/bn0Rkf//rf5Yd//Ic459ES37QyUwvEVKizQ6qAR6cDL776Kb5twTVvmp3LfCiglnHYc3114NOffs4//p3f5U9+8uWf/wvOggULFixYsGDBgr9QeGtE9PmX/5p7Dz7Gt/dQG8CYWYUyMzkpoJkcY53isAbnLMbUbUwEcpoYDj0Xl68wRIwK7XrL9uQdrq6uGYYDXWixNjAOI227hpQpOc4qW65qF4WSlRA6vM1c33xFtz0F9RQ1ZFWsqSpmKQWxFhUDYnG26rg195mxtpb3GAttZ4ilILaqTt75mnm9fXMuhsOhZ4qRvu9RgVze2GHRhJaqimop5Kx47wihTnMM40TTrVExDIcDYmsjrJZKL0Jo6FVwrmqLzm1rW26O5DQyDRek3RXd+SklR/YXTzBlT56JYC11SogqiZbV+XfYnH6IGkuMA20IlF7r95Pb+ZZ6omStZZomYrqppTxdSylCsZbGOZomUFlKmQmKVgV2zt6ipZb/3JUR1cckWlXocRhQAe8biszK5bwHevv958+cZ1DqY8oKbnWENYVx9wIbx7kMy4ALeN9ixEMWipT556sE14hDxTGMEevqvXCbHy6aMQJiZmsuCcpcXGUaxCjd2mFdSwjdrCDOkz6zVVu1Hlz0w55xHNhuNndN0XGaMMaRZCK45m6axlnFKDjr2Q0Dz1++ZkzgXMNm1ZFiRkvhyRefsb/p+aXvfEjMAyWNlYTOfVZVza+26BxztWWrsj455Vd/42/XS1ni3X1ubJ0LurUpGGMATxaLdfVwAOPIearTM9by4vkFP/38Bf/iX/8zfvjjL3n66sCYUi30WrBgwYIFCxYs+LNgFqy+qa+14BePt0ZEtX/Bsy9G3vngt3DdKTkmxAcwFiMGN5fFGGOgSM1JGluJgXVQItOwI097ckz185yj257jmi3W9ATn63xGnHh9ecm3v/N9rl7+DI1XoBmZSVtmxPg1vt2yH15VRU4CsShjHNGS8c6yaltAEQmIDXjvGIahEhmUVGqDqIhWXU5dnTJhznFqwZRSlSQteG9o2paUIIsHCjlGSh4wOtRpmNnqW4dKEjFOjMOa0ByxPTquhKZkUoxoMgQviJRqj206wuY++/4SsbOF2FpimbDNmpgNSRNKYrp+go7zY583OWstrpBpWJ9+yPr8I+oCZ8ZYQSSQcsQ5ASyFApRqD24CuYCSuNnvuby55vz0HuvViu12S5E6PiNaf9lZ/Sxaia35mqUWqVMfSkal4HxgvRaGYSTFiWBMff5vJ23E1GuuaSbUM9ER5r+3mNU92rBi/+oLdLjBesGjZM2orXlkw1yUJRYVUGuZckJFEXW4uSG5lILclVHNBw1UlVSLYiXU8q22ViDFojidm2WzQalKbSmzvTdFBKWd77ecE943OO85xEgce9qjE0JjGfqeqSSefPWMYUoYE/DBYwwchhHXtvg2EHxAS2QqcPzwu1y/+Jw87sAoYhxWqwshayXbYz9SUGKeMGKrTdoYvFPGmMgpU4y9KyJSFYwRTFjx6OPfxPk6cZMVXDD8+Eef8L/73//f+cGnTzhMI7lEYpkXRBceumDBggULFiz4s0L55iy1izP3reDtWXPbUwoNipBzRLSqMVoSt+9OjTGUpOQUKbNVUm1k2r3kMFwSfIuzjnfffcxutyOXwqrZ4E1AY8Yaw2Ho6W8uOPR7Hj3+Dt3xtxiuvyIOLxApFC0UAsdHD2iaMyZxrLYBjK820rm8pgnN3PZpaNoGjCelPFsUDdYIznlSLCgRMZZpSjjnGafIMI5Y59ms1wRfy3imGOukhWvYbrfc7GrbrzGQY0FyRGbrLiJY63FqyKlAKOQSZ9IMx0drxnEipUzMmRACahs2Zx+Qhp7h+id4B946cvJMUbChIeYRKQNx3JHzQNGCGDPPz0AyK9qzD+jOPkTVkNNUN1QBRGp7rblVxqrKa61FSyVWxjqatsG5hqbxeO8qWbcGuFVe6zUWA5L17prfYZ7KqSqyYK1Uq3GwTPsRLX623d7ualb11KBv1NDbn++2xAhB/Jrjdz5i9+oL8nRTyW6plluAYiMGj4ojFcFIQ9utmJLOP5beNQTPFwQxMhcdyWx5rd8TY+7swaFpgbl9mYLMO6K5ZFJKTNOI875e59mOPU0T1gtIZn/9kny44P7jb9Guj7m8fM7VxSusbwleMblUxitCTb4WvFfSNPH65VPOHrxLe/IOhytL7m/w1s17twYrgdC1ValVaObt1tsJHOOqTf2nn33FZnNayacxiJGaDxbBOl//7WJwduBHP/oJ/5v/7f+ZT5/swK8xWHLssaKkXIhpac1dsGDBggULFiz464a3N9/iT7h3/gAfOgoKJTIcdvjQ4ENb34TPRSpGqjoKmZIyaeqxMm8SSn3Tuz0ONb+JVoImif3uguHQ0/cHSlJ2u57TB4846j5mf9lxc/kpqo5773wX1x3jmzVH3QnTOJHmRtBSCih3FsxxHEGU4KGkVPOftr7dr9MshXF/XR+HP0IRDvsDwxhpO8Po4h2hNcbMtluDDYEQAsNhBCxIg5LJJSIzwbI2sF3XHcdSIkYLUqrN1/lALJHj7TExSyVUKEk8m7PHHHZfkabXKBONb+hRMoWpvyJPB+K4rzZcqWQiSYNptqyOHtKdvEvC1b3PMlG9s0LKYPx6JjF2tjUIIYSZVBUsgg8toW1QLaQ0YW1tOq6ttnkufHqjWs6jqMA8e8MtkXxTQCSisxpXiSylYGz92Cp9lnnCpDBN05zHBef83dfKOVPMms35hxwun5HTTc1NllytwEXnJt2CFkvTtsRSLbq3P9v8U/7cNI+WmvN8s19ardxFqTnmmbzmnOe88DwFq4WUItZVkn4Y9qxWR1hraduWsd9jVbCS+OLTPyTlifc+/jXW6y1npydcX1/T3+zYTQecKbNSXa+V5nrAM1xfcn19zfvf+RWOH3zAzesXpGFfzcSqrDdbjHeUrNhbtXPON9enp2CMcv/8FOc7xnHAWAjez/ll92Yrl8LL1y/5L/7Lf8CPvrxgSAWZD0+csXShflyZa8oWLFiwYMGCBQv+nbHMt/ylx1sjosf33sXPypDM78RTnoiHgSNX9wVv36B7Z+dW0Zq/PApnqNTsZopT1dUExBpygWGcODl/wJOvPmcqE6uzd3G2Nula3yHWsjk/IqzOa6bPrTFNQ1ahiGCbUFtrSybF2pbadWGeYMkcDjdEcwCkKlX2BFFb1T0tOAxkAQepvCm0yUU5DD2Ifu2NvaDeItZVwqKWosyFNE0tkCmZtmmwvsXYmq20kkjD5bynKeSpoQ3HWCnVWmosMUeiJqyxHJ9/wPWT1xhGyMqqdVxe7+mvn5LPHlEwIAYVT+ju0a0f0Bw/QKWlKARnwVpy9jDP2lgXODl/hOY6/7E73JBSvHu81lia0OBDIKeENJacI6VYrHF3JUCV8P+3/JlyS0Jv1cc3f1GLn2Z10VlUUy0XQskpE0vCGIuzlYi1bcPQHwjeY2a1rjI/UBGKrOhO3iUeXpKGq7koqWYkRW+/fyaniHENYgKpjKR5JubuB55/zjLfuMbYeRamKp4qCum2QbmilDo/I6a2/rrg2W62xDjdPfciUHLdP02qrI/v89F3hHG/Z7p5gbjAex9+wHviSDHy6sVTbi6eE4eb+RBDcO0K7xtiMjx89BjvA1k8m/NHpPFAjBEXGlxo672nipZISQO+XZHVzkp1puTaBLzqAm2ojb1iqMVeSZlKxFnL0ydP+S/+T/8Vv/8nn+G6FRsppJwRPKKmlpDx5jlesGDBggULFixY8NcHb42INt2amn+LWHGkHNkf9mhO5DhyfHxGyakqMHLn1sVYM5NUwShY60g5kdJUJz40UXJBbcsH3/lVnn/1hM32mPXmmNXREc57cs5kUYrpECm0PhB8Q4wJa/2s6ARiHFmtVuxvrnj16hnHp+cIiZtXz2ibDheqtZg8IXggI2XC2Gp7TapgLG23oh9qOUyMkcnJXfGRFkMuI4jBWct6vSHHyM1NJYzetXRtbZ9NKgTnMETIE8EoYgtxOlDSjjFOGCdg1hS1WPEoCbyjOXkPf/OCsv8ZMQ+IWNogXN98wX73AavzbxHjnnZ1hG9PKdKQMZgyR0VzYpzGOpviHYoSfENBUVcVwZXWjGMtZDJY3zA/bQTvqgVbqmqpJXEngsotiXtDSPRrv3IdrrybxkELJRtKFrxrGKYdOSfKaEACWR3jdGDVBkIT0JIYDzv6mBlCy3q7wTctvvG11VgEJdCuHzBiSLsXGAOqBlHBCDgLOQ04t0GojcAp1RIpY2y11+ptBrXOmej8Z7fNuylHrJ2Lke4eZ3pjTUbYro9BDdY40Egu49cs4uCMx4llHyecnbh68icU1yHtGd3xOxjf8c773+Hee99C84TTVDdWc6xzRQns6oRYMkYtWQTTrOlag2hVOw1CP15z+fyn7C6ecP7wO2zvf1QPfLQ+ptrsPOG8J8VUCaYLFJR9TDz5/Cv+D//H/wuf/Owrmq5DSs3GWjPvjxrBhqqE3u68LliwYMGCBQsW/Duj1FLJb+xrLfiF4+1lRI1FilJkYhxeQ040CuJX5BK5vHhO2zUYcztv4aqvkdtW1lpII9Zh5/1GzYlbZSrGie12S/NRQIzHugYzN6kaYyqJUMVawzRNtQzGvbFc3jaa+hBQlFJgmEaGly+4+OlPMT4gPnDv3XeJovimRVHitGc4XNKJkGVFxiIGmjaQtU7TTNN0l3dULVVtm9U15wIGw6pbo9nTtQ7VzOEw0HYNFgHNlNTjiARrKKpYo+AmxsNLXFBsd0TOddPy5atXHB1v2Zx/yMV4AekC0oRT8GJ4/uJnfPToVzFyH2ssxXh0/rmdGDTHeSMz1f1Uqi32tn245nnrVMybx6UU7N2USyllnuOZLakoxtqvkdDy87nQ+XlQ1XmntD7HcZrq84YhD3uKeIZhJLQe6zvGKZO1kBVudge6FFl3LcF5ikIaeq6GHoD10THNalVtw2IpBpruHt44+t1T7KzVFYUMUApW6qRNzQU7YoyIe5OZrVej/l7K3IZb7yBSSqAWNxN3LQVnK4nVecs0pR5VSy5CTBE7RXxocL6pJVEIMSXW2zM0CjqNxFgoMdYpG2vIWdGSyXGglEzbdYyHC1795A958PAjCOe1VkpHmiC1STruuXr+Gb4UYsmUMiFl5KgzTPtXpKP7GL+6s8srVa01RQg+MOx6Ll6+5rPPv+Bf/Js/5sc/fcbVbk8yzXyakL72nFa7+9fniBYsWLBgwYIFCxb89cJbI6I//L3/N2Rlf3jJi5efYYHGrzh95zv88t/4O6RcLbuH3Q6DsN4eYU0dxrglKEC1Nc550ZKrzbMaMiHmDNaiUm2lMtsob0nNdrtlmnq8D5UQfY1E3SpUKdcCmfPTU0yBV69fY6VacEmRV198zktTODk/5/T+fbyBvhSmYaDbbhmzchjGSrqMxVlLSpEYI03T4BtHydX6qVonNFKKFM24+Q16zglQnLF1IkRgmHpE6kSIMVWhLGkixh6Ngm1XlLqAwunJKUUSpT3m6OFvcP38h+T9V0zjDaFds370LYSGAiQxdL4jpUjOIwkhx8jctwOllkeJCM64Oatp5kIgM2ciZba/ujvTZT/05LlE6Zas3pHQ+aPelArdPrX6c79SqsqbMZY43PCj3/uvOXvvY8LRA4Yx0XlHaDz7oUdn5TXFiFuv8dbWLHKuTcSalcPr1xwuLzHB4ZqGo7PzuZlXOD59yH53QS5grCMXxYqnUNXp22sLQowJ59zdY0opUUuIaiNuLoVpGlEyq1UHcyO0dQah2phfXvd88vkzDruJ6+uJZ69uyJo5P1vz8N45989OOVl1eEmIVYgTpoAtlqyGq90BHS6YiqClFjpNw57nz55xdbNHXOBwreQ//YKb8SlDP+Ct4fSoY9UoG585W8H9Y8d2Y9g0gsWAePAN0zTifQNSDw9CCJRcGKeRw82B/+a/+ef86Sdf8PJmx6vdwG7M7IcDmYT1BrEg4qDIPC9UqhK9ZDIWLFiwYMGCBf//YMmI/qXHWyOiv/+7/4STbYc1E6IjU85M00j/1RecPfo2jx69V7NnCE3T1Df5GGovTW2rFQOiMvfSCFIUYyHmhBqD0bpt6azDmJq9zCmS4sSqa3GSSRpJY0KLIraW8BhTbYqC0vjA8fFZzcldXzAerrFWwICxikhBHCCJaRpwoeHo9CFZLWKEYB0nPjBNE4dhRGZCSi7kWLDzTuUw7BFqY+3QH9A8YiwkTC34EcWK1ExrKbRNQ5oKwTcYLZScySnhnWWKB8y4R2lQcfM8iCWVgu/ucf7+lpefFVL/khIzJ6uHiK2tp2osed6zLKUQ04QotbFX6rSOuLrB6p0FTCX+Vup/i/kaka+twaWGXhHR2W5d1bCs88yN3Jbq/HxO9Pbr5FxIKc/ZSkHEsrt+Rp6+IqYPuH/+PpdXV+wPN7jg502pAgLW2fp9y4QxhWCFWOYDCzMrcTET04GpbbAy8Or5z/BOaJtQyZcNNN0KbDN/bVtt4nMLbs7ViisiaClVJZ2nTXJK7PodrQ8cb7ZYY+pWbFFEM1Mx/NPf/zH/t3/8r3nxeocaBxJQcTRdR/hqZLf7GdN4oPMN27YWeeU4oZppGg/GUMTgmk/ntuaIqL3LlcaU6u6uKjAigHPgsKxuCmOM7A9DPdhgZBsSj882vH9vy6ZzbDplc9Kw75/SdR3r7QoDNGKwKWKy8Hd+47f493/rbyECh1gYYubFxSV/+KMf8oc//hE/+PwzEhCaDSK2UnQjUISUlhf/BQsWLFiwYMGCv254a0T05GxFawMiLVmFxq14/6Pv8vjDX6FZHaGaMaK0TYv3HmvdPGUBNXOXkSKgiRwnShzRPLDvIzYEmtWGUhLONrOClclFyTGxXa8oJUKZ6HeXWBvQAvluHqQql20Is13WE9PE/ua67o8aOyt/lVx1XUfbNLVwqdRCJefDrMAqQqZrAl3TMo4jfYwIBdVIjAWhWpVVMxboguPm6opd6mlDoAkBK5Uc56QcdjesGw8SGWPBCgi2klwMToQUI6Ht5txe3bPMOZK14LtjHn3nP+D69WOSCuvtQzIWYwKhbasDuijDXJhjZku0MQY/z6QU1a+1GVdD9O1hkszEMqeJUjJTKvVQYd4Ilblw6E4LVeXrs6GVcL7Z1Uwp3eVEZb4HLl4+heaYe49/BbUN25NTLi9f0h/2tU0ZQQ0omTjt0XxAUawJFCMUDFrmldGZQA6HnqNtYL1ak8ZrDtc7Us4cn73HdnuPIg1TKXMzb90WzSr4pp3ttVpVeRFiTnhj2fUHUkpsz86wcmubNRgiEeH/+k/+gH/wz39ElIZmU7dCi1bLeUqJmAu+XWGbjmEYKFmwBUp2xCKM+wGlugdKvsR7j/eeQ9+DGHxwhODxzhBjqnEKEeI0knLEO8vp0ZamC8RsOAyZV1fCV9cTf/DkCmMMVnp8uMR5w1HbMQw9Y85YMbx7vOY3Hz3k+x/eY9WAZqXxDhMa3lt3/NqjB/xP/v2/xydPnvDP/uCP+Bd/+AMuhojvAsHWgqyTzdGf86vNggULFixYsOCvGm5jTt/U11rwi8dbI6JOQYtD3Jb3P/wlvvPLf4vVZkPURE4TmhVjwIrB2YAYWzc/S66WR+Vu6zDHA69efE6/27HZntPJFs0ZsQExrt6oonhr6XxHKYkcI0LEGkMTPOIczhiMrSTTzIRUZ9Vu6g/cXF2BGsS4eTsxYyy0bUvbrijWg/UzSTWzqlg3O5woTAfksOfk+AH7YSSXSgyapsFai7f2LsdqjXB+7128NVxfPuXm5hX3Hn4bFxra7ggloiaTNdb8rKlE3YhDMBzGTGgNKc1tQwpGLCpCjCNqW7p7v4z1fiYoVaHtOod1FiuOnBM5D7NlOBF8MyuYYJF5w/Rraqa8IaO1YViJ04je2XAt3gecqwcLOae7DGi1Jt9mK2tmtpQyk9E3ZUXee8brVwyXrzh9/zdxq3sUqffGZnPKyxdPMQjOe4xxeCeU1EMeUK1lxrcWYoytLbflNp+qNKstOfUMaURlpJTE9cuv6A8965P7+NUJSIexhinmWpY0jHRNfd6ttXj1DIc9Qz8Cwv3zc4LzHIae0ASKRpTAv/zDT/iH//KH3CSPc1DixDAl8teak7WAmmrvtc7Rx1rs461FbACpBxliILh6/fohUoxlSomhz7RFCK4q1zkr4ziAwJASaRjoU+Ko63DW0a22ODswDSM3h2kmnD3TGPG+4cP33sXZgLGGr16+4qvriR8837H94z/lP/z+t/nvfech21AtuGn+P4dODL/06F2+9fAd/gd/92/z//ydf8Xv/NGfMA4FfOB6t//zf8FZsGDBggULFvzVwmLN/UuPt0ZEo2w4uf8h3/rub/DOe9/C2TC/+U6IESyOkhPGVHJ1O52SS65tpsZirOAErl6/YDjccLQ5YrVaY0ODqCUnpRgFqeU4YgGt9lDVTCxKu1pjbYPYgLX2bs/yVuHS+SbXFNnf3OBmknlrJw3B4ZzDGIdxlYg67wCDxkLMEdWR/e4prz79fazd8NHf+PscbbfkzJv5D5GaI3UWYyzb4zNiSXU3tWSGcWQcDrXtNbTkZMDW1lY11cqpAoqlaCV2KRWsN+RbpRIz5xihiOJCJb2+aWiMYRgGxnFgtdrMedaqrk1jpJQMcz71jfp5a8W9fVbftMHKnXVVyaUqn7ezK/Xjqg1apBKkWzJ4SzxjirXQxghNUydrDocDWgqvn/wMcR2Pv//biF2hAnHowXiONmc4b/DOUUqhDZ64O2BKRlQoEhEJ9fmtQy1AnVwZpwlxAR9WZN+R44h1CVVBhysuv7xEmjXN5oRuc5+wOiJTaJswHz5UK/DKWawYhmFktdpgbf0uxnp+9tVz/uiHT/iTn73gBz97yW4UhjFSgFxA84hzAd/UXVlFGcZpLnaaJ3ZKIU2J4Bwy79vmnGfbb+a2Ztr7wDRGchLUGpq25eZmh3WBmCecdQTvKSK83o84iTRjRjSTpkQuSj+N5BRriVYwPN/d4ATun5xwsl1xuT9wPWTc6ox//JMX/PEXL/ntX37Mr7x7htVqNXcCloJT+Pa9E/4X/+n/kP/oN36L//If/jP+6LPPcF3zjb++LFiwYMGCBQsWLPiLjbdGRP+j/9H/kuN1QxUAC8KItwYpBSWDURQ7n3aMcxGRYGfiY7Qw7F4TEdq2Y7v9kGkamHKEQWmQuomYM0ESTz/9I6wzvPvh90ECRVO1iNoW62ydhZnVU1RRU9/wl5zRUqqKpAVjTN0speAseN9iXYfxLfhQC490VjWpP2fsL/jyh/8Mp8L5h78EYnHGYuV2H3VuhU2lqqLzBqW1wn53Q7e5x/b4HZz3X9vXFKxtKcZRZC5iEqDUzlbrLDGOuNBUm+o8GSLzT2aNw4qrmVtVNBe0FKY01r/zDih4G8g21u9bSi19krnBeFa8jJFZOf75EqkMlLlhtf55qW27pRDj1z+3Ps05F6YpVqW0ZIyZbbM5Y1zA+QbNE1cXLzl999t431JsQLCYdcPY39AdASVhDTgKWDDiUWOhFAwJEY8aR1ZTVTvnWG+3+PUK6wMmNEhosTHMtlegKCIZzQPp4iteXTznwbd+k+I6QLF+NR9wFFQMITQ0jSeq5auXO/7Nn/yU3/mDH/H5iytupsCYC/txonGe1vu55ChTzApjhVQypDhP+qxRoB8mfPA1I4rWgwd5s0uaUsQFD2pIU5zVeIOKMGZld3FVLedNQ54yacpYaymzXXrVrWicJeZcnx9T8M4zDQMhwHrVELzn+voaI5YQPKumYdV1fPT+e3z15RM+ebFnl77is1eX/P3vf0BrHaLyZlcVJajyqx+8y/v/s/8p/6/f+33+we/86z/X15oFCxYsWLBgwV9BlDdrGt/M11rwi8ZbI6Knx1sYL9i/fsJud4m11a5Z226nu2ymlltLqbl7U+1Dizeew9UFR8cnBB/ohz2qiqGqd+MhYY3BeuHzn/weTz79fYwx9Ncvef87v4W4BuMcxlrUuDuiBnxthqOSvhQj/c1NbY5FcSIYK4Q2YJsWfIv1XSU7fN2rroiAb9Y8+uBv0jUn7MZCwRJLRgBvw91sTEmpMjKpmc4pTVVRcr4qfPOPZo0AjpR0VihrNrRKllXNdb4Q05zJnFVXnR/b7eMquRLr+U9w1hHzQH/zCvKAyRMYQxsaslJLZuZSHiNCplp250c6y6TzXItWa26KqarFxjBNY22bLVpVO2oTbikFcmGaJmKMABiZFVMMYuqvKUY0TiRpuPfoWxwOl7jmBBtWqHGE1TF5CqSprxlgzWieyCWixHrAoEISxXct3gQkZ7quw3lHrv5lCorzgRJaVA2aMtgMmqHUMRZbEuPNK7p7H9BPIzIe0FSIZSLGic32Hp8+u+Yf/asf80//6DMudxNJDVlb0jQh3uN9S9sE2uDp+x4EvLOINcSSsNZhvefQjxSEtm0ws2LfhIafK3hSJYiQtBDjVK+pGLz3xClinaFtW1Ctm5/z543jRFFw1jLFSIoR7z2IULISQoM5NjUHvVoxDCNGLOOUMOI4vXdG4x1X15d89NFjLq93/Nsff8YffvIpz15d8p//9t8BGytjnpVbEcE74fyo4T/77/9dvvPeu9/Uy8qCBQsWLFiwYMGCvyR4e9bcq0+5fvk5sb/AWsG3G0QCxjdYU9CSa3Ot1JZURSilaqfDkLDNhq5tSFPPTX+4s9UWSm3HLRblgmm44YtPfheTR4xvuX75GZ+OifP3v8W9B+9h1BCnjPctYuTOKpuLojnT9z05RdZHJ8RxT+731Q5p6jRMNg5xHSptVffyNCuWt7ZeQwjnHJ21HHYXnN87owktUWvLbJlVRecc4zSSs6nWZGspxQA1s3p7TqOlkIGc0vw9ajmOFhAz2zcrk8V7//8zfH1LQm6/dyk1Z5hjIg4Rpj3j5Sek/jlFPM36lGZ9j84/QufcbCVEQpFq9y1zy7C53RAtBTA0TUdK9XlcrTY466vVteicPa0ts8ybp29+3vp8O2cw4jDi5uumfPC93wC3IqeEyK4+HtchxuGaLb7dQIlM44EyXBFTpIwDRR2mPSKsjzHtCsTTUDOYU1FSjsScKKqkojjfUrKQNaGacL4aektKxJS4ePk5fn0PYyzPvvgRXWNJYcVPvrrid//4D/jXP3nN5ShMSREcq64lxYloBKyjcQ6jinOO7XaLtZaubVFgnHO7YqtinIvStjVLPE0TAG3TMg4jOdepHwOkcaSUMtt6BWMcwQtZM6vVin5/oO8PdQIGCCGwatc1i+0tu5trch4JoaFtOxSlc2tKLkwp40Jgv9uTUz1ImaYNq27F2O/44v/b3r2FWH6Wex7/vof/YR3q0NVd1d2mqxO77RjRsaPxJszOOAgzyGbEKM6Ae3sRZObGQxAFQVAMKMjcBEVvJwoDgzI4BhQVR1C2qG0mO9m23TvGdOxT0qeqrkPXOvwP72Eu3n9VDDuzTcbuatk+H+iLrlX1rloHivX83+f9PZcvYjPD4lyJagtWAzz5whXeccd+ekbhfLtzJlh1FzRKFTl+7NDN/NMihBBCiL8EMaR/N2stsetuWyG6efksOrZYFEZnqX00pgI0jRFJozpQCqNiCpbBoCKorEdelNR1RTXZRKkAIZ3bjCYnRI3JMgaDOUZrqwRfY4ynxWFjS/BjVi6dZbS5wczcPOVwHl8OKcoBOqRCrvU+jXpxDqMN5XDIQnYIX08Zb6xTTza6cSrpXGhUutvV1zuzTlMx5vGhARQrK1e4cP4ZDhw8wsLSnWTFDD5EWufIMovenl7SBSVBBo0nBE/bNilRNXis1im4KbbEGFKqrdIQI1p37bttTEE23Sscu31PSDuSkW6EiOrmOsZIU9doFFlhIVeEaQQ/ZbI+JbQtZdnDZks0znc7bqkYjTGm+aZp1W7nEawtiBEyu51Gpl4WSNS2Dd57vPe0dUOep3O6cadA9jjnMTbbmd2pdcZgbpG6aSiKPrgKh0ZHjS0s6Cw9hdZSmALyHhrHVvSU+YBssJc2GryyWK3Q3bzRqCPBp4sHJrM4a1HBo00gRoX3ihg0qmsvznKNdx5fTenN72N+8RDPX7zMz55+nqd/v8aW00RdpITh0OB9S1/38N2IHuf9zsWWMrf0en2896kjwDlc6wgxoH3cOYPqQqRpmp15q65NYU+ZMZRlQWgbcgpcVeG8AzTGZKiuDVwpRdkryfKMtq0AcM4Rnafsl10acJqLOhjkOO8wWY4xmvUbaxDhrjsPY4mcO3uWsc1w0ZPlFhVgY20NN61Y3r+Xu99+N3v3zPO7K6vU45p/f+8xTJse+3ZKstGWYDxWAgKEEEIIIQCYTqd86Utf4pvf/CYXLlxgYWGBd7/73XzhC1/gjjvueE1rra+v88gjj/D4449z5coVDhw4wPve9z4eeeQR5ufnb80DeA1uWyEag6cNLSrF66S2zuCILnQjNbZbMi1a5yiVgSnQyoLSXLt6icl4E6tB6zSLM0aNLQvm9uzFFD3Qlj37D3Dl/Dz4DYxKI1OUiumsaTtma23M1mid2b2HUjG33eLafTbOspy2bdA6kuUFWZ6T9Xo01SzONyjbo3WezNd/0CaZdrGcc10LLChdsv/QUVYvnefi889y9YWz7D90lH2vez1e93FVQ4xpVzDoFMKkuyIjywyVbwnBYUwqNFPRpNIcVe/QRqczmioFORmt8DHgvaMwGShDIO0YawLBp/OvSikCCte0aKWwRuOnU5p6iiK1cW7eGDMc7kETuzRbQ+tabFbQ1On51KRiNM3tTOmxWmepW1fT7aLqLonX4WOgqqqdHei86OFdCzoVtChFZlLgkHOephkTu8cc25rCGnRMRVnwY5xL436y3lxqs44KpTJ0njHYdwyVzzJav4omo+jP4p0H2jRAJmp0DJQW6klNr+ij+4barROUAQvaKFoXGdctG1sjNiYNW5VjdO4stT/P+RdXOXd1g1HIaUnttVppbC+FV2VZRlEUzMwMCUqhosY1FUqD957JZEJdNxS9PkVR0OvnONcSYkrJTcFZAYXCdGctXdOk930I1JMWpRXWGgb9HlVdk+UFbVBUbaCfmZ1RONYaaNLu/9zskH5ZpPmf0yk6M1RbI66v1ux/3QEwhoOL+7EqcvHci4S65dixIwyLkjPPP0czHXP+3HlUjOydm2Xp4CyH79jH/n17+T9P/wPPnr3M9cOHuefQXpb3zqb3pfOgNEqDQe9cnBBCCCGEeLVi7I573aS1/hxUVcW73vUuTpw4wcGDB3nve9/LuXPn+PrXv873vvc9Tpw4wZEjR17VWqurq9x///2cOXOGI0eO8OCDD3L69Gm+8pWv8IMf/IBf/vKXLCws3OJH9M+7bYWoC+n8Yllm3e5f3Dm3qIhdKm4PbQu0HaBtjrIFKM362iqbm6topSHEFDxkMhb2LtKfXSIrSnxIITzGlmTlHHFSYzVkpkijTDSgYxrXYjPyPKdtW6bTlBprdmaFpqLYu5ZI6NJgc3r9BbRORWY6R9qk9uDt86VKYUzawUyFWc4wW2Rw9z72Lx9j9eo5XnzhOdZWrrB0cJnB/F6yYoBvuxRcrVFosqzAGEWeR6qmIc8s2mi8b6nqijyz3WHW0M38NOnxoXDOgbJozUvnRMNL8zm3bZcBRVHgXUuIgUhEG00eLHvmFkBtpwNnDMohN7Y2CUHTtCkYyDU1EDAmFaF5XuCqEWWZwny893gNmc1pukRWY4uXxr8AzvtUxAfHdJxabofDYff7/GHa7vbuqEqP10eCaqkmm0RtsUWf9NY26dqCUvRnFrsQnxqrNVlRcGNccfb8Rc6ee4G6DYyamqpqIKRiqXUtMWhC0IyqKVPX4jCM65baRTyR2ntChKZyKGMIKpA2/bYvQkSs1jRNw3g8BgWD4SC1UndnapvW0TQteV5SVVX3nlM0TY3N/uA9FUGpCBHatsW5Jr22MZBpg/IafGBmdp4yTxdIIp4Gl8KHrCXPcwaDAcZst6AHJlVF8C2D/oCiKFAusrmxyubqKksHX0eeGRYX97G6usbGjS1+99zzvOnuN3DHgYM8f+4syhiOHnk9bzhyiDfctcyzzzzD6pWrFBks33WQ65Xjf/3iN/znv/43zGcKFX2a8dq1jwshhBBCCPjiF7/IiRMnuP/++/nRj37EcDgE4NFHH+VTn/oUH/7wh/npT3/6qtb6xCc+wZkzZ3j/+9/Pt771rZ1MmocffpivfvWrfPKTn+Qb3/jGLXokr46Kt2mC6w//69+gVUDRAq5r54y4GNLokHyAKefoDRcwKqMNAd+N/GjrmtVrVxmPNlAqkOU5SwfvpD/ch+8CgrxPu6whRi5f+B0bV851LY6K3OYURYG2YGxGb7hIf+4A19auY4xlYc9eVBf04r3v5lm2EAORgDaaENI5TBUcioDpZpBCVyBpi/ddOyrb5zINqIjvgltCu8X1q5e4fuUFVAwcXH49c4uHUTbD2hwVItYostziXENVN1ibY62hbStcO03BRcF3hU8aERJJKbpV1ZCXfWxWomyaqVo10xS+E+kuAKSgMI3CGkPwjnZ8nXbrHG6ylsJ58iH9uUWKmX3kvXlsPmQyHRPRtK2D4MmsZjJJgU7bhdN24FDwnqqpUcbQ7w3Jsh7K5Ni82EkMNtFRV5P0XKqIqyta59IaJkPbnCzLMcZC1Hgf0vnYGGgax6QOjGtPUH2yvEe/N0jJw5mm7A8osgxDy7TaYGVthb8/+Xt+deo8L15fZ9I42ggeTb83wLtA27QEFcmyYue+dJcAbLQmyzOIkSzLurZZh8LgXAq3CjGkCxg+7UorBb1eDxc846oiMzllmWPSVj7G5Cg0nrRrmcT0PHe7ycYYvHe0bbsz43ZmOCB6R1HkGJPROgfakNkcozT9Xg/vPZvjKT6kcKosy8gKS/CeyWTKdDqmzHPmhjMcXl7m+voazz37j0xHIwbDGWbnZnnrvcc58/tzrFzbYHxji9lezuKeWRrfMLewwP7FBWYHJU8+/RteuLLCsUP7OHpoL5c2a85eG9PLc46/bp73/9V9HJ7rgff4ELDWYozhLX/7X3b1748Q4tb5d/o/3u5fQQjxZ+J/h/9509Y6ffo0b3nLW3b+f795N0M1d1PWHsVNful/uPP/U6dO8eY3v/mmrP1qNU3D0tISm5ubPPXUU7ztbW972e3Hjx/n5MmTPPnkk9x3333/7FqXL1/m0KFDWGu5cOEC+/fv37mtrmuWl5dZW1vj0qVLLC0t3ZLH82rcth3RrMxTYRdSm653Lu1iKkOez5L35zH5kKY1eF/jfIvznsFglrwYcHD5KNNqzGQyYn5+gSzvoVRGbhQheKJ3KKOwWc7hY/dy9I3vIM9zxhvXefH506jQEHz60G7yHqPxFGvSuA7nHTazVG1FVdUMB71u5zWNa+kPZohommpCVd1Ax5BSYp1LhVUwaB0hKoYzs7QxUFfT1KqpFZlRKZBouMTM/CL7D9zFi2d/y8q1a+SDRYo+6Zyo1rQBCGkWaCCmuaQqpbsaa4khoIwGn8Ka0lWFiFYabRTeO2wGbV2hjE2jSFT6vkBLBIyyaDQEBUFjdAa2h9ea4B02HxCzPpujEQMydO1S+7ROu6TEtHtcln3qZsr2HqvzGmvS86mN6kavph1Ck5foLE9vBh0IrQc0VlsUgbwApdNrNKlr1q6PWVlvuLwyYlwFNm+MqJxnNGkYTT3TuiWi8Q7AY7OUBqzw9Ht9yqJg0OthMsXq+nVGtcfpjGBnMMqTWYML6YymNgZbKFQXEqSNoa4d2hiMtWAs3oeUZOzBtQ5jLUppitww6PeZTKcprVanMSdap4K7qZuU3BtiCt0yaUyQ9zVaG8pemeZ3VtM0Wid4jEmvg29rsiwnNxatTXcfKR26btLII9C0VU3rJuRFzqSuWNqzl14ZqZyjbRqyEBiUA0Jw5MZg8dzYGtE2DXlhmZ2b447Dy5x77jlcNWW1rvjtM79lfm6eK1XF3rlZeoVm8cA+lg8fIkTNM8/8jp+fPUvbeoqih8r7XJ8E1jbGWJNSr59ZHfPfvv93/Ke/uo+33rmICoCK6f0shBBCCPEaxHDzPkPcpn25l/n5z3/O5uYmR48e/SdFKMAHPvABTp48yXe/+90/Woj+8Ic/JITAAw888LIiFFIH5Hve8x4ee+wxvv/97/PQQw/dzIfxmty2QlRnOURwDnxw6CzHmJK8nEPnPaZ1gxtNCDElwjqfAn/KPKALiATyokde9tDdbqTRGq01bZe+mhJDixTggwKd0Z/bhzcF7bRCZ4a5ufmUetumURVVXTGdTihCQa83oJwtca5JZ/OMpewNQWdYk2Gtxfka17jujGqEkEaS5Dn0+gNMZqiqhrIsyYxNBdn2fMwYUVGRzS1SvmnAeHIDawuqpqb1nqIoiDHSuBZj0tlPrVNCLTGi02nPLlX4pTgi1e1EWmvTyJPQYrSF7uxlQKGV7lJnFZiU8hsUeAVBK7zSuBjxaIb9OVQ+RFuDdxqvAy4oUKlISgcdQcWCoixxzuFDIBpF6yMey6RpmLQOvzEBapTawGaWxrUpIico6skUqzQqBqqm5trmmMurm1y8tsVWo2iiJihL1VQ0jcPanKpxaWCODxA8Vpu0YzypMCajCZ44TvMzddcGbLLUsts00zSWpOwTYkT77WCigMk0vTK1t7oIZc9R1RXWpp3ITKfd5Na5VHC2Dmst0+mUoizJi5x82O/GlaSfMSYVisq1GGNRylA3aTbocDhkPB4zrSqyLGMwGAKRajqmaWqMSmdDt4tbFzwzwxkAnAsMBoN0oaL1KNIZ4XRu1rCytkHRy4gxpBAuH8lHYw7sX6JtahZm5zh77hybWyO2xhN0bhjMzrC0/yBrl6+hA6yvraBVw5vuWebA/iUya2mD5zennuHFyysEB+iSubkcbeDi5RVu7Jllcc9epldXmLYepXq8GBzf+tkTxHgfx+88RAwNdM+lEEIIIcRfql//+tcAvP3tb3/F27e/fvLkyZuy1mOPPfaq1rqVblshanuzad6lH+DcEO9CmgdpSramVWojVGCMwuiCPGZpqqTenvepMDad4dRad+NE4k4gi/eevOgBZnuAJoGAyXOOvfk41Xgjzbi0Oa0HkykIjqLIaJqKtmmYGczinE8f/o0hL0q0zUFpfPQQQJscVAMqtWz64AnK41VKkK3bhjIvyG2G3TkLuz2qBLTyKKuw5JQMCd5TlEU6Lwk75wVVN+c0hNg9B6ntV5GKU2XozqN2RS7pXKhSGh/S7FEPTNuGtq7p5T2qukFpQ+Na6sZRNZ6qdkxGYzY3x6zf0GAM7vnLtOEqLhiaEJlMK3y3m628og0O17VZpnCnNo2jaR0qKFwbaWPEq1Qsb190Ugoal3a6tc4x3UiP4Hz3emk8mkgaW+IiqfVVGfozfTJt6fcjlffE1hFbj+taPo3NUcqS2TK12UZQKs3nDJOWoshxLjIaTxkoQyDu3G+MCqvT8+YCVK3Dqog2Nl0MUKR23LYlYih6GW1V0TRNSrN1jmlVkeeWwbBPnuU7LchZZimjZzyaEHwgzyxVVeO7dtztOZtFkdqWB8MB/dBDxYA2hhAibeN2An6stUwmEzY3t1DGUJY9jAFUS1H0aZuIynPm5uaZGQy5dnWFy1eucL2uyLRh0B9QDnq86Y1v5B9O/prnz51ncbKPmV6fIu8xt3cPC3MlR+86wMLsLKY3z9XVdf7x9O84d/FFIpasKJgbDhnMzLA5usGN8QgfFWujmmHfc3D/fi6vrOHqmn2LCxSDGf7H3/09+t8W/KvDB9K5VyGEEEKI12ASt27a2JUJ45f9/8yZM695jcXFxT+pzfXChQsAHDr0ymPttr9+/vz5XV3rVrqNO6IlRI3SEaOLFDbjHJPxGsboFChjMzKTEZUixlR0ogKR9AFcd7NDlUozJ9u2pW3bnQ/zkZjO9ymAiCaksRlakw9miXXD5uYNbJ6lltXgu/N8Ku08BodrWyDtABbGoLTZaX+NKGxWEkOLd3XamVOGohwwHM5gbY7RujvZShpb0f20RhFiBAwKhyWgs4xGa6JzaF5qE9ge12GM7do4045od7wQ59JZyeB9V5RD7aZMqpbR2DGejLhRBdZHIzbGYyajBu8t46Zm0jqcU9Q+0AaV0kx9+i2bYKkCaBNRNARlMMpjiHTNvDjvaWMK7bEGok8zLkOsUBiKLCcGaENEGd1NmQmYEAhd+3TjPDDBB0/rHL2yR4yG2vmUmozC2owmKJoAxtgUKhQrenlGrhXRggOM0pRFD4UGLEFrRmELHRU++JfamcN28nDAh3TBoJqO6JU9+r3hTgpxWeTUbYsP3egRpbvXLT1XWW4hBsqyRGvNZDLBOSjyAcam73OuxodAWRS0bQNo+r2SGGE8npIpRXQpCAvSxYe2TcXy9oidPMtxXTASqjuLrFJYU3qs6b2QZTlV1TAzM8PSvv2sr03YmG7ywsoad0TN3Ufvoiw0Z547y8rVFab9io1M0R9Y9i/tZXW6BSpycHEPiwsz7Fuap1QQRyOefvYsz18bc/36BgpFfzhHP8vJc4M1mhtba7QhYAoLWJqmYXV9i3vuPsZhYxjdGBOjY9LU+N4M//1nT/Dx//AuDu+bvTV/ZIQQQgjxL9ZJfnHL1n7wwQdf8898/vOf55FHHvn/vs/RaARAv99/xdsHgwEAW1tbu7rWrXTbCtF//aEv3q67FkIIIcQtcjPDSYQQQvzLpf/4twghhBBCCCGEuFW2R7VMJpNXvH08Tu3DMzMzu7rWrXTbdkSFEEIIIYQQ4tU4evQop06duqX3cf36ddbX11leXqYoitf0s4uLi3/SfR8+fBiAF1544RVv3/76nXfeuatr3UpSiAohhBBCCCH+rJVlueuzPXfT8ePHAXjqqade8fbtr7/1rW/d1bVuJRX/HAbnCCGEEEIIIcRfqKZpWFpaYnNzk6effpp77733ZbcfP36ckydP8uSTT/7ROaKXL1/m0KFDWGu5ePHiy9J867pmeXmZtbU1Ll269Ccl/f6p5IyoEEIIIYQQQtxGeZ7zsY99DICPfvSjO+c4AR599FFOnjzJO9/5zpcVoV/72te45557+MxnPvOytQ4ePMgHP/hBmqbhIx/5CK4bEwjw6U9/mpWVFT70oQ/d1iIUpDVXCCGEEEIIIW67z372s/z4xz/mF7/4BceOHeOBBx7g/Pnz/OpXv2JxcZHHHnvsZd+/urrKs88+y+XLl//JWl/+8pc5ceIE3/72t7nnnnt4xzvewenTpzl16hTHjh3j0Ucf3a2H9f8kO6JCCCGEEEIIcZuVZclPfvITPve5z9Hv93n88cc5f/48Dz30EE899RRHjhx51Wvt27ePJ554go9//OM0TcN3vvMdNjc3efjhh3niiSdYWFi4hY/k1ZEzokIIIYQQQgghdpXsiAohhBBCCCGE2FVSiAohhBBCCCGE2FVSiAohhBBCCCGE2FVSiAohhBBCCCGE2FVSiAohhBBCCCGE2FVSiAohhBBCCCGE2FVSiAohhBBCCCGE2FVSiAohhBBCCCGE2FVSiAohhBBCCCGE2FVSiAohhBBCCCGE2FVSiAohhBBCCCGE2FVSiAohhBBCCCGE2FVSiAohhBBCCCGE2FVSiAohhBBCCCGE2FVSiAohhBBCCCGE2FVSiAohhBBCCCGE2FVSiAohhBBCCCGE2FVSiAohhBBCCCGE2FVSiAohhBBCCCGE2FVSiAohhBBCCCGE2FVSiAohhBBCCCGE2FVSiAohhBBCCCGE2FVSiAohhBBCCCGE2FVSiAohhBBCCCGE2FVSiAohhBBCCCGE2FVSiAohhBBCCCGE2FVSiAohhBBCCCGE2FVSiAohhBBCCCGE2FVSiAohhBBCCCGE2FVSiAohhBBCCCGE2FVSiAohhBBCCCGE2FVSiAohhBBCCCGE2FVSiAohhBBCCCGE2FX/F60t9onptE2WAAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "h_patches, w_patches = [int(d / PATCH_SIZE) for d in image_resized.shape[1:]]\n", - "\n", - "fg_score = clf.predict_proba(x)[:, 1].reshape(h_patches, w_patches)\n", - "fg_score_mf = torch.from_numpy(signal.medfilt2d(fg_score, kernel_size=3))\n", - "\n", - "plt.rcParams.update({\n", - " \"xtick.labelsize\": 5,\n", - " \"ytick.labelsize\": 5,\n", - " \"axes.labelsize\": 5,\n", - " \"axes.titlesize\": 4,\n", - "})\n", - "\n", - "plt.figure(figsize=(4, 2), dpi=300)\n", - "plt.subplot(1, 2, 1)\n", - "plt.imshow(image)\n", - "plt.axis('off')\n", - "plt.title(f\"Image, Size {image.size}\")\n", - "plt.subplot(1, 2, 2)\n", - "plt.imshow(fg_score_mf)\n", - "plt.title(f\"Foreground Score, Size {tuple(fg_score_mf.shape)}\")\n", - "plt.colorbar()\n", - "plt.axis('off')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "9d85c34e", - "metadata": {}, - "source": [ - "### Extracting Foreground Patches\n", - "We find the patches with positive classifier output, in order to fit the PCA only on the foreground." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "f5fb40d4", - "metadata": {}, - "outputs": [], - "source": [ - "foreground_selection = fg_score_mf.view(-1) > 0.5\n", - "fg_patches = x[foreground_selection]" - ] - }, - { - "cell_type": "markdown", - "id": "ccc29844", - "metadata": {}, - "source": [ - "### Fitting the PCA\n", - "We use 3 components, and use whitening. " - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "f45e2928", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "PCA(n_components=3, whiten=True)" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pca = PCA(n_components=3, whiten=True)\n", - "pca.fit(fg_patches)" - ] - }, - { - "cell_type": "markdown", - "id": "1936f236", - "metadata": {}, - "source": [ - "### Applying the PCA, and Masking Background\n", - "Finally, we project the features using the PCA, and apply a mask to set the background to black." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "4894d6b7", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# apply the PCA, and then reshape\n", - "projected_image = torch.from_numpy(pca.transform(x.numpy())).view(h_patches, w_patches, 3)\n", - "\n", - "# multiply by 2.0 and pass through a sigmoid to get vibrant colors \n", - "projected_image = torch.nn.functional.sigmoid(projected_image.mul(2.0)).permute(2, 0, 1)\n", - "\n", - "# mask the background using the fg_score_mf\n", - "projected_image *= (fg_score_mf.unsqueeze(0) > 0.5)\n", - "\n", - "# enjoy\n", - "plt.figure(dpi=300)\n", - "plt.imshow(projected_image.permute(1, 2, 0))\n", - "plt.axis('off')\n", - "plt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/notebooks/segmentation_tracking.ipynb b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/notebooks/segmentation_tracking.ipynb deleted file mode 100644 index 9b97d4c9abbd57c7f01994650e268235648143b6..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/notebooks/segmentation_tracking.ipynb +++ /dev/null @@ -1,1123 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "de738fb5", - "metadata": {}, - "source": [ - "# Segmentation Tracking with DINOv3\n", - "\n", - "This notebook demonstrates using DINOv3 for video segmentation tracking\n", - "using a non-parametric method similar to\n", - "[\"Space-time correspondence as a contrastive random walk\" (Jabri et al. 2020)](https://arxiv.org/abs/2006.14613).\n", - "\n", - "Given:\n", - "- RGB video frames\n", - "- Instance segmentation masks for the first frame\n", - "\n", - "We will extract patch features from each frame and use patch similarity\n", - "to propagate the ground-truth labels to all frames." - ] - }, - { - "cell_type": "markdown", - "id": "6ab28340", - "metadata": {}, - "source": [ - "## Setup\n", - "\n", - "Let's start by loading some pre-requisites, setting up the environment and checking the DINOv3 repository location:\n", - "- `local` if `DINOV3_LOCATION` environment variable was set to work with a local version of DINOv3 repository;\n", - "- `github` if the code should be loaded via torch hub." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "1d4d54c3", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "DINOv3 location set to /private/home/vkhalidov/fairvit_dinov3_release/app/dinov3_release/\n" - ] - } - ], - "source": [ - "import datetime\n", - "import functools\n", - "import io\n", - "import logging\n", - "import math\n", - "import os\n", - "from pathlib import Path\n", - "import tarfile\n", - "import time\n", - "import urllib\n", - "\n", - "import lovely_tensors\n", - "import matplotlib.pyplot as plt\n", - "import mediapy as mp\n", - "import numpy as np\n", - "from PIL import Image\n", - "import torch\n", - "import torch.nn.functional as F\n", - "import torchvision.transforms as TVT\n", - "import torchvision.transforms.functional as TVTF\n", - "from torch import Tensor, nn\n", - "from tqdm import tqdm\n", - "\n", - "DISPLAY_HEIGHT = 200\n", - "lovely_tensors.monkey_patch()\n", - "torch.set_grad_enabled(False)\n", - "logging.basicConfig(level=logging.INFO, format=\"%(asctime)s - %(levelname)s - %(message)s\")\n", - "\n", - "DINOV3_GITHUB_LOCATION = \"facebookresearch/dinov3\"\n", - "\n", - "if os.getenv(\"DINOV3_LOCATION\") is not None:\n", - " DINOV3_LOCATION = os.getenv(\"DINOV3_LOCATION\")\n", - "else:\n", - " DINOV3_LOCATION = DINOV3_GITHUB_LOCATION\n", - "\n", - "print(f\"DINOv3 location set to {DINOV3_LOCATION}\")" - ] - }, - { - "cell_type": "markdown", - "id": "1a28e044", - "metadata": {}, - "source": [ - "## Model\n", - "\n", - "We load the DINOv3 ViT-L model and get some attributes. Feel free to try other DINOv3 models as well!" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "75cd313f", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-08-13 14:13:40,010 - INFO - using base=100 for rope new\n", - "2025-08-13 14:13:40,011 - INFO - using min_period=None for rope new\n", - "2025-08-13 14:13:40,011 - INFO - using max_period=None for rope new\n", - "2025-08-13 14:13:40,012 - INFO - using normalize_coords=separate for rope new\n", - "2025-08-13 14:13:40,012 - INFO - using shift_coords=None for rope new\n", - "2025-08-13 14:13:40,012 - INFO - using rescale_coords=2 for rope new\n", - "2025-08-13 14:13:40,012 - INFO - using jitter_coords=None for rope new\n", - "2025-08-13 14:13:40,013 - INFO - using dtype=fp32 for rope new\n", - "2025-08-13 14:13:40,014 - INFO - using mlp layer as FFN\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Patch size: 16\n", - "Embedding dimension: 1024\n", - "Peak GPU memory: 1.1 GB\n" - ] - } - ], - "source": [ - "# examples of available DINOv3 models:\n", - "MODEL_DINOV3_VITS = \"dinov3_vits16\"\n", - "MODEL_DINOV3_VITSP = \"dinov3_vits16plus\"\n", - "MODEL_DINOV3_VITB = \"dinov3_vitb16\"\n", - "MODEL_DINOV3_VITL = \"dinov3_vitl16\"\n", - "MODEL_DINOV3_VITHP = \"dinov3_vith16plus\"\n", - "MODEL_DINOV3_VIT7B = \"dinov3_vit7b16\"\n", - "\n", - "# we take DINOv3 ViT-L\n", - "MODEL_NAME = MODEL_DINOV3_VITL\n", - "\n", - "model = torch.hub.load(\n", - " repo_or_dir=DINOV3_LOCATION,\n", - " model=MODEL_NAME,\n", - " source=\"local\" if DINOV3_LOCATION != DINOV3_GITHUB_LOCATION else \"github\",\n", - ")\n", - "model.to(\"cuda\")\n", - "model.eval()\n", - "\n", - "patch_size = model.patch_size\n", - "embed_dim = model.embed_dim\n", - "print(f\"Patch size: {patch_size}\")\n", - "print(f\"Embedding dimension: {embed_dim}\")\n", - "print(f\"Peak GPU memory: {torch.cuda.max_memory_allocated() / 2**30:.1f} GB\")" - ] - }, - { - "cell_type": "markdown", - "id": "6e7881a8", - "metadata": {}, - "source": [ - "We want to process one image at the time and get L2-normalized features.\n", - "Here is a wrapper to do just that." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "c85e1dc1", - "metadata": {}, - "outputs": [], - "source": [ - "@torch.compile(disable=True)\n", - "def forward(\n", - " model: nn.Module,\n", - " img: Tensor, # [3, H, W] already normalized for the model\n", - ") -> Tensor:\n", - " feats = model.get_intermediate_layers(img.unsqueeze(0), n=1, reshape=True)[0] # [1, D, h, w]\n", - " feats = feats.movedim(-3, -1) # [1, h, w, D]\n", - " feats = F.normalize(feats, dim=-1, p=2)\n", - " return feats.squeeze(0) # [h, w, D]" - ] - }, - { - "cell_type": "markdown", - "id": "8a66e30f", - "metadata": {}, - "source": [ - "## Data\n", - "\n", - "Here we load the video frames and the instance segmentation masks for the first frame." - ] - }, - { - "cell_type": "markdown", - "id": "a27a2bbe", - "metadata": {}, - "source": [ - "\n", - "This notebook assumes that the video has already been processed to extract individual frames as `.jpg` images in the current directory.\n", - "```txt\n", - "000001.jpg\n", - "000002.jpg\n", - "...\n", - "```\n", - "\n", - "To run this notebook on your own `.mp4` video, use `ffmpeg` to extract frames at 24 FPS:\n", - "```bash\n", - "INPUT_VIDEO=\"video.mp4\"\n", - "OUTPUT_DIR=\".\"\n", - "ffmpeg -hide_banner -i \"${INPUT_VIDEO}\" -qscale:v 2 -vf fps=24 -y \"${OUTPUT_DIR}/%06d.jpg\"\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "ac498bc7", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of frames: 174\n", - "Original size: width=1920, height=1440\n" - ] - } - ], - "source": [ - "VIDEO_FRAMES_URI = \"https://dl.fbaipublicfiles.com/dinov3/notebooks/segmentation_tracking/video_frames.tar.gz\"\n", - "\n", - "def load_video_frames_from_remote_tar(tar_uri: str) -> list[Image.Image]:\n", - " images = []\n", - " indices = []\n", - " with urllib.request.urlopen(tar_uri) as f:\n", - " tar = tarfile.open(fileobj=io.BytesIO(f.read()))\n", - " for member in tar.getmembers():\n", - " index_str, _ = os.path.splitext(member.name)\n", - " image_data = tar.extractfile(member)\n", - " image = Image.open(image_data).convert(\"RGB\")\n", - " images.append(image)\n", - " indices.append(int(index_str))\n", - " order = np.argsort(indices)\n", - " return [images[i] for i in order]\n", - "\n", - "frames = load_video_frames_from_remote_tar(VIDEO_FRAMES_URI)\n", - "num_frames = len(frames)\n", - "print(f\"Number of frames: {num_frames}\")\n", - "\n", - "original_width, original_height = frames[0].size\n", - "print(f\"Original size: width={original_width}, height={original_height}\")" - ] - }, - { - "cell_type": "markdown", - "id": "06ac1ba9-9737-4e8f-901b-99d8baf50f52", - "metadata": {}, - "source": [ - "Let's show four sample frames from the video:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "567b31fb-ec61-40a3-89d3-b4457a09c72d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def mask_to_rgb(mask: np.ndarray | Tensor, num_masks: int) -> np.ndarray:\n", - " if isinstance(mask, Tensor):\n", - " mask = mask.cpu().numpy()\n", - "\n", - " # Exclude background\n", - " background = mask == 0\n", - " mask = mask - 1\n", - " num_masks = num_masks - 1\n", - "\n", - " # Choose palette\n", - " if num_masks <= 10:\n", - " mask_rgb = plt.get_cmap(\"tab10\")(mask)[..., :3]\n", - " elif num_masks <= 20:\n", - " mask_rgb = plt.get_cmap(\"tab20\")(mask)[..., :3]\n", - " else:\n", - " mask_rgb = plt.get_cmap(\"gist_rainbow\")(mask / (num_masks - 1))[..., :3]\n", - "\n", - " mask_rgb = (mask_rgb * 255).astype(np.uint8)\n", - " mask_rgb[background, :] = 0\n", - " return mask_rgb\n", - "\n", - "\n", - "def load_image_from_url(url: str) -> Image:\n", - " with urllib.request.urlopen(url) as f:\n", - " return Image.open(f)\n", - "\n", - "\n", - "first_mask_np = np.array(\n", - " load_image_from_url(\n", - " \"https://dl.fbaipublicfiles.com/dinov3/notebooks/segmentation_tracking/first_video_frame_mask.png\"\n", - " )\n", - ")\n", - "\n", - "mask_height, mask_width = first_mask_np.shape # Abbreviated at [H', W']\n", - "print(f\"Mask size: {[mask_height, mask_width]}\")\n", - "\n", - "num_masks = int(first_mask_np.max() + 1) # Abbreviated as M\n", - "print(f\"Number of masks: {num_masks}\")\n", - "\n", - "mp.show_images(\n", - " [frames[0], mask_to_rgb(first_mask_np, num_masks)],\n", - " titles=[\"Frame\", \"Mask\"],\n", - " height=DISPLAY_HEIGHT,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "8b8cc9d6", - "metadata": {}, - "source": [ - "Time for some math! Input frames need to be resized to match the desired forward resolution and the model patch size.\n", - "\n", - "The desired forward resolution refers to the _short side_ of the input.\n", - "If the desired resolution is not a multiple of the patch size, we simply round it up.\n", - "Then, we determine the _long side_ by maintaining the original aspect ratio as much as possible, but rounding up to the patch size as well.\n", - "\n", - "With the occasion, we'll also setup the torchvision transforms and test them out on the first frame." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "3b2c3fe2", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "First frame: tensor[3, 960, 1280] n=3686400 (14Mb) x∈[-2.118, 2.640] μ=0.186 σ=0.915 cuda:0\n" - ] - } - ], - "source": [ - "class ResizeToMultiple(nn.Module):\n", - " def __init__(self, short_side: int, multiple: int):\n", - " super().__init__()\n", - " self.short_side = short_side\n", - " self.multiple = multiple\n", - "\n", - " def _round_up(self, side: float) -> int:\n", - " return math.ceil(side / self.multiple) * self.multiple\n", - "\n", - " def forward(self, img):\n", - " old_width, old_height = TVTF.get_image_size(img)\n", - " if old_width > old_height:\n", - " new_height = self._round_up(self.short_side)\n", - " new_width = self._round_up(old_width * new_height / old_height)\n", - " else:\n", - " new_width = self._round_up(self.short_side)\n", - " new_height = self._round_up(old_height * new_width / old_width)\n", - " return TVTF.resize(img, [new_height, new_width], interpolation=TVT.InterpolationMode.BICUBIC)\n", - "\n", - "\n", - "SHORT_SIDE = 960\n", - "\n", - "transform = TVT.Compose(\n", - " [\n", - " ResizeToMultiple(short_side=SHORT_SIDE, multiple=patch_size),\n", - " TVT.ToTensor(),\n", - " TVT.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n", - " ]\n", - ")\n", - "first_frame = transform(frames[0]).to(\"cuda\")\n", - "print(f\"First frame: {first_frame}\")\n", - "\n", - "_, frame_height, frame_width = first_frame.shape # Abbreviated as [H, W]\n", - "feats_height, feats_width = frame_height // patch_size, frame_width // patch_size # Abbreviated as [h, w]" - ] - }, - { - "cell_type": "markdown", - "id": "b123acb4", - "metadata": {}, - "source": [ - "Label propagation happens at the output resolution of the model,\n", - "so we downsample the ground-truth masks of the first frame and turn them into a one-hot probability map." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "783a97c7", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "First mask: tensor[60, 80] i64 n=4800 (38Kb) x∈[0, 5] μ=0.229 σ=0.855 cuda:0\n", - "First probs: tensor[60, 80, 6] n=28800 (0.1Mb) x∈[0., 1.000] μ=0.167 σ=0.373 cuda:0\n" - ] - } - ], - "source": [ - "first_mask = torch.from_numpy(first_mask_np).to(\"cuda\", dtype=torch.long) # [H', W']\n", - "first_mask = F.interpolate(\n", - " first_mask[None, None, :, :].float(), # [1, 1, H', W']\n", - " (feats_height, feats_width),\n", - " mode=\"nearest-exact\",\n", - ")[0, 0].long() # [h, w]\n", - "print(f\"First mask: {first_mask}\")\n", - "\n", - "first_probs = F.one_hot(first_mask, num_masks).float() # [h, w, M]\n", - "print(f\"First probs: {first_probs}\")" - ] - }, - { - "cell_type": "markdown", - "id": "29a8f33a", - "metadata": {}, - "source": [ - "## How it works\n", - "\n", - "And now the fun part!\n", - "\n", - "Label propagation takes as input:\n", - "- The features of the current frame, with shape `[h, w, D]`\n", - "- The features of the `t` context frames, with shape `[t, h, w, D]`\n", - "- The mask probabilities of `t` context frames, with shape `[t, h, w, M]`\n", - "\n", - "For each patch of the current frame:\n", - "- We compute the cosine similarity with all context patches.\n", - "- We restrict the focus to a local neighborhood and select the top-k most similar context patches.\n", - "- We compute a weighted average of the mask probabilities of the selected patches to obtain a prediction for the mask probabilies of the current patch." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "4f78d2e7", - "metadata": {}, - "outputs": [], - "source": [ - "@torch.compile(disable=True)\n", - "def propagate(\n", - " current_features: Tensor, # [h\", w\", D], where h=h\", w=w\", and \" stands for current\n", - " context_features: Tensor, # [t, h, w, D]\n", - " context_probs: Tensor, # [t, h, w, M]\n", - " neighborhood_mask: Tensor, # [h\", w\", h, w]\n", - " topk: int,\n", - " temperature: float,\n", - ") -> Tensor:\n", - " t, h, w, M = context_probs.shape\n", - "\n", - " # Compute similarity current -> context\n", - " dot = torch.einsum(\n", - " \"ijd, tuvd -> ijtuv\",\n", - " current_features, # [h\", w\", D]\n", - " context_features, # [t, h, w, D]\n", - " ) # [h\", w\", t, h, w]\n", - "\n", - " # Restrict focus to local neighborhood\n", - " dot = torch.where(\n", - " neighborhood_mask[:, :, None, :, :], # [h\", w\", 1, h, w]\n", - " dot, # [h\", w\", t, h, w]\n", - " -torch.inf,\n", - " )\n", - "\n", - " # Select top-k patches inside the neighborhood\n", - " dot = dot.flatten(2, -1).flatten(0, 1) # [h\"w\", thw]\n", - " k_th_largest = torch.topk(dot, dim=1, k=topk).values # [h\"w\", k]\n", - " dot = torch.where(\n", - " dot >= k_th_largest[:, -1:], # [h\"w\", thw]\n", - " dot, # [h\"w\", thw]\n", - " -torch.inf,\n", - " )\n", - "\n", - " # Propagate probabilities from context to current frame\n", - " weights = F.softmax(dot / temperature, dim=1) # [h\"w\", thw]\n", - " current_probs = torch.mm(\n", - " weights, # [h\"w\", thw]\n", - " context_probs.flatten(0, 2), # [thw, M]\n", - " ) # [h\"w\", M]\n", - "\n", - " # Propagated probs should already sum to 1, but just in case\n", - " current_probs = current_probs / current_probs.sum(dim=1, keepdim=True) # [h\"w\", M]\n", - "\n", - " return current_probs.unflatten(0, (h, w)) # [h\", w\", M]\n", - "\n", - "\n", - "@functools.lru_cache()\n", - "def make_neighborhood_mask(h: int, w: int, size: float, shape: str) -> Tensor:\n", - " ij = torch.stack(\n", - " torch.meshgrid(\n", - " torch.arange(h, dtype=torch.float32, device=\"cuda\"),\n", - " torch.arange(w, dtype=torch.float32, device=\"cuda\"),\n", - " indexing=\"ij\",\n", - " ),\n", - " dim=-1,\n", - " ) # [h, w, 2]\n", - " if shape == \"circle\":\n", - " ord = 2\n", - " elif shape == \"square\":\n", - " ord = torch.inf\n", - " else:\n", - " raise ValueError(f\"Invalid {shape=}\")\n", - " norm = torch.linalg.vector_norm(\n", - " ij[:, :, None, None, :] - ij[None, None, :, :, :], # [h\", w\", h, w, 2]\n", - " ord=ord,\n", - " dim=-1,\n", - " ) # [h\", w\", h, w]\n", - " mask = norm <= size # [h\", w\", h, w] bool, True inside, False outside\n", - " return mask" - ] - }, - { - "cell_type": "markdown", - "id": "d5e119ef", - "metadata": {}, - "source": [ - "How does the neighborhood mask look like?" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "ca3500c9", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "neighborhood_mask = make_neighborhood_mask(feats_height, feats_width, size=12, shape=\"circle\")\n", - "\n", - "mp.show_images(\n", - " {f\"{(i, j)}\": neighborhood_mask[i, j].cpu().numpy() for i, j in [[3, 14], [20, 25]]},\n", - " height=DISPLAY_HEIGHT,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "63987fe5", - "metadata": {}, - "source": [ - "To understand how it works, let's do it for one frame only.\n", - "The \"context\" contains only the first frame and the \"current frame\" is the second one." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "d1301ba3", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "First feats: torch.Size([60, 80, 1024])\n", - "Current feats: torch.Size([60, 80, 1024])\n", - "Current probs: tensor[60, 80, 6] n=28800 (0.1Mb) x∈[0., 1.000] μ=0.167 σ=0.371 cuda:0\n" - ] - } - ], - "source": [ - "torch._dynamo.maybe_mark_dynamic(first_frame, (1, 2))\n", - "first_feats = forward(model, first_frame) # [h, w, D]\n", - "print(f\"First feats: {first_feats.shape}\")\n", - "\n", - "frame_idx = 1\n", - "current_frame_pil = frames[frame_idx]\n", - "current_frame = transform(current_frame_pil).to(\"cuda\") # [3, H, W]\n", - "torch._dynamo.maybe_mark_dynamic(current_frame, (1, 2))\n", - "current_feats = forward(model, current_frame) # [h\", w\", D]\n", - "print(f\"Current feats: {current_feats.shape}\")\n", - "\n", - "current_probs = propagate(\n", - " current_feats, # [h\", w\", D]\n", - " context_features=first_feats.unsqueeze(0), # [1, h, w, D]\n", - " context_probs=first_probs.unsqueeze(0), # [1, h, w, M]\n", - " neighborhood_mask=neighborhood_mask, # [h\", w\", h, w]\n", - " topk=5,\n", - " temperature=0.2,\n", - ") # [h\", w\", M]\n", - "print(f\"Current probs: {current_probs}\")" - ] - }, - { - "cell_type": "markdown", - "id": "25c47c78", - "metadata": {}, - "source": [ - "Then, we upsample the predicted probabilities and postprocess them.\n", - "\n", - "Finally, we visualize:\n", - "- The first frame with its ground truth next to the second frame with the predicted masks.\n", - "- The per-mask probabilties predicted for the second frame." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "4818851d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Time to do it for real!\n", - "\n", - "This time we will process all frames, using a queue of context frames and a queue context mask probabilities.\n", - "The queues will contain a limited number of the most recent frames, determined by `max_context_length`.\n", - "The first frame is always included in the context and doesn't need to go in the queue." - ] - }, - { - "cell_type": "markdown", - "id": "6efef2c9", - "metadata": {}, - "source": [ - "Let's define all hyperparameters in one place." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "132ee2c0", - "metadata": {}, - "outputs": [], - "source": [ - "MAX_CONTEXT_LENGTH = 7\n", - "NEIGHBORHOOD_SIZE = 12\n", - "NEIGHBORHOOD_SHAPE = \"circle\"\n", - "TOPK = 5\n", - "TEMPERATURE = 0.2" - ] - }, - { - "cell_type": "markdown", - "id": "5485cd7e", - "metadata": {}, - "source": [ - "Let's go!\n", - "\n", - "The predicted mask probabilities and the masks, at the original mask resolution, will be stored in `mask_predictions` and `mask_probabilities`." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "a587723e", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Processing: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 173/173 [03:09<00:00, 1.09s/it]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Processing time: 0:03:09\n", - "Mask probabilities: tensor[174, 6, 1440, 1920] n=2886451200 (11Gb) x∈[0., 1.000] μ=0.167 σ=0.368\n", - "Mask predictions: tensor[174, 1440, 1920] u8 n=481075200 (0.4Gb) x∈[0, 5] μ=0.268 σ=0.903\n" - ] - } - ], - "source": [ - "mask_predictions = torch.zeros([num_frames, mask_height, mask_width], dtype=torch.uint8) # [T, H', W']\n", - "mask_predictions[0, :, :] = torch.from_numpy(first_mask_np)\n", - "\n", - "mask_probabilities = torch.zeros([num_frames, num_masks, mask_height, mask_width]) # [T, M, H', W']\n", - "mask_probabilities[0, :, :, :] = F.one_hot(torch.from_numpy(first_mask_np).long(), num_masks).movedim(-1, -3)\n", - "\n", - "features_queue: list[Tensor] = []\n", - "probs_queue: list[Tensor] = []\n", - "\n", - "neighborhood_mask = make_neighborhood_mask(\n", - " feats_height,\n", - " feats_width,\n", - " size=NEIGHBORHOOD_SIZE,\n", - " shape=NEIGHBORHOOD_SHAPE,\n", - ") # [h\", w\", h, w]\n", - "\n", - "start = time.perf_counter()\n", - "for frame_idx in tqdm(range(1, num_frames), desc=\"Processing\"):\n", - " # Extract features for the current frame\n", - " current_frame_pil = frames[frame_idx]\n", - " current_frame = transform(current_frame_pil).to(\"cuda\") # [3, H, W]\n", - " torch._dynamo.maybe_mark_dynamic(current_frame, (1, 2))\n", - " current_feats = forward(model, current_frame) # [h\", w\", D]\n", - "\n", - " # Prepare the context, marking the time and mask dimensions as dynamic for torch compile\n", - " context_feats = torch.stack([first_feats, *features_queue], dim=0) # [1+len(queue), h, w, D]\n", - " context_probs = torch.stack([first_probs, *probs_queue], dim=0) # [1+len(queue), h, w, M]\n", - " torch._dynamo.maybe_mark_dynamic(context_feats, 0)\n", - " torch._dynamo.maybe_mark_dynamic(context_probs, (0, 3))\n", - "\n", - " # Propagate segmentation probs from context frames\n", - " current_probs = propagate(\n", - " current_feats,\n", - " context_feats,\n", - " context_probs,\n", - " neighborhood_mask,\n", - " TOPK,\n", - " TEMPERATURE,\n", - " ) # [h\", w\", M]\n", - "\n", - " # Update queues with current features and probs\n", - " features_queue.append(current_feats)\n", - " probs_queue.append(current_probs)\n", - " if len(features_queue) > MAX_CONTEXT_LENGTH:\n", - " features_queue.pop(0)\n", - " if len(probs_queue) > MAX_CONTEXT_LENGTH:\n", - " probs_queue.pop(0)\n", - "\n", - " # Upsample and postprocess segmentation probs, argmax to obtain a prediction\n", - " current_probs = F.interpolate(\n", - " current_probs.movedim(-1, -3)[None, :, :, :],\n", - " size=(mask_height, mask_width),\n", - " mode=\"nearest\",\n", - " ) # [1, M, H', W']\n", - " current_probs = postprocess_probs(current_probs) # [1, M, H', W']\n", - " current_probs = current_probs.squeeze(0)\n", - " mask_probabilities[frame_idx, :, :, :] = current_probs\n", - " pred = torch.argmax(current_probs, dim=0).to(dtype=torch.uint8) # [H', W']\n", - " mask_predictions[frame_idx, :, :] = pred # [H', W']\n", - "\n", - "torch.cuda.synchronize()\n", - "end = time.perf_counter()\n", - "print(f\"Processing time: {datetime.timedelta(seconds=round(end - start))}\")\n", - "print(f\"Mask probabilities: {mask_probabilities}\")\n", - "print(f\"Mask predictions: {mask_predictions}\")" - ] - }, - { - "cell_type": "markdown", - "id": "020a82cc", - "metadata": {}, - "source": [ - "Let's visualize a few frames and a video of the result." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "3c4432ff", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import mediapy as mp\n", - "\n", - "mp.show_images(\n", - " [frames[i].convert(\"RGB\") for i in selected_frames]\n", - " + [mask_to_rgb(mask_predictions[i], num_masks) for i in selected_frames],\n", - " titles=[f\"Frame {i}\" for i in selected_frames] + [\"\"] * len(selected_frames),\n", - " columns=len(selected_frames),\n", - " height=DISPLAY_HEIGHT,\n", - ")\n", - "\n", - "mp.show_videos(\n", - " {\n", - " \"Input\": [np.array(frame) for frame in frames],\n", - " \"Pred\": mask_to_rgb(mask_predictions, num_masks),\n", - " },\n", - " height=DISPLAY_HEIGHT,\n", - " fps=24,\n", - ")\n", - "mp.show_videos(\n", - " {f\"Prob {i}\": mask_probabilities[:, i].numpy() for i in range(num_masks)},\n", - " height=DISPLAY_HEIGHT,\n", - " fps=24,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "4a5bc771", - "metadata": {}, - "source": [ - "# Conclusion\n", - "\n", - "This notebook showed how to use DINOv3 for video segmentation tracking.\n", - "It should be fairly straightforward to run it to your own video and masks.\n", - "The notebook hyperparameters can also be adjusted to see the effect on the results." - ] - }, - { - "cell_type": "markdown", - "id": "5f3adf2c", - "metadata": {}, - "source": [ - "Let's discuss GPU memory usage:\n", - "- The ViT-L model takes approximately 1.1 GB to load.\n", - "- The similarity matrix `dot` inside `propagate()` can get pretty big,\n", - " especially at higher resolution and longer context length.\n", - "\n", - "To reduce memory usage and increase speed, the notebook is already set up to work with `torch.compile()`.\n", - "In particular, note the use of `torch._dynamo.maybe_mark_dynamic()` to mark a few dimensions as dynamic\n", - "to avoid too many recompilations. To enable compilation, just set `disable` to `False` in the\n", - "`@torch.compile()` decorators for the `propagate()` and `forward()` functions.\n", - "\n", - "If you are low on memory, it also possible to use a smaller model and\n", - "reduce the forward resolution, but tracking results will look worse." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "94d339b0", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Peak GPU memory: 3.6 GB\n" - ] - } - ], - "source": [ - "print(f\"Peak GPU memory: {torch.cuda.max_memory_allocated() / 2**30:.1f} GB\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/pyproject.toml b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/pyproject.toml deleted file mode 100644 index ede1516806550631618dcccdc74d10b4c0035984..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/pyproject.toml +++ /dev/null @@ -1,45 +0,0 @@ -[tool.mypy] -python_version = "3.11" -ignore_missing_imports = true -files = "dinov3" -exclude = '''(?x)( - ^dinov3/tests/([^/]+/)*test_.*\.py$ # Unit tests -)''' - -[tool.pylint.master] -persistent = false -score = false - -[tool.pylint.messages_control] -disable = "all" -enable = [ - "miscellaneous", - "similarities", -] - -[tool.pylint.similarities] -ignore-comments = true -ignore-docstrings = true -ignore-imports = true -min-similarity-lines = 8 - -[tool.pylint.reports] -reports = false - -[tool.pylint.miscellaneous] -notes = [ - "FIXME", - "XXX", - "TODO", -] - -[tool.ruff] -line-length = 120 -target-version = "py311" - -[tool.ruff.lint] -ignore = ["E203", "E501"] - -[tool.ruff.lint.per-file-ignores] -"__init__.py" = ["F401"] -"hubconf.py" = ["F401"] diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/requirements-dev.txt b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/requirements-dev.txt deleted file mode 100644 index 44cb0c6c6fd22ddb04add6a91dc4baa253b425e1..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/requirements-dev.txt +++ /dev/null @@ -1,4 +0,0 @@ -docstr-coverage==2.3.2 -mypy[reports]==1.17 -pylint==3.3.8 -ruff==0.12.8 diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/requirements.txt b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/requirements.txt deleted file mode 100644 index c14d0fac8a9b2294fd2e9b5b3189f3c74b7a719e..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/requirements.txt +++ /dev/null @@ -1,9 +0,0 @@ -ftfy -omegaconf -regex -scikit-learn -submitit -termcolor -torch -torchmetrics -torchvision diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/setup.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/setup.py deleted file mode 100644 index 9302c1249c4c57680aa7b8a6c33582932fa22364..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3/setup.py +++ /dev/null @@ -1,86 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This software may be used and distributed in accordance with -# the terms of the DINOv3 License Agreement. - -from pathlib import Path -import re -from typing import List, Tuple - -from setuptools import setup, find_packages - - -NAME = "dinov3" -DESCRIPTION = "" - -URL = "https://github.com/facebookresearch/dinov3" -AUTHOR = "Meta AI" -REQUIRES_PYTHON = ">=3.11" -HERE = Path(__file__).parent - - -try: - with open(HERE / "README.md", encoding="utf-8") as f: - long_description = "\n" + f.read() -except FileNotFoundError: - long_description = DESCRIPTION - - -def get_requirements(path: str = HERE / "requirements.txt") -> Tuple[List[str], List[str]]: - requirements = [] - extra_indices = [] - with open(path) as f: - for line in f.readlines(): - line = line.rstrip("\r\n") - if line.startswith("--extra-index-url "): - extra_indices.append(line[18:]) - continue - requirements.append(line) - return requirements, extra_indices - - -def get_package_version() -> str: - with open(HERE / "dinov3/__init__.py") as f: - result = re.search(r"^__version__ = ['\"]([^'\"]*)['\"]", f.read(), re.M) - if result: - return result.group(1) - raise RuntimeError("Can't get package version") - - -requirements, extra_indices = get_requirements() -version = get_package_version() -dev_requirements, _ = get_requirements(HERE / "requirements-dev.txt") - - -setup( - name=NAME, - version=version, - description=DESCRIPTION, - long_description=long_description, - long_description_content_type="text/markdown", - author=AUTHOR, - python_requires=REQUIRES_PYTHON, - url=URL, - packages=find_packages(), - package_data={ - "": ["*.yaml"], - }, - install_requires=requirements, - dependency_links=extra_indices, - extras_require={ - "dev": dev_requirements, - }, - install_package_data=True, - license="DINOv3 LIcense", - license_files=("LICENSE.md",), - classifiers=[ - # Trove classifiers: https://github.com/pypa/trove-classifiers/blob/main/src/trove_classifiers/__init__.py - "Development Status :: 3 - Alpha", - "Intended Audience :: Developers", - "Intended Audience :: Science/Research", - "License :: Other/Proprietary License", - "Programming Language :: Python :: 3.11", - "Topic :: Scientific/Engineering :: Artificial Intelligence", - "Topic :: Software Development :: Libraries :: Python Modules", - ], -) diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3_adpther.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3_adpther.py deleted file mode 100644 index a77648f1ea2de23638ed6d1c40305aa1f03df3c1..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dinov3_adpther.py +++ /dev/null @@ -1,86 +0,0 @@ -# dinov3_adapter.py -import torch -import torch.nn as nn -import torch.nn.functional as F - - -class DINOv3Adapter(nn.Module): - """ - DINOv3 Adapter: - """ - - MODEL_MAP = { - "vits": "dinov3_vits16", - "vitb": "dinov3_vitb16", - "vitl": "dinov3_vitl16", - "vitg": "dinov3_vitg14", - "vit7b": "dinov3_vit7b16", - } - - def __init__(self, model_name, repo_dir, arch=None, weight_path=None): - super().__init__() - - if arch is None: - if model_name not in self.MODEL_MAP: - raise ValueError(f"Unknown model_name={model_name}, must be one of {list(self.MODEL_MAP.keys())}") - arch = self.MODEL_MAP[model_name] - - self.model = torch.hub.load(repo_dir, arch, source="local", pretrained=False) - - self.embed_dim = getattr(self.model, "embed_dim", None) - if self.embed_dim is None: - raise AttributeError("DINOv3 model missing embed_dim") - - self.patch_size = getattr(self.model, "patch_size", None) - if self.patch_size is None: - pe = getattr(self.model, "patch_embed", None) - if pe is not None and hasattr(pe, "patch_size"): - ps = pe.patch_size - self.patch_size = ps if isinstance(ps, int) else ps[0] - if self.patch_size is None: - raise AttributeError("DINOv3 model missing patch_size") - - self.blocks = getattr(self.model, "blocks", None) - if self.blocks is None: - raise AttributeError("DINOv3 model missing blocks") - - self.n_blocks = getattr(self.model, "n_blocks", len(self.blocks)) - self.depth = self.n_blocks - - self.norm = nn.LayerNorm(self.embed_dim) - - # @torch.no_grad() - def get_intermediate_layers( - self, x, n=1, return_class_token=False, norm=True - ): - outputs = self.model.get_intermediate_layers( - x, n=n, reshape=False, return_class_token=True, norm=norm - ) - - patch_maps, cls_tokens = [], [] - H, W = x.shape[-2], x.shape[-1] - h, w = H // self.patch_size, W // self.patch_size - - for (out_all, out_cls) in outputs: - if norm: - out_all = self.norm(out_all) - - out_patches = out_all[:, 1:, :] # [B, N, C] - B, N, C = out_patches.shape - sqrtN = int(N ** 0.5) - if sqrtN * sqrtN == N: - grid = out_patches.transpose(1, 2).reshape(B, C, sqrtN, sqrtN) - else: - grid = out_patches.transpose(1, 2).reshape(B, C, N, 1) - grid = F.interpolate(grid, size=(h * w, 1), mode="bilinear").squeeze(-1) - grid = grid.reshape(B, C, h, w) - - if grid.shape[-2:] != (h, w): - grid = F.interpolate(grid, size=(h, w), mode="bilinear", align_corners=False) - - patch_maps.append(grid.contiguous()) - cls_tokens.append(out_cls) - - if return_class_token: - return tuple(zip(patch_maps, cls_tokens)) - return tuple(patch_maps) diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dpt.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dpt.py deleted file mode 100644 index bc79147f8e9f5caf46da60f6215547a52b944207..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dpt.py +++ /dev/null @@ -1,233 +0,0 @@ -import cv2 -import torch -import torch.nn as nn -import torch.nn.functional as F -from torchvision.transforms import Compose - -from .dinov2 import DINOv2 -from .dinov3_adpther import DINOv3Adapter -from .util.blocks import FeatureFusionBlock, _make_scratch -from .util.transform import Resize, NormalizeImage, PrepareForNet - - -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 ConvBlock(nn.Module): - def __init__(self, in_feature, out_feature): - super().__init__() - - self.conv_block = nn.Sequential( - nn.Conv2d(in_feature, out_feature, kernel_size=3, stride=1, padding=1), - nn.BatchNorm2d(out_feature), - nn.ReLU(True) - ) - - def forward(self, x): - return self.conv_block(x) - - -class DPTHead(nn.Module): - def __init__( - self, - in_channels, - features=256, - use_bn=False, - out_channels=[256, 512, 1024, 1024], - use_clstoken=False - ): - super(DPTHead, self).__init__() - - 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 - - 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.Sigmoid() - ) - - def forward(self, out_features, patch_h, patch_w, patch_size=16): - 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) if x.dim() == 3 else None - if readout is not None: - x = self.readout_projects[i](torch.cat((x, readout), -1)) - else: - x = x[0] if isinstance(x, (tuple, list)) else x - if x.dim() == 3: - x = x.permute(0, 2, 1).reshape((x.shape[0], x.shape[-1], patch_h, patch_w)) - elif x.dim() == 4: - if x.shape[-2] != patch_h or x.shape[-1] != patch_w: - x = F.interpolate(x, size=(patch_h, patch_w), mode="bilinear", align_corners=True) - else: - raise RuntimeError(f"Unexpected feature shape {x.shape}, expected 3D or 4D") - - 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 * patch_size), int(patch_w * patch_size)), mode="bilinear", align_corners=True) - out = self.scratch.output_conv2(out) - return out - - -class DepthAnythingV2(nn.Module): - def __init__( - self, - encoder='vitl', - features=256, - out_channels=[256, 512, 1024, 1024], - use_bn=False, - use_clstoken=False, - max_depth=20.0, - dinov3_repo_dir="", # 你的本地 repo - dinov3_arch="", # 例如 'dinov3_vitl16' - dinov3_weight="", - ): - super().__init__() - - self.intermediate_layer_idx = { - 'vits': [2, 5, 8, 11], - 'vitb': [2, 5, 8, 11], - 'vitl': [4, 11, 17, 23], - 'vitg': [9, 19, 29, 39] - } - - self.max_depth = max_depth - - self.encoder = encoder - self.pretrained = DINOv3Adapter( - model_name=encoder, - repo_dir=dinov3_repo_dir, - arch=dinov3_arch, - weight_path=dinov3_weight - ) - self.depth_head = DPTHead(self.pretrained.embed_dim, features, use_bn, out_channels=out_channels, use_clstoken=use_clstoken) - self.mask_head = DPTHead(self.pretrained.embed_dim, features, use_bn, out_channels=out_channels, use_clstoken=use_clstoken) - self.patch_size = int(self.pretrained.patch_size) - - def forward(self, x): - patch_size = getattr(self.pretrained, "patch_size", 16) # DINOv3=16 - patch_h, patch_w = x.shape[-2] // patch_size, x.shape[-1] // patch_size - - - features = self.pretrained.get_intermediate_layers(x, self.intermediate_layer_idx[self.encoder], return_class_token=True) - depth = self.depth_head(features, patch_h, patch_w, patch_size) * self.max_depth - mask = self.mask_head(features, patch_h, patch_w, patch_size) - - return depth.squeeze(1), mask.squeeze(1) - - @torch.no_grad() - def infer_image(self, raw_image, input_size=518): - image, (h, w) = self.image2tensor(raw_image, input_size) - depth = self.forward(image) - depth = F.interpolate(depth[:, None], (h, w), mode="bilinear", align_corners=True)[0, 0] - - return depth.cpu().numpy() - - def image2tensor(self, raw_image, input_size=518): - transform = Compose([ - Resize( - width=input_size, - height=input_size, - resize_target=False, - keep_aspect_ratio=True, - ensure_multiple_of=16, - 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(), - ]) - - h, w = raw_image.shape[:2] - - image = cv2.cvtColor(raw_image, cv2.COLOR_BGR2RGB) / 255.0 - - image = transform({'image': image})['image'] - image = torch.from_numpy(image).unsqueeze(0) - - DEVICE = 'cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu' - image = image.to(DEVICE) - - return image, (h, w) diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dpt_v2.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dpt_v2.py deleted file mode 100644 index 604e78f4d6dd3c9a084493a69d85322ce0977ab3..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/dpt_v2.py +++ /dev/null @@ -1,225 +0,0 @@ -import cv2 -import torch -import torch.nn as nn -import torch.nn.functional as F -from torchvision.transforms import Compose - -from .dinov2 import DINOv2 -from .dinov3_adpther import DINOv3Adapter -from .util.blocks import FeatureFusionBlock, _make_scratch -from .util.transform import Resize, NormalizeImage, PrepareForNet - - -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 ConvBlock(nn.Module): - def __init__(self, in_feature, out_feature): - super().__init__() - - self.conv_block = nn.Sequential( - nn.Conv2d(in_feature, out_feature, kernel_size=3, stride=1, padding=1), - nn.BatchNorm2d(out_feature), - nn.ReLU(True) - ) - - def forward(self, x): - return self.conv_block(x) - - -class DPTHead(nn.Module): - def __init__( - self, - in_channels, - features=256, - use_bn=False, - out_channels=[256, 512, 1024, 1024], - use_clstoken=False - ): - super(DPTHead, self).__init__() - - 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 - - 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.Sigmoid() - ) - - 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 DepthAnythingV2(nn.Module): - def __init__( - self, - encoder='vitl', - features=256, - out_channels=[256, 512, 1024, 1024], - use_bn=False, - use_clstoken=False, - max_depth=20.0 - ): - super(DepthAnythingV2, self).__init__() - - self.intermediate_layer_idx = { - 'vits': [2, 5, 8, 11], - 'vitb': [2, 5, 8, 11], - 'vitl': [4, 11, 17, 23], - 'vitg': [9, 19, 29, 39] - } - - self.max_depth = max_depth - - self.encoder = encoder - self.pretrained = DINOv2(model_name=encoder) - - self.depth_head = DPTHead(self.pretrained.embed_dim, features, use_bn, out_channels=out_channels, use_clstoken=use_clstoken) - self.mask_head = DPTHead(self.pretrained.embed_dim, features, use_bn, out_channels=out_channels, use_clstoken=use_clstoken) - - def forward(self, x): - patch_h, patch_w = x.shape[-2] // 14, x.shape[-1] // 14 - - features = self.pretrained.get_intermediate_layers(x, self.intermediate_layer_idx[self.encoder], return_class_token=True) - - depth = self.depth_head(features, patch_h, patch_w) * self.max_depth - mask = self.mask_head(features, patch_h, patch_w) - - return depth.squeeze(1), mask.squeeze(1) - - @torch.no_grad() - def infer_image(self, raw_image, input_size=518): - image, (h, w) = self.image2tensor(raw_image, input_size) - print(image.shape) - depth = self.forward(image) - print(depth.shape) - depth = F.interpolate(depth[:, None], (h, w), mode="bilinear", align_corners=True)[0, 0] - - return depth.cpu().numpy() - - def image2tensor(self, raw_image, input_size=518): - transform = Compose([ - Resize( - width=input_size, - height=input_size, - 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(), - ]) - - h, w = raw_image.shape[:2] - - image = cv2.cvtColor(raw_image, cv2.COLOR_BGR2RGB) / 255.0 - - image = transform({'image': image})['image'] - image = torch.from_numpy(image).unsqueeze(0) - - DEVICE = 'cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu' - image = image.to(DEVICE) - - return image, (h, w) diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/util/__pycache__/blocks.cpython-310.pyc b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/util/__pycache__/blocks.cpython-310.pyc deleted file mode 100644 index 1f9f58cf93cb25c382c5acefb258a96c46b7a3b6..0000000000000000000000000000000000000000 Binary files a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/util/__pycache__/blocks.cpython-310.pyc and /dev/null differ diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/util/__pycache__/transform.cpython-310.pyc b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/util/__pycache__/transform.cpython-310.pyc deleted file mode 100644 index 3bbd4d1d35b61bd1a7e6d9ed5be89365f92b6145..0000000000000000000000000000000000000000 Binary files a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/util/__pycache__/transform.cpython-310.pyc and /dev/null differ diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/util/blocks.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/util/blocks.py deleted file mode 100644 index 382ea183a40264056142afffc201c992a2b01d37..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/util/blocks.py +++ /dev/null @@ -1,148 +0,0 @@ -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 diff --git a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/util/transform.py b/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/util/transform.py deleted file mode 100644 index b14aacd44ea086b01725a9ca68bb49eadcf37d73..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/depth_anything_v2/util/transform.py +++ /dev/null @@ -1,158 +0,0 @@ -import numpy as np -import cv2 - - -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 "depth" in sample: - sample["depth"] = cv2.resize(sample["depth"], (width, height), interpolation=cv2.INTER_NEAREST) - - if "mask" in sample: - sample["mask"] = cv2.resize(sample["mask"].astype(np.float32), (width, height), interpolation=cv2.INTER_NEAREST) - - 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 "depth" in sample: - depth = sample["depth"].astype(np.float32) - sample["depth"] = np.ascontiguousarray(depth) - - if "mask" in sample: - sample["mask"] = sample["mask"].astype(np.float32) - sample["mask"] = np.ascontiguousarray(sample["mask"]) - - return sample \ No newline at end of file diff --git a/DAP-main-2/depth_anything_v2_metric/util/dist_helper.py b/DAP-main-2/depth_anything_v2_metric/util/dist_helper.py deleted file mode 100644 index 7b6eb432b4988638ac9549a82fbaebf968fe9c61..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/util/dist_helper.py +++ /dev/null @@ -1,41 +0,0 @@ -import os -import subprocess - -import torch -import torch.distributed as dist - - -def setup_distributed(backend="nccl", port=None): - """AdaHessian Optimizer - Lifted from https://github.com/BIGBALLON/distribuuuu/blob/master/distribuuuu/utils.py - Originally licensed MIT, Copyright (c) 2020 Wei Li - """ - num_gpus = torch.cuda.device_count() - - if "SLURM_JOB_ID" in os.environ: - rank = int(os.environ["SLURM_PROCID"]) - world_size = int(os.environ["SLURM_NTASKS"]) - node_list = os.environ["SLURM_NODELIST"] - addr = subprocess.getoutput(f"scontrol show hostname {node_list} | head -n1") - # specify master port - if port is not None: - os.environ["MASTER_PORT"] = str(port) - elif "MASTER_PORT" not in os.environ: - os.environ["MASTER_PORT"] = "10685" - if "MASTER_ADDR" not in os.environ: - os.environ["MASTER_ADDR"] = addr - os.environ["WORLD_SIZE"] = str(world_size) - os.environ["LOCAL_RANK"] = str(rank % num_gpus) - os.environ["RANK"] = str(rank) - else: - rank = int(os.environ["RANK"]) - world_size = int(os.environ["WORLD_SIZE"]) - - torch.cuda.set_device(rank % num_gpus) - - dist.init_process_group( - backend=backend, - world_size=world_size, - rank=rank, - ) - return rank, world_size diff --git a/DAP-main-2/depth_anything_v2_metric/util/loss.py b/DAP-main-2/depth_anything_v2_metric/util/loss.py deleted file mode 100644 index 2ae5b304effd46661e93ea23127d1115c36b5265..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/util/loss.py +++ /dev/null @@ -1,16 +0,0 @@ -import torch -from torch import nn - - -class SiLogLoss(nn.Module): - def __init__(self, lambd=0.5): - super().__init__() - self.lambd = lambd - - def forward(self, pred, target, valid_mask): - valid_mask = valid_mask.detach() - diff_log = torch.log(target[valid_mask]) - torch.log(pred[valid_mask]) - loss = torch.sqrt(torch.pow(diff_log, 2).mean() - - self.lambd * torch.pow(diff_log.mean(), 2)) - - return loss diff --git a/DAP-main-2/depth_anything_v2_metric/util/metric.py b/DAP-main-2/depth_anything_v2_metric/util/metric.py deleted file mode 100644 index 8638cf25875c753cb62c3977af1417c221237dce..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/util/metric.py +++ /dev/null @@ -1,26 +0,0 @@ -import torch - - -def eval_depth(pred, target): - assert pred.shape == target.shape - - thresh = torch.max((target / pred), (pred / target)) - - d1 = torch.sum(thresh < 1.25).float() / len(thresh) - d2 = torch.sum(thresh < 1.25 ** 2).float() / len(thresh) - d3 = torch.sum(thresh < 1.25 ** 3).float() / len(thresh) - - diff = pred - target - diff_log = torch.log(pred) - torch.log(target) - - abs_rel = torch.mean(torch.abs(diff) / target) - sq_rel = torch.mean(torch.pow(diff, 2) / target) - - rmse = torch.sqrt(torch.mean(torch.pow(diff, 2))) - rmse_log = torch.sqrt(torch.mean(torch.pow(diff_log , 2))) - - log10 = torch.mean(torch.abs(torch.log10(pred) - torch.log10(target))) - silog = torch.sqrt(torch.pow(diff_log, 2).mean() - 0.5 * torch.pow(diff_log.mean(), 2)) - - return {'d1': d1.item(), 'd2': d2.item(), 'd3': d3.item(), 'abs_rel': abs_rel.item(), 'sq_rel': sq_rel.item(), - 'rmse': rmse.item(), 'rmse_log': rmse_log.item(), 'log10':log10.item(), 'silog':silog.item()} \ No newline at end of file diff --git a/DAP-main-2/depth_anything_v2_metric/util/utils.py b/DAP-main-2/depth_anything_v2_metric/util/utils.py deleted file mode 100644 index e89b994538c5123075605fb6130022867f37c99b..0000000000000000000000000000000000000000 --- a/DAP-main-2/depth_anything_v2_metric/util/utils.py +++ /dev/null @@ -1,26 +0,0 @@ -import os -import re -import numpy as np -import logging - -logs = set() - - -def init_log(name, level=logging.INFO): - if (name, level) in logs: - return - logs.add((name, level)) - logger = logging.getLogger(name) - logger.setLevel(level) - ch = logging.StreamHandler() - ch.setLevel(level) - if "SLURM_PROCID" in os.environ: - rank = int(os.environ["SLURM_PROCID"]) - logger.addFilter(lambda record: rank == 0) - else: - rank = 0 - format_str = "[%(asctime)s][%(levelname)8s] %(message)s" - formatter = logging.Formatter(format_str) - ch.setFormatter(formatter) - logger.addHandler(ch) - return logger diff --git a/DAP-main-2/erp2cubemap.py b/DAP-main-2/erp2cubemap.py deleted file mode 100644 index db360c7ed1cc2c641cfd6c29ae10e58635bceca2..0000000000000000000000000000000000000000 --- a/DAP-main-2/erp2cubemap.py +++ /dev/null @@ -1,177 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -ERP (equirectangular panorama, 2:1) -> Cube Map (6 faces) - -依赖: - pip install opencv-python numpy - -用法示例: - python erp2cubemap.py input.jpg --size 1024 --out out_dir - python erp2cubemap.py input.jpg --size 1024 --layout cross --out cube_cross.png -""" -import os -import math -import argparse -import numpy as np -import cv2 - - -def build_face_map(face_size, face): - """ - 为指定面构建从 cube-face 像素到 ERP 的映射。 - 返回: map_x, map_y (float32, 用于 cv2.remap) - 约定的面及朝向（右手坐标, X 右, Y 上, Z 向前）: - +X: right - -X: left - +Y: top - -Y: bottom - +Z: front - -Z: back - 每个面 FOV = 90°，u,v ∈ [-1,1] 覆盖该面。 - """ - # 像素网格中心采样 - s = face_size - jj, ii = np.meshgrid(np.arange(s, dtype=np.float32), - np.arange(s, dtype=np.float32)) - # 将像素坐标映射到 [-1,1]（中心为0），保证像素中心采样 - a = 2.0 * (jj + 0.5) / s - 1.0 - b = 2.0 * (ii + 0.5) / s - 1.0 - - # 为不同面定义方向向量 (dx, dy, dz) - if face == 'right': # +X - dx, dy, dz = np.ones_like(a), -b, -a - elif face == 'left': # -X - dx, dy, dz = -np.ones_like(a), -b, a - elif face == 'top': # +Y - dx, dy, dz = a, np.ones_like(a), b - elif face == 'bottom': # -Y - dx, dy, dz = a, -np.ones_like(a), -b - elif face == 'front': # +Z - dx, dy, dz = a, -b, np.ones_like(a) - elif face == 'back': # -Z - dx, dy, dz = -a, -b, -np.ones_like(a) - else: - raise ValueError(f'Unknown face: {face}') - - # 归一化方向 - norm = np.sqrt(dx*dx + dy*dy + dz*dz) - dx /= norm; dy /= norm; dz /= norm - - # ERP 映射（经纬 -> 像素） - # 经度 theta ∈ (-pi, pi]，按 X->Z 的 atan2；纬度 phi ∈ [-pi/2, pi/2] - theta = np.arctan2(dz, dx) # 水平角：朝 +Z 为 0 -> 正确前向（front） - phi = np.arcsin(dy) # 垂直角：+Y 为 +pi/2 顶部 - - # 输出 map_x, map_y 是 ERP 图像坐标（列x/行y） - # 假设输入宽 W, 高 H： - # x = (theta + pi) / (2*pi) * W - # y = (pi/2 - phi) / pi * H (phi=+pi/2 -> y=0 顶部) - # W,H 先占位，后面 remap 时会依据实际尺寸使用比例缩放 - # 为了支持任意输入尺寸，我们先输出归一化坐标 [0,1)，再在 remap 前乘以 W/H - map_x_norm = (theta + math.pi) / (2.0 * math.pi) - map_y_norm = (math.pi/2 - phi) / math.pi - - return map_x_norm.astype(np.float32), map_y_norm.astype(np.float32) - - -def remap_face(erp_img, map_x_norm, map_y_norm, interp, border): - H, W = erp_img.shape[:2] - map_x = map_x_norm * (W - 1) - map_y = map_y_norm * (H - 1) - return cv2.remap(erp_img, map_x, map_y, interpolation=interp, borderMode=border) - - -def save_six_faces(faces_dict, out_dir, base): - os.makedirs(out_dir, exist_ok=True) - for name, img in faces_dict.items(): - cv2.imwrite(os.path.join(out_dir, f"{base}_{name}.png"), img) - - -def make_cross_layout(faces, face_size): - """ - 生成常见 4x3 横向十字拼图： - [ ][top ][ ][ ] - [left][front][right][back] - [ ][bottom][ ][ ] - 画布大小: (3H, 4W) = (3S, 4S)，空白填充黑色。 - """ - S = face_size - canvas = np.zeros((3*S, 4*S, 3), dtype=np.uint8) - - # 放置 - def put(name, row, col): - canvas[row*S:(row+1)*S, col*S:(col+1)*S] = faces[name] - - put('top', 0, 1) - put('left', 1, 0) - put('front', 1, 1) - put('right', 1, 2) - put('back', 1, 3) - put('bottom', 2, 1) - return canvas - - -def parse_args(): - ap = argparse.ArgumentParser(description='ERP -> CubeMap converter') - ap.add_argument('input', help='输入 ERP 图片路径（宽高比约 2:1）') - ap.add_argument('--size', type=int, default=1024, help='每个立方体面的尺寸（像素），默认 1024') - ap.add_argument('--out', default='out', help='输出目录（六图模式）或输出文件（十字模式）') - ap.add_argument('--layout', choices=['six', 'cross'], default='six', - help='输出布局: six=六张面, cross=十字拼图') - ap.add_argument('--interp', choices=['linear','nearest','cubic','lanczos'], default='lanczos', - help='重采样插值方式') - ap.add_argument('--border', choices=['wrap','reflect','constant'], default='wrap', - help='经度边界处理（wrap推荐），纬度超界会按选项处理') - return ap.parse_args() - - -def main(): - args = parse_args() - - interp_map = { - 'nearest': cv2.INTER_NEAREST, - 'linear' : cv2.INTER_LINEAR, - 'cubic' : cv2.INTER_CUBIC, - 'lanczos': cv2.INTER_LANCZOS4, - } - border_map = { - 'wrap' : cv2.BORDER_WRAP, - 'reflect' : cv2.BORDER_REFLECT_101, - 'constant': cv2.BORDER_CONSTANT, - } - - erp = cv2.imread(args.input, cv2.IMREAD_COLOR) - if erp is None: - raise SystemExit(f'读取失败: {args.input}') - H, W = erp.shape[:2] - if abs((W / max(1,H)) - 2.0) > 0.2: - print(f'警告: 输入宽高比看起来不是 2:1（实际 {W}:{H}），请确认这是 ERP 全景图。') - - faces_order = ['right','left','top','bottom','front','back'] - maps = {} - for name in faces_order: - maps[name] = build_face_map(args.size, name) - - faces = {} - for name in faces_order: - map_x_norm, map_y_norm = maps[name] - face_img = remap_face(erp, map_x_norm, map_y_norm, - interp=interp_map[args.interp], - border=border_map[args.border]) - faces[name] = face_img - - if args.layout == 'six': - base = os.path.splitext(os.path.basename(args.input))[0] - save_six_faces(faces, args.out, base) - print(f'已输出六个面的图片到目录: {args.out}\n面名称: {faces_order}') - else: - cross = make_cross_layout(faces, args.size) - ok = cv2.imwrite(args.out, cross) - if not ok: - raise SystemExit(f'写入失败: {args.out}') - print(f'已输出十字拼图: {args.out}\n面名称(布局行列见代码注释): {faces_order}') - - -if __name__ == '__main__': - main() diff --git a/DAP-main-2/networks/__init__.py b/DAP-main-2/networks/__init__.py deleted file mode 100644 index d089ad98ef9a3c9d80a347781526d0c8ba146070..0000000000000000000000000000000000000000 --- a/DAP-main-2/networks/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from .dap import DAP \ No newline at end of file diff --git a/DAP-main-2/networks/__pycache__/__init__.cpython-310.pyc b/DAP-main-2/networks/__pycache__/__init__.cpython-310.pyc deleted file mode 100644 index db3a8b3c61c5eb1999ff9f644747c732d943cade..0000000000000000000000000000000000000000 Binary files a/DAP-main-2/networks/__pycache__/__init__.cpython-310.pyc and /dev/null differ diff --git a/DAP-main-2/networks/__pycache__/dap.cpython-310.pyc b/DAP-main-2/networks/__pycache__/dap.cpython-310.pyc deleted file mode 100644 index 8b3a5a634fcbf9d49d4e98a27878a8f56eb286cd..0000000000000000000000000000000000000000 Binary files a/DAP-main-2/networks/__pycache__/dap.cpython-310.pyc and /dev/null differ diff --git a/DAP-main-2/networks/__pycache__/models.cpython-310.pyc b/DAP-main-2/networks/__pycache__/models.cpython-310.pyc deleted file mode 100644 index 2cec20f32b8577e2a6288e256c90473a1e3ac297..0000000000000000000000000000000000000000 Binary files a/DAP-main-2/networks/__pycache__/models.cpython-310.pyc and /dev/null differ diff --git a/DAP-main-2/networks/__pycache__/panda.cpython-310.pyc b/DAP-main-2/networks/__pycache__/panda.cpython-310.pyc deleted file mode 100644 index fa2afa64bb3e1cd71260937b5a13fbb1a6d08091..0000000000000000000000000000000000000000 Binary files a/DAP-main-2/networks/__pycache__/panda.cpython-310.pyc and /dev/null differ diff --git a/DAP-main-2/networks/__pycache__/utils.cpython-310.pyc b/DAP-main-2/networks/__pycache__/utils.cpython-310.pyc deleted file mode 100644 index 1e6af1945fc49b66e1a1ac38de0be527c6793757..0000000000000000000000000000000000000000 Binary files a/DAP-main-2/networks/__pycache__/utils.cpython-310.pyc and /dev/null differ diff --git a/DAP-main-2/networks/blocks.py b/DAP-main-2/networks/blocks.py deleted file mode 100644 index 38dbcfeffc0c38ef51bcb20dfd347e50b2a60616..0000000000000000000000000000000000000000 --- a/DAP-main-2/networks/blocks.py +++ /dev/null @@ -1,153 +0,0 @@ -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 diff --git a/DAP-main-2/networks/dap.py b/DAP-main-2/networks/dap.py deleted file mode 100644 index 2960f459c96d65de7e19aea9f9bbd3e447df5e00..0000000000000000000000000000000000000000 --- a/DAP-main-2/networks/dap.py +++ /dev/null @@ -1,117 +0,0 @@ -import torch -import numpy as np -from einops import rearrange -import torch.nn as nn -import torch.nn.functional as F -from torchvision.transforms import Compose -import cv2 -from depth_anything_v2_metric.depth_anything_v2.dpt import DepthAnythingV2 -from depth_anything_v2_metric.depth_anything_v2.dinov3_adpther import DINOv3Adapter -from argparse import Namespace -from .models import register -from depth_anything_utils import Resize, NormalizeImage, PrepareForNet - -class DAP(nn.Module): - def __init__(self, args): - super().__init__() - midas_model_type = args.midas_model_type - fine_tune_type = args.fine_tune_type - min_depth = args.min_depth - self.max_depth = args.max_depth - train_decoder = args.train_decoder - - # Pre-defined setting of the model - model_configs = { - 'vits': {'encoder': 'vits', 'features': 64, 'out_channels': [48, 96, 192, 384]}, - 'vitb': {'encoder': 'vitb', 'features': 128, 'out_channels': [96, 192, 384, 768]}, - 'vitl': {'encoder': 'vitl', 'features': 256, 'out_channels': [256, 512, 1024, 1024]}, - 'vitg': {'encoder': 'vitg', 'features': 384, 'out_channels': [1536, 1536, 1536, 1536]} - } - - # Load the pretrained model of depth anything - dinov3_repo_dir="./depth_anything_v2_metric/depth_anything_v2/dinov3" # 你的本地 repo - dinov3_arch="dinov3_vitl16" - dinov3_weight="" - - depth_anything = DepthAnythingV2( - **{**model_configs[midas_model_type], 'max_depth': 1.0}, - dinov3_repo_dir=dinov3_repo_dir, - dinov3_arch=dinov3_arch, - dinov3_weight=dinov3_weight - ) - - - self.core = depth_anything - for param in self.core.parameters(): - param.requires_grad = True - - - def forward(self, image): - if image.dim() == 3: - image = image.unsqueeze(0) - - erp_pred, mask_pred = self.core(image) - erp_pred = erp_pred.unsqueeze(1) - erp_pred[erp_pred < 0] = 0 - mask_pred = mask_pred.unsqueeze(1) - outputs = {} - outputs["pred_depth"] = erp_pred * self.max_depth - outputs["pred_mask"] = mask_pred - - - return outputs - - def get_encoder_decoder_params(self): - encoder_params = list(self.core.pretrained.parameters()) - decoder_params = list(self.core.depth_head.parameters()) - mask_params = list(self.core.mask_head.parameters()) - - return encoder_params, decoder_params, mask_params - - @torch.no_grad() - def infer_image(self, raw_image, input_size=518): - image, (h, w) = self.image2tensor(raw_image, input_size) - - depth = self.forward(image)["pred_depth"] - - depth = F.interpolate(depth, (h, w), mode="bilinear", align_corners=True)[0, 0] - - return depth.cpu().numpy() - - def image2tensor(self, raw_image, input_size=518): - transform = Compose([ - Resize( - width=input_size * 2, - height=input_size, - resize_target=False, - keep_aspect_ratio=True, - ensure_multiple_of=self.core.patch_size, - # 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(), - ]) - - h, w = raw_image.shape[:2] - - image = cv2.cvtColor(raw_image, cv2.COLOR_BGR2RGB) / 255.0 - - image = transform({'image': image})['image'] - image = torch.from_numpy(image).unsqueeze(0) - - DEVICE = 'cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu' - image = image.to(DEVICE) - - return image, (h, w) - -@register('dap') -def make_model(midas_model_type='vitl', fine_tune_type='none', min_depth=0.001, max_depth=1.0, train_decoder=True): - args = Namespace() - args.midas_model_type = midas_model_type - args.fine_tune_type = fine_tune_type - args.min_depth = min_depth - args.max_depth = max_depth - args.train_decoder = train_decoder - return DAP(args) diff --git a/DAP-main-2/networks/dpt.py b/DAP-main-2/networks/dpt.py deleted file mode 100644 index 862b4caaa45905732111b1c6b24bdc4b39446721..0000000000000000000000000000000000000000 --- a/DAP-main-2/networks/dpt.py +++ /dev/null @@ -1,202 +0,0 @@ -import argparse -import torch -import torch.nn as nn -import torch.nn.functional as F -from huggingface_hub import PyTorchModelHubMixin, hf_hub_download - -from .blocks import FeatureFusionBlock, _make_scratch - -from argparse import Namespace -from .models import register - - -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 = [] - print(out_features) - 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_feats = [path_4, path_3, path_2, path_1] - - 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_feats = out - out = self.scratch.output_conv2(out) - - # return out, out_feats - 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) - 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) - self.mask_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, depth_feats = self.depth_head(features, patch_h, patch_w) - depth = self.depth_head(features, patch_h, patch_w) - - mask = self.mask_head(features, patch_h, patch_w) - - depth = F.interpolate(depth, size=(h, w), mode="bilinear", align_corners=True) - depth = F.relu(depth) - - mask = F.interpolate(mask, size=(h, w), mode="bilinear", align_corners=True) - mask = F.relu(mask) - # return depth, depth_feats - return depth, mask - - -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) - \ No newline at end of file diff --git a/DAP-main-2/networks/models.py b/DAP-main-2/networks/models.py deleted file mode 100644 index 136cce9202106162c9e0d0816d89a9ff9b9ccb2f..0000000000000000000000000000000000000000 --- a/DAP-main-2/networks/models.py +++ /dev/null @@ -1,20 +0,0 @@ -import copy - -models = {} - -def register(name): - def decorator(cls): - models[name] = cls - return cls - return decorator - -def make(model_spec, args=None, load_sd=False): - if args is not None: - model_args = copy.deepcopy(model_spec['args']) - model_args.update(args) - else: - model_args = model_spec['args'] - model = models[model_spec['name']](**model_args) - if load_sd: - model.load_state_dict(model_spec['sd']) - return model \ No newline at end of file diff --git a/DAP-main-2/networks/projection_utils.py b/DAP-main-2/networks/projection_utils.py deleted file mode 100644 index 93b01190da8b061f7c1bc7dbf03a62e1f91e4289..0000000000000000000000000000000000000000 --- a/DAP-main-2/networks/projection_utils.py +++ /dev/null @@ -1,478 +0,0 @@ -import numpy as np -import torch -import torch.nn as nn -import torch.nn.functional as F -from scipy.ndimage import map_coordinates -import cv2 -import math -from os import makedirs -from os.path import join, exists - -# Based on https://github.com/sunset1995/py360convert -class Equirec2Cube: - def __init__(self, equ_h, equ_w, face_w): - ''' - equ_h: int, height of the equirectangular image - equ_w: int, width of the equirectangular image - face_w: int, the length of each face of the cubemap - ''' - - self.equ_h = equ_h - self.equ_w = equ_w - self.face_w = face_w - - self._xyzcube() - self._xyz2coor() - - # For convert R-distance to Z-depth for CubeMaps - cosmap = 1 / np.sqrt((2 * self.grid[..., 0]) ** 2 + (2 * self.grid[..., 1]) ** 2 + 1) - self.cosmaps = np.concatenate(6 * [cosmap], axis=1)[..., np.newaxis] - - def _xyzcube(self): - ''' - Compute the xyz cordinates of the unit cube in [F R B L U D] format. - ''' - self.xyz = np.zeros((self.face_w, self.face_w * 6, 3), np.float32) - rng = np.linspace(-0.5, 0.5, num=self.face_w, dtype=np.float32) - self.grid = np.stack(np.meshgrid(rng, -rng), -1) - - # Front face (z = 0.5) - self.xyz[:, 0 * self.face_w:1 * self.face_w, [0, 1]] = self.grid - self.xyz[:, 0 * self.face_w:1 * self.face_w, 2] = 0.5 - - # Right face (x = 0.5) - self.xyz[:, 1 * self.face_w:2 * self.face_w, [2, 1]] = self.grid[:, ::-1] - self.xyz[:, 1 * self.face_w:2 * self.face_w, 0] = 0.5 - - # Back face (z = -0.5) - self.xyz[:, 2 * self.face_w:3 * self.face_w, [0, 1]] = self.grid[:, ::-1] - self.xyz[:, 2 * self.face_w:3 * self.face_w, 2] = -0.5 - - # Left face (x = -0.5) - self.xyz[:, 3 * self.face_w:4 * self.face_w, [2, 1]] = self.grid - self.xyz[:, 3 * self.face_w:4 * self.face_w, 0] = -0.5 - - # Up face (y = 0.5) - self.xyz[:, 4 * self.face_w:5 * self.face_w, [0, 2]] = self.grid[::-1, :] - self.xyz[:, 4 * self.face_w:5 * self.face_w, 1] = 0.5 - - # Down face (y = -0.5) - self.xyz[:, 5 * self.face_w:6 * self.face_w, [0, 2]] = self.grid - self.xyz[:, 5 * self.face_w:6 * self.face_w, 1] = -0.5 - - def _xyz2coor(self): - - # x, y, z to longitude and latitude - x, y, z = np.split(self.xyz, 3, axis=-1) - lon = np.arctan2(x, z) - c = np.sqrt(x ** 2 + z ** 2) - lat = np.arctan2(y, c) - - # longitude and latitude to equirectangular coordinate - self.coor_x = (lon / (2 * np.pi) + 0.5) * self.equ_w - 0.5 - self.coor_y = (-lat / np.pi + 0.5) * self.equ_h - 0.5 - - def sample_equirec(self, e_img, order=0): - pad_u = np.roll(e_img[[0]], self.equ_w // 2, 1) - pad_d = np.roll(e_img[[-1]], self.equ_w // 2, 1) - e_img = np.concatenate([e_img, pad_d, pad_u], 0) - # pad_l = e_img[:, [0]] - # pad_r = e_img[:, [-1]] - # e_img = np.concatenate([e_img, pad_l, pad_r], 1) - - return map_coordinates(e_img, [self.coor_y, self.coor_x], - order=order, mode='wrap')[..., 0] - - def run(self, equ_img, equ_dep=None): - - h, w = equ_img.shape[:2] - if h != self.equ_h or w != self.equ_w: - equ_img = cv2.resize(equ_img, (self.equ_w, self.equ_h)) - if equ_dep is not None: - equ_dep = cv2.resize(equ_dep, (self.equ_w, self.equ_h), interpolation=cv2.INTER_NEAREST) - - cube_img = np.stack([self.sample_equirec(equ_img[..., i], order=1) - for i in range(equ_img.shape[2])], axis=-1) - - if equ_dep is not None: - cube_dep = np.stack([self.sample_equirec(equ_dep[..., i], order=0) - for i in range(equ_dep.shape[2])], axis=-1) - cube_dep = cube_dep * self.cosmaps - - if equ_dep is not None: - return cube_img, cube_dep - else: - return cube_img - -# Based on https://github.com/sunset1995/py360convert -class Cube2Equirec(nn.Module): - def __init__(self, face_w, equ_h, equ_w): - super(Cube2Equirec, self).__init__() - ''' - face_w: int, the length of each face of the cubemap - equ_h: int, height of the equirectangular image - equ_w: int, width of the equirectangular image - ''' - - self.face_w = face_w - self.equ_h = equ_h - self.equ_w = equ_w - - - # Get face id to each pixel: 0F 1R 2B 3L 4U 5D - self._equirect_facetype() - self._equirect_faceuv() - - - def _equirect_facetype(self): - ''' - 0F 1R 2B 3L 4U 5D - ''' - tp = np.roll(np.arange(4).repeat(self.equ_w // 4)[None, :].repeat(self.equ_h, 0), 3 * self.equ_w // 8, 1) - - # Prepare ceil mask - mask = np.zeros((self.equ_h, self.equ_w // 4), bool) - idx = np.linspace(-np.pi, np.pi, self.equ_w // 4) / 4 - idx = self.equ_h // 2 - np.round(np.arctan(np.cos(idx)) * self.equ_h / np.pi).astype(int) - for i, j in enumerate(idx): - mask[:j, i] = 1 - mask = np.roll(np.concatenate([mask] * 4, 1), 3 * self.equ_w // 8, 1) - - tp[mask] = 4 - tp[np.flip(mask, 0)] = 5 - - self.tp = tp - self.mask = mask - - def _equirect_faceuv(self): - - lon = ((np.linspace(0, self.equ_w -1, num=self.equ_w, dtype=np.float32 ) +0.5 ) /self.equ_w - 0.5 ) * 2 *np.pi - lat = -((np.linspace(0, self.equ_h -1, num=self.equ_h, dtype=np.float32 ) +0.5 ) /self.equ_h -0.5) * np.pi - - lon, lat = np.meshgrid(lon, lat) - - coor_u = np.zeros((self.equ_h, self.equ_w), dtype=np.float32) - coor_v = np.zeros((self.equ_h, self.equ_w), dtype=np.float32) - - for i in range(4): - mask = (self.tp == i) - coor_u[mask] = 0.5 * np.tan(lon[mask] - np.pi * i / 2) - coor_v[mask] = -0.5 * np.tan(lat[mask]) / np.cos(lon[mask] - np.pi * i / 2) - - mask = (self.tp == 4) - c = 0.5 * np.tan(np.pi / 2 - lat[mask]) - coor_u[mask] = c * np.sin(lon[mask]) - coor_v[mask] = c * np.cos(lon[mask]) - - mask = (self.tp == 5) - c = 0.5 * np.tan(np.pi / 2 - np.abs(lat[mask])) - coor_u[mask] = c * np.sin(lon[mask]) - coor_v[mask] = -c * np.cos(lon[mask]) - - # Final renormalize - coor_u = (np.clip(coor_u, -0.5, 0.5)) * 2 - coor_v = (np.clip(coor_v, -0.5, 0.5)) * 2 - - # Convert to torch tensor - self.tp = torch.from_numpy(self.tp.astype(np.float32) / 2.5 - 1) - self.coor_u = torch.from_numpy(coor_u) - self.coor_v = torch.from_numpy(coor_v) - - sample_grid = torch.stack([self.coor_u, self.coor_v, self.tp], dim=-1).view(1, 1, self.equ_h, self.equ_w, 3) - self.sample_grid = nn.Parameter(sample_grid, requires_grad=False) - - def forward(self, cube_feat): - - bs, ch, h, w = cube_feat.shape - assert h == self.face_w and w // 6 == self.face_w - - cube_feat = cube_feat.view(bs, ch, 1, h, w) - cube_feat = torch.cat(torch.split(cube_feat, self.face_w, dim=-1), dim=2) - - cube_feat = cube_feat.view([bs, ch, 6, self.face_w, self.face_w]) - sample_grid = torch.cat(bs * [self.sample_grid], dim=0) - equi_feat = F.grid_sample(cube_feat, sample_grid, padding_mode="border", align_corners=True) - - return equi_feat.squeeze(2) - -# generate patches in a closed-form -# the transformation and equation is referred from http://blog.nitishmutha.com/equirectangular/360degree/2017/06/12/How-to-project-Equirectangular-image-to-rectilinear-view.html -def pair(t): - return t if isinstance(t, tuple) else (t, t) - -def uv2xyz(uv): - xyz = np.zeros((*uv.shape[:-1], 3), dtype = np.float32) - xyz[..., 0] = np.multiply(np.cos(uv[..., 1]), np.sin(uv[..., 0])) - xyz[..., 1] = np.multiply(np.cos(uv[..., 1]), np.cos(uv[..., 0])) - xyz[..., 2] = np.sin(uv[..., 1]) - return xyz - -def equi2pers(erp_img, fov, nrows, patch_size): - bs, _, erp_h, erp_w = erp_img.shape - height, width = pair(patch_size) - fov_h, fov_w = pair(fov) - FOV = torch.tensor([fov_w/360.0, fov_h/180.0], dtype=torch.float32) - - PI = math.pi - PI_2 = math.pi * 0.5 - PI2 = math.pi * 2 - yy, xx = torch.meshgrid(torch.linspace(0, 1, height), torch.linspace(0, 1, width)) - screen_points = torch.stack([xx.flatten(), yy.flatten()], -1) - - if nrows==4: - num_rows = 4 - num_cols = [3, 6, 6, 3] - phi_centers = [-67.5, -22.5, 22.5, 67.5] - if nrows==6: - num_rows = 6 - num_cols = [3, 8, 12, 12, 8, 3] - phi_centers = [-75.2, -45.93, -15.72, 15.72, 45.93, 75.2] - if nrows==3: - num_rows = 3 - num_cols = [3, 4, 3] - phi_centers = [-60, 0, 60] - if nrows==5: - num_rows = 5 - num_cols = [3, 6, 8, 6, 3] - phi_centers = [-72.2, -36.1, 0, 36.1, 72.2] - - phi_interval = 180 // num_rows - all_combos = [] - erp_mask = [] - for i, n_cols in enumerate(num_cols): - for j in np.arange(n_cols): - theta_interval = 360 / n_cols - theta_center = j * theta_interval + theta_interval / 2 - - center = [theta_center, phi_centers[i]] - all_combos.append(center) - up = phi_centers[i] + phi_interval / 2 - down = phi_centers[i] - phi_interval / 2 - left = theta_center - theta_interval / 2 - right = theta_center + theta_interval / 2 - up = int((up + 90) / 180 * erp_h) - down = int((down + 90) / 180 * erp_h) - left = int(left / 360 * erp_w) - right = int(right / 360 * erp_w) - mask = np.zeros((erp_h, erp_w), dtype=int) - mask[down:up, left:right] = 1 - erp_mask.append(mask) - all_combos = np.vstack(all_combos) - shifts = np.arange(all_combos.shape[0]) * width - shifts = torch.from_numpy(shifts).float() - erp_mask = np.stack(erp_mask) - erp_mask = torch.from_numpy(erp_mask).float() - num_patch = all_combos.shape[0] - - center_point = torch.from_numpy(all_combos).float() # -180 to 180, -90 to 90 - center_point[:, 0] = (center_point[:, 0]) / 360 #0 to 1 - center_point[:, 1] = (center_point[:, 1] + 90) / 180 #0 to 1 - - cp = center_point * 2 - 1 - center_p = cp.clone() - cp[:, 0] = cp[:, 0] * PI - cp[:, 1] = cp[:, 1] * PI_2 - cp = cp.unsqueeze(1) - convertedCoord = screen_points * 2 - 1 - convertedCoord[:, 0] = convertedCoord[:, 0] * PI - convertedCoord[:, 1] = convertedCoord[:, 1] * PI_2 - convertedCoord = convertedCoord * (torch.ones(screen_points.shape, dtype=torch.float32) * FOV) - convertedCoord = convertedCoord.unsqueeze(0).repeat(cp.shape[0], 1, 1) - - x = convertedCoord[:, :, 0] - y = convertedCoord[:, :, 1] - - rou = torch.sqrt(x ** 2 + y ** 2) - c = torch.atan(rou) - sin_c = torch.sin(c) - cos_c = torch.cos(c) - lat = torch.asin(cos_c * torch.sin(cp[:, :, 1]) + (y * sin_c * torch.cos(cp[:, :, 1])) / rou) - lon = cp[:, :, 0] + torch.atan2(x * sin_c, rou * torch.cos(cp[:, :, 1]) * cos_c - y * torch.sin(cp[:, :, 1]) * sin_c) - lat_new = lat / PI_2 - lon_new = lon / PI - lon_new[lon_new > 1] -= 2 - lon_new[lon_new<-1] += 2 - - lon_new = lon_new.view(1, num_patch, height, width).permute(0, 2, 1, 3).contiguous().view(height, num_patch*width) - lat_new = lat_new.view(1, num_patch, height, width).permute(0, 2, 1, 3).contiguous().view(height, num_patch*width) - grid = torch.stack([lon_new, lat_new], -1) - grid = grid.unsqueeze(0).repeat(bs, 1, 1, 1).to(erp_img.device) - pers = F.grid_sample(erp_img, grid, mode='bilinear', padding_mode='border', align_corners=True) - pers = F.unfold(pers, kernel_size=(height, width), stride=(height, width)) - pers = pers.reshape(bs, -1, height, width, num_patch) - - grid_tmp = torch.stack([lon, lat], -1) - xyz = uv2xyz(grid_tmp) - xyz = xyz.reshape(num_patch, height, width, 3).transpose(0, 3, 1, 2) - xyz = torch.from_numpy(xyz).to(pers.device).contiguous() - - uv = grid[0, ...].reshape(height, width, num_patch, 2).permute(2, 3, 0, 1) - uv = uv.contiguous() - return pers, xyz, uv, center_p - -def pers2equi(pers_img, fov, nrows, patch_size, erp_size, layer_name): - bs = pers_img.shape[0] - channel = pers_img.shape[1] - device=pers_img.device - height, width = pair(patch_size) - fov_h, fov_w = pair(fov) - erp_h, erp_w = pair(erp_size) - n_patch = pers_img.shape[-1] - grid_dir = './grid' - if not exists(grid_dir): - makedirs(grid_dir) - grid_file = join(grid_dir, layer_name + '.pth') - - if not exists(grid_file): - FOV = torch.tensor([fov_w/360.0, fov_h/180.0], dtype=torch.float32) - - PI = math.pi - PI_2 = math.pi * 0.5 - PI2 = math.pi * 2 - - if nrows==4: - num_rows = 4 - num_cols = [3, 6, 6, 3] - phi_centers = [-67.5, -22.5, 22.5, 67.5] - if nrows==6: - num_rows = 6 - num_cols = [3, 8, 12, 12, 8, 3] - phi_centers = [-75.2, -45.93, -15.72, 15.72, 45.93, 75.2] - if nrows==3: - num_rows = 3 - num_cols = [3, 4, 3] - phi_centers = [-59.6, 0, 59.6] - if nrows==5: - num_rows = 5 - num_cols = [3, 6, 8, 6, 3] - phi_centers = [-72.2, -36.1, 0, 36.1, 72.2] - phi_interval = 180 // num_rows - all_combos = [] - - for i, n_cols in enumerate(num_cols): - for j in np.arange(n_cols): - theta_interval = 360 / n_cols - theta_center = j * theta_interval + theta_interval / 2 - - center = [theta_center, phi_centers[i]] - all_combos.append(center) - - - all_combos = np.vstack(all_combos) - n_patch = all_combos.shape[0] - - center_point = torch.from_numpy(all_combos).float() # -180 to 180, -90 to 90 - center_point[:, 0] = (center_point[:, 0]) / 360 #0 to 1 - center_point[:, 1] = (center_point[:, 1] + 90) / 180 #0 to 1 - - cp = center_point * 2 - 1 - cp[:, 0] = cp[:, 0] * PI - cp[:, 1] = cp[:, 1] * PI_2 - cp = cp.unsqueeze(1) - - lat_grid, lon_grid = torch.meshgrid(torch.linspace(-PI_2, PI_2, erp_h), torch.linspace(-PI, PI, erp_w)) - lon_grid = lon_grid.float().reshape(1, -1)#.repeat(num_rows*num_cols, 1) - lat_grid = lat_grid.float().reshape(1, -1)#.repeat(num_rows*num_cols, 1) - cos_c = torch.sin(cp[..., 1]) * torch.sin(lat_grid) + torch.cos(cp[..., 1]) * torch.cos(lat_grid) * torch.cos(lon_grid - cp[..., 0]) - new_x = (torch.cos(lat_grid) * torch.sin(lon_grid - cp[..., 0])) / cos_c - new_y = (torch.cos(cp[..., 1])*torch.sin(lat_grid) - torch.sin(cp[...,1])*torch.cos(lat_grid)*torch.cos(lon_grid-cp[...,0])) / cos_c - new_x = new_x / FOV[0] / PI # -1 to 1 - new_y = new_y / FOV[1] / PI_2 - cos_c_mask = cos_c.reshape(n_patch, erp_h, erp_w) - cos_c_mask = torch.where(cos_c_mask > 0, 1, 0) - - w_list = torch.zeros((n_patch, erp_h, erp_w, 4), dtype=torch.float32) - - new_x_patch = (new_x + 1) * 0.5 * height - new_y_patch = (new_y + 1) * 0.5 * width - new_x_patch = new_x_patch.reshape(n_patch, erp_h, erp_w) - new_y_patch = new_y_patch.reshape(n_patch, erp_h, erp_w) - mask = torch.where((new_x_patch < width) & (new_x_patch > 0) & (new_y_patch < height) & (new_y_patch > 0), 1, 0) - mask *= cos_c_mask - - x0 = torch.floor(new_x_patch).type(torch.int64) - x1 = x0 + 1 - y0 = torch.floor(new_y_patch).type(torch.int64) - y1 = y0 + 1 - - x0 = torch.clamp(x0, 0, width-1) - x1 = torch.clamp(x1, 0, width-1) - y0 = torch.clamp(y0, 0, height-1) - y1 = torch.clamp(y1, 0, height-1) - - wa = (x1.type(torch.float32)-new_x_patch) * (y1.type(torch.float32)-new_y_patch) - wb = (x1.type(torch.float32)-new_x_patch) * (new_y_patch-y0.type(torch.float32)) - wc = (new_x_patch-x0.type(torch.float32)) * (y1.type(torch.float32)-new_y_patch) - wd = (new_x_patch-x0.type(torch.float32)) * (new_y_patch-y0.type(torch.float32)) - - wa = wa * mask.expand_as(wa) - wb = wb * mask.expand_as(wb) - wc = wc * mask.expand_as(wc) - wd = wd * mask.expand_as(wd) - - w_list[..., 0] = wa - w_list[..., 1] = wb - w_list[..., 2] = wc - w_list[..., 3] = wd - - - save_file = {'x0':x0, 'y0':y0, 'x1':x1, 'y1':y1, 'w_list': w_list, 'mask':mask} - torch.save(save_file, grid_file) - else: - # the online merge really takes time - # pre-calculate the grid for once and use it during training - load_file = torch.load(grid_file) - #print('load_file') - x0 = load_file['x0'] - y0 = load_file['y0'] - x1 = load_file['x1'] - y1 = load_file['y1'] - w_list = load_file['w_list'] - mask = load_file['mask'] - - w_list = w_list.to(device) - mask = mask.to(device) - z = torch.arange(n_patch) - z = z.reshape(n_patch, 1, 1) - Ia = pers_img[:, :, y0, x0, z] - Ib = pers_img[:, :, y1, x0, z] - Ic = pers_img[:, :, y0, x1, z] - Id = pers_img[:, :, y1, x1, z] - output_a = Ia * mask.expand_as(Ia) - output_b = Ib * mask.expand_as(Ib) - output_c = Ic * mask.expand_as(Ic) - output_d = Id * mask.expand_as(Id) - - output_a = output_a.permute(0, 1, 3, 4, 2) - output_b = output_b.permute(0, 1, 3, 4, 2) - output_c = output_c.permute(0, 1, 3, 4, 2) - output_d = output_d.permute(0, 1, 3, 4, 2) - w_list = w_list.permute(1, 2, 0, 3) - w_list = w_list.flatten(2) - w_list *= torch.gt(w_list, 1e-5).type(torch.float32) - w_list = F.normalize(w_list, p=1, dim=-1).reshape(erp_h, erp_w, n_patch, 4) - w_list = w_list.unsqueeze(0).unsqueeze(0) - output = output_a * w_list[..., 0] + output_b * w_list[..., 1] + \ - output_c * w_list[..., 2] + output_d * w_list[..., 3] - img_erp = output.sum(-1) - - return img_erp - -def img2windows(img, H_sp, W_sp): - """ - img: B C H W - """ - B, C, H, W = img.shape - img_reshape = img.view(B, C, H // H_sp, H_sp, W // W_sp, W_sp) - img_perm = img_reshape.permute(0, 2, 4, 3, 5, 1).contiguous().reshape(-1, H_sp, W_sp, C) - return img_perm - -def windows2img(img_splits_hw, H_sp, W_sp, H, W): - """ - img_splits_hw: B' H W C - """ - B = int(img_splits_hw.shape[0] / (H * W / H_sp / W_sp)) - - img = img_splits_hw.view(B, H // H_sp, W // W_sp, H_sp, W_sp, -1) - img = img.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) - return img \ No newline at end of file diff --git a/DAP-main-2/requirements.txt b/DAP-main-2/requirements.txt deleted file mode 100644 index dbd9e3afdaa581ca60a6affe98e734e1f489cac2..0000000000000000000000000000000000000000 --- a/DAP-main-2/requirements.txt +++ /dev/null @@ -1,15 +0,0 @@ -gradio_imageslider -gradio==4.36.0 -torch==2.7.1 -torchmetrics==1.8.2 -torchvision==0.22.1 -tornado==6.5.2 -opencv-python -matplotlib -huggingface_hub -einops -safetensors -open3d -tensorboardX -mmengine -pyexr diff --git a/DAP-main-2/test/__pycache__/metrics_st.cpython-310.pyc b/DAP-main-2/test/__pycache__/metrics_st.cpython-310.pyc deleted file mode 100644 index 562a47664bc726fc2dabb9fcbacaace43045ec44..0000000000000000000000000000000000000000 Binary files a/DAP-main-2/test/__pycache__/metrics_st.cpython-310.pyc and /dev/null differ diff --git a/DAP-main-2/test/eval.py b/DAP-main-2/test/eval.py deleted file mode 100644 index 9523a0354dbce0a1ded82a404de3974d7cf8239f..0000000000000000000000000000000000000000 --- a/DAP-main-2/test/eval.py +++ /dev/null @@ -1,103 +0,0 @@ -from __future__ import absolute_import, division, print_function -import os -import argparse -import tqdm -import yaml -import numpy as np -import cv2 -import sys -sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) -import torch -from torch.utils.data import DataLoader -import torch.nn as nn -import torch.nn.functional as F - -import datasets -from metrics_st import Evaluator -from networks.models import * - -def main(config, i): - model_path = os.path.join(config["load_weights_dir"], 'model.pth') - model_dict = torch.load(model_path) - - # data - datasets_dict = {"stanford2d3d": datasets.Stanford2D3D, - "deep360": datasets.Deep360, - "insta23k": datasets.Insta23k, - "m3d": datasets.M3D,} - - cf_test = config['test_dataset_' + str(i+1)] - dataset_val = datasets_dict[cf_test['name']] - test_dataset = dataset_val(cf_test['root_path'], - cf_test['list_path'], - cf_test['args']['height'], - cf_test['args']['width'], - cf_test['args']['augment_color'], - cf_test['args']['augment_flip'], - cf_test['args']['augment_rotation'], - cf_test['args']['repeat'], - is_training=False) - test_loader = DataLoader(test_dataset, - cf_test['batch_size'], - False, - num_workers=cf_test['num_workers'], - pin_memory=True, - drop_last=False) - num_test_samples = len(test_dataset) - num_steps = num_test_samples // cf_test['batch_size'] - print("Num. of test samples:", num_test_samples, "Num. of steps:", num_steps, "\n") - - # network - model = make(config['model']) - if any(key.startswith('module') for key in model_dict.keys()): - model = nn.DataParallel(model) - - model.cuda() - model_state_dict = model.state_dict() - model.load_state_dict({k: v for k, v in model_dict.items() if k in model_state_dict}, strict=False) - model.eval() - - evaluator = Evaluator(config['median_align']) - evaluator.reset_eval_metrics() - pbar = tqdm.tqdm(test_loader) - pbar.set_description("Testing") - - with torch.no_grad(): - for batch_idx, inputs in enumerate(pbar): - - equi_inputs = inputs["rgb"].cuda() - outputs = model(equi_inputs) - - outputs['pred_mask'] = 1 - outputs['pred_mask'] - outputs['pred_mask'] = (outputs['pred_mask'] > 0.5) - outputs['pred_depth'][~outputs['pred_mask']] = 1 - - - pred_depth = outputs['pred_depth'].clone() - pred_depth =pred_depth.detach().cpu() - gt_depth = inputs["gt_depth"] - mask = inputs["val_mask"] - - - for i in range(gt_depth.shape[0]): - evaluator.compute_eval_metrics(gt_depth[i:i + 1], pred_depth[i:i + 1], mask[i:i + 1]) - - - evaluator.print() - - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument('--config', default='config/test.yaml') - parser.add_argument('--gpu', default='0') - args = parser.parse_args() - - os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID' - os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu - - with open(args.config, 'r') as f: - config = yaml.load(f, Loader=yaml.FullLoader) - print('config loaded.') - - main(config, 0) \ No newline at end of file diff --git a/DAP-main-2/test/infer.py b/DAP-main-2/test/infer.py deleted file mode 100644 index df0ac1535954b5a794e139cdc217855ce64b2615..0000000000000000000000000000000000000000 --- a/DAP-main-2/test/infer.py +++ /dev/null @@ -1,157 +0,0 @@ -from __future__ import absolute_import, division, print_function - -import os -import sys -import cv2 -import torch -import yaml -import argparse -import numpy as np -import torch.nn as nn -from tqdm import tqdm - -PROJECT_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) -sys.path.append(PROJECT_ROOT) - -from networks.models import make # 建议用 make，而不是 import * - -import matplotlib - -def colorize_depth_fixed(depth_u8: np.ndarray, cmap: str = "Spectral") -> np.ndarray: - """ - depth_u8: uint8, 0~255 - return: RGB uint8 - """ - disp = depth_u8.astype(np.float32) / 255.0 - colored = matplotlib.colormaps[cmap](disp)[..., :3] - colored = (colored * 255).astype(np.uint8) - return np.ascontiguousarray(colored) - -def ensure_dir_for_file(path: str): - os.makedirs(os.path.dirname(path), exist_ok=True) - -def load_model(config): - model_path = os.path.join(config["load_weights_dir"], "model.pth") - print(f"🔹 Loading model weights from: {model_path}") - - device = "cuda" if torch.cuda.is_available() else "cpu" - state = torch.load(model_path, map_location=device) - - m = make(config["model"]) - if any(k.startswith("module") for k in state.keys()): - m = nn.DataParallel(m) - - m = m.to(device) - m_state = m.state_dict() - m.load_state_dict({k: v for k, v in state.items() if k in m_state}, strict=False) - m.eval() - - print("✅ Model loaded successfully.\n") - return m, device - -def infer_raw(model, device, img_rgb_u8: np.ndarray) -> np.ndarray: - """ - img_rgb_u8: HWC uint8 RGB - return: pred float32 (H,W) - """ - img = img_rgb_u8.astype(np.float32) / 255.0 - tensor = torch.from_numpy(img.transpose(2, 0, 1)).unsqueeze(0).to(device) - - with torch.inference_mode(): - outputs = model(tensor) - - if isinstance(outputs, dict) and "pred_depth" in outputs: - if "pred_mask" in outputs: - mask = 1 - outputs["pred_mask"] - mask = mask > 0.5 - outputs["pred_depth"][~mask] = 1 - pred = outputs["pred_depth"][0].detach().cpu().squeeze().numpy() - else: - pred = outputs[0].detach().cpu().squeeze().numpy() - - return pred.astype(np.float32) - -def pred_to_vis(pred: np.ndarray, vis_range: str = "100m", cmap: str = "Spectral"): - """ - return: - depth_gray_u8: (H,W) uint8 - depth_color_rgb: (H,W,3) uint8 RGB - """ - if vis_range == "100m": - pred_clip = np.clip(pred, 0.0, 1.0) - depth_gray = (pred_clip * 255).astype(np.uint8) - elif vis_range == "10m": - pred_clip = np.clip(pred, 0.0, 0.1) - depth_gray = (pred_clip * 10.0 * 255).astype(np.uint8) - else: - raise ValueError(f"Unknown vis_range: {vis_range} (use '100m' or '10m')") - - depth_color = colorize_depth_fixed(depth_gray, cmap=cmap) - return depth_gray, depth_color - -def infer_and_save(model, device, img_path, out_root, idx, vis_range="100m", cmap="Spectral"): - img_bgr = cv2.imread(img_path) - if img_bgr is None: - print(f"⚠️ Cannot read image: {img_path}") - return - - img_rgb = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB) - - pred = infer_raw(model, device, img_rgb) - - depth_gray, depth_color_rgb = pred_to_vis(pred, vis_range=vis_range, cmap=cmap) - - filename = f"{idx:06d}" - - pred_npy_path = os.path.join(out_root, "depth_npy", filename + ".npy") - gray_png_path = os.path.join(out_root, f"depth_vis_gray_{vis_range}", filename + ".png") - color_png_path = os.path.join(out_root, f"depth_vis_color_{vis_range}", filename + ".png") - - ensure_dir_for_file(pred_npy_path) - ensure_dir_for_file(gray_png_path) - ensure_dir_for_file(color_png_path) - - np.save(pred_npy_path, pred) - - cv2.imwrite(gray_png_path, depth_gray) - - cv2.imwrite(color_png_path, cv2.cvtColor(depth_color_rgb, cv2.COLOR_RGB2BGR)) - - -def main(config_path, txt_path, out_root, vis_range="100m", cmap="Spectral"): - with open(config_path, "r") as f: - config = yaml.load(f, Loader=yaml.FullLoader) - print("✅ Config loaded.") - - model, device = load_model(config) - - with open(txt_path, "r") as f: - img_list = [l.strip() for l in f.readlines() if l.strip()] - - print(f"🔹 Total images to infer: {len(img_list)}") - print(f"🔹 Visualization: {vis_range}, colormap: {cmap}\n") - - for idx, img_path in enumerate(tqdm(img_list, desc="Inferencing"), start=1): - infer_and_save(model, device, img_path, out_root, idx, vis_range=vis_range, cmap=cmap) - - print("\n🎯 推理完成！") - print(f" depth npy: {out_root}/depth_npy") - print(f" depth gray png: {out_root}/depth_vis_gray_{vis_range}") - print(f" depth color png: {out_root}/depth_vis_color_{vis_range}") - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument("--config", default="config/infer.yaml") - parser.add_argument("--txt", default="datasets/test.txt") - parser.add_argument("--output", default="test_output") - parser.add_argument("--gpu", default="0", help="使用的GPU编号") - - parser.add_argument("--vis", default="100m", choices=["100m", "10m"], help="可视化范围（只影响png，不影响npy）") - parser.add_argument("--cmap", default="Spectral", help="matplotlib colormap name, e.g. Spectral, Turbo, Viridis") - - args = parser.parse_args() - - os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" - os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu - - main(args.config, args.txt, args.output, vis_range=args.vis, cmap=args.cmap) diff --git a/DAP-main-2/test/metrics_st.py b/DAP-main-2/test/metrics_st.py deleted file mode 100644 index eda2acc5b512e21446a17c8d87caccfa66285263..0000000000000000000000000000000000000000 --- a/DAP-main-2/test/metrics_st.py +++ /dev/null @@ -1,156 +0,0 @@ -import os -import torch -import numpy as np - -#========================== -# Depth Prediction Metrics -#========================== - -def compute_depth_metrics(gt, pred, mask=None, median_align=False): - """Computation of metrics between predicted and ground truth depths - """ - - if mask is None: - mask = gt > 0 - - gt = gt.squeeze(1) - pred = pred.squeeze(1) - mask = mask.squeeze(1) - gt = gt[mask] - pred = pred[mask] - - - thresh = torch.max((gt / pred), (pred / gt)) - a1 = (thresh < 1.25 ).float().mean() - a2 = (thresh < 1.25 ** 2).float().mean() - a3 = (thresh < 1.25 ** 3).float().mean() - - rmse = (gt - pred) ** 2 - rmse = torch.sqrt(rmse).mean() - - rmse_log = (torch.log10(gt) - torch.log10(pred)) ** 2 - rmse_log = torch.sqrt(rmse_log).mean() - - abs_ = torch.mean(torch.abs(gt - pred)) - - abs_rel = torch.mean(torch.abs(gt - pred) / gt) - - sq_rel = torch.mean((gt - pred) ** 2 / gt) - - log10 = torch.mean(torch.abs(torch.log10(pred/gt))) - - return abs_, abs_rel, sq_rel, rmse, rmse_log, log10, a1, a2, a3 - - -# From https://github.com/fyu/drn -class AverageMeter(object): - """Computes and stores the average and current value""" - - def __init__(self): - self.vals = [] - self.reset() - - def reset(self): - self.val = 0 - self.avg = 0 - self.sum = 0 - self.count = 0 - - def update(self, val, n=1): - self.vals.append(val) - self.val = val - self.sum += val * n - self.count += n - self.avg = self.sum / self.count - - def to_dict(self): - return { - 'val': self.val, - 'sum': self.sum, - 'count': self.count, - 'avg': self.avg - } - - def from_dict(self, meter_dict): - self.val = meter_dict['val'] - self.sum = meter_dict['sum'] - self.count = meter_dict['count'] - self.avg = meter_dict['avg'] - - -class Evaluator(object): - - def __init__(self, median_align=False): - - self.median_align = median_align - # Error and Accuracy metric trackers - self.metrics = {} - self.metrics["err/abs_"] = AverageMeter() - self.metrics["err/abs_rel"] = AverageMeter() - self.metrics["err/sq_rel"] = AverageMeter() - self.metrics["err/rms"] = AverageMeter() - self.metrics["err/log_rms"] = AverageMeter() - self.metrics["err/log10"] = AverageMeter() - self.metrics["acc/a1"] = AverageMeter() - self.metrics["acc/a2"] = AverageMeter() - self.metrics["acc/a3"] = AverageMeter() - - def reset_eval_metrics(self): - """ - Resets metrics used to evaluate the model - """ - self.metrics["err/abs_"].reset() - self.metrics["err/abs_rel"].reset() - self.metrics["err/sq_rel"].reset() - self.metrics["err/rms"].reset() - self.metrics["err/log_rms"].reset() - self.metrics["err/log10"].reset() - self.metrics["acc/a1"].reset() - self.metrics["acc/a2"].reset() - self.metrics["acc/a3"].reset() - - def compute_eval_metrics(self, gt_depth, pred_depth, mask): - """ - Computes metrics used to evaluate the model - """ - N = gt_depth.shape[0] - - abs_, abs_rel, sq_rel, rms, rms_log, log10, a1, a2, a3 = \ - compute_depth_metrics(gt_depth, pred_depth, mask, self.median_align) - - self.metrics["err/abs_"].update(abs_, N) - self.metrics["err/abs_rel"].update(abs_rel, N) - self.metrics["err/sq_rel"].update(sq_rel, N) - self.metrics["err/rms"].update(rms, N) - self.metrics["err/log_rms"].update(rms_log, N) - self.metrics["err/log10"].update(log10, N) - self.metrics["acc/a1"].update(a1, N) - self.metrics["acc/a2"].update(a2, N) - self.metrics["acc/a3"].update(a3, N) - - def print(self, dir=None): - avg_metrics = [] - avg_metrics_print = [] - - avg_metrics.append(self.metrics["err/abs_"].avg) - avg_metrics.append(self.metrics["err/abs_rel"].avg) - avg_metrics.append(self.metrics["err/sq_rel"].avg) - avg_metrics.append(self.metrics["err/rms"].avg) - avg_metrics.append(self.metrics["err/log_rms"].avg) - avg_metrics.append(self.metrics["err/log10"].avg) - avg_metrics.append(self.metrics["acc/a1"].avg) - avg_metrics.append(self.metrics["acc/a2"].avg) - avg_metrics.append(self.metrics["acc/a3"].avg) - avg_metrics_print.append(self.metrics["err/abs_rel"].avg) - avg_metrics_print.append(self.metrics["err/rms"].avg) - avg_metrics_print.append(self.metrics["acc/a1"].avg) - - print("\n "+ ("{:>8} | " * 3).format("abs_rel", "rms", "a1")) - print(("& {: 8.5f} " * 3).format(*avg_metrics_print)) - - if dir is not None: - file = os.path.join(dir, "result.txt") - with open(file, 'w') as f: - print("\n " + ("{:>9} | " * 9).format("abs_", "abs_rel", "sq_rel", "rms", "rms_log", - "log10", "a1", "a2", "a3"), file=f) - print(("& {: 8.5f} " * 9).format(*avg_metrics), file=f)