xsl
ce445b64a6
部署修复： - torch.load 增加 weights_only=False patch，兼容 PyTorch 2.6+ 加载旧权重 - OSS 改为懒加载，本地用 output_format=base64 无需配凭证即可启动 - 补全被 gitignore 误排除的必需代码：core/models/layers/data、models/layers/data、keypoints/lib - webui 训练命令 --xformers 改 --sdpa（修复 xformers 无 CUDA 支持报错） 功能调整： - hair_grow_service 端口改 8899、preview 路由修复（send_file） - list_hairstyles 增加发型白名单，测试页只展示当前5个发型 新增脚本： - train_lora_parallel.py：直接调 kohya 并行训练 LoRA（绕过 photo_service 串行限制） - train_hairstyles_parallel.py / train_batch_stepC.py：批量训练辅助脚本 - scripts/sync_data_to_server.sh：大文件断点续传到云服务器 文档： - docs/换发型集成文档.md：换发型完整流程、服务架构、资源依赖、训练方法、集成步骤
293 lines
10 KiB
Python
293 lines
10 KiB
Python
# ------------------------------------------------------------------------------
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# Copyright (c) Microsoft
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# Licensed under the MIT License.
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# Written by Bin Xiao (Bin.Xiao@microsoft.com)
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# ------------------------------------------------------------------------------
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import copy
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import logging
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import random
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import cv2
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import numpy as np
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import torch
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from torch.utils.data import Dataset
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from utils.transforms import get_affine_transform
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from utils.transforms import affine_transform
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from utils.transforms import fliplr_joints
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logger = logging.getLogger(__name__)
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class JointsDataset(Dataset):
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def __init__(self, cfg, root, image_set, is_train, transform=None):
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self.num_joints = 0
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self.pixel_std = 200
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self.flip_pairs = []
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self.parent_ids = []
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self.is_train = is_train
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self.root = root
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self.image_set = image_set
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self.output_path = cfg.OUTPUT_DIR
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self.data_format = cfg.DATASET.DATA_FORMAT
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self.scale_factor = cfg.DATASET.SCALE_FACTOR
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self.rotation_factor = cfg.DATASET.ROT_FACTOR
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self.flip = cfg.DATASET.FLIP
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self.num_joints_half_body = cfg.DATASET.NUM_JOINTS_HALF_BODY
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self.prob_half_body = cfg.DATASET.PROB_HALF_BODY
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self.color_rgb = cfg.DATASET.COLOR_RGB
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self.target_type = cfg.MODEL.TARGET_TYPE
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self.image_size = np.array(cfg.MODEL.IMAGE_SIZE)
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self.heatmap_size = np.array(cfg.MODEL.HEATMAP_SIZE)
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self.sigma = cfg.MODEL.SIGMA
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self.use_different_joints_weight = cfg.LOSS.USE_DIFFERENT_JOINTS_WEIGHT
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self.joints_weight = 1
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self.transform = transform
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self.db = []
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def _get_db(self):
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raise NotImplementedError
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def evaluate(self, cfg, preds, output_dir, *args, **kwargs):
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raise NotImplementedError
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def half_body_transform(self, joints, joints_vis):
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upper_joints = []
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lower_joints = []
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for joint_id in range(self.num_joints):
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if joints_vis[joint_id][0] > 0:
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if joint_id in self.upper_body_ids:
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upper_joints.append(joints[joint_id])
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else:
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lower_joints.append(joints[joint_id])
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if np.random.randn() < 0.5 and len(upper_joints) > 2:
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selected_joints = upper_joints
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else:
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selected_joints = lower_joints \
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if len(lower_joints) > 2 else upper_joints
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if len(selected_joints) < 2:
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return None, None
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selected_joints = np.array(selected_joints, dtype=np.float32)
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center = selected_joints.mean(axis=0)[:2]
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left_top = np.amin(selected_joints, axis=0)
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right_bottom = np.amax(selected_joints, axis=0)
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w = right_bottom[0] - left_top[0]
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h = right_bottom[1] - left_top[1]
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if w > self.aspect_ratio * h:
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h = w * 1.0 / self.aspect_ratio
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elif w < self.aspect_ratio * h:
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w = h * self.aspect_ratio
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scale = np.array(
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[
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w * 1.0 / self.pixel_std,
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h * 1.0 / self.pixel_std
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],
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dtype=np.float32
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)
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scale = scale * 1.5
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return center, scale
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def __len__(self,):
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return len(self.db)
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def __getitem__(self, idx):
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db_rec = copy.deepcopy(self.db[idx])
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image_file = db_rec['image']
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filename = db_rec['filename'] if 'filename' in db_rec else ''
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imgnum = db_rec['imgnum'] if 'imgnum' in db_rec else ''
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if self.data_format == 'zip':
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from utils import zipreader
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data_numpy = zipreader.imread(
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image_file, cv2.IMREAD_COLOR | cv2.IMREAD_IGNORE_ORIENTATION
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)
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else:
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data_numpy = cv2.imread(
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image_file, cv2.IMREAD_COLOR | cv2.IMREAD_IGNORE_ORIENTATION
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)
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if self.color_rgb:
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data_numpy = cv2.cvtColor(data_numpy, cv2.COLOR_BGR2RGB)
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if data_numpy is None:
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logger.error('=> fail to read {}'.format(image_file))
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raise ValueError('Fail to read {}'.format(image_file))
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joints = db_rec['joints_3d']
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joints_vis = db_rec['joints_3d_vis']
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c = db_rec['center']
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s = db_rec['scale']
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score = db_rec['score'] if 'score' in db_rec else 1
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r = 0
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if self.is_train:
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if (np.sum(joints_vis[:, 0]) > self.num_joints_half_body
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and np.random.rand() < self.prob_half_body):
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c_half_body, s_half_body = self.half_body_transform(
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joints, joints_vis
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)
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if c_half_body is not None and s_half_body is not None:
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c, s = c_half_body, s_half_body
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sf = self.scale_factor
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rf = self.rotation_factor
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s = s * np.clip(np.random.randn()*sf + 1, 1 - sf, 1 + sf)
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r = np.clip(np.random.randn()*rf, -rf*2, rf*2) \
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if random.random() <= 0.6 else 0
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if self.flip and random.random() <= 0.5:
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data_numpy = data_numpy[:, ::-1, :]
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joints, joints_vis = fliplr_joints(
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joints, joints_vis, data_numpy.shape[1], self.flip_pairs)
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c[0] = data_numpy.shape[1] - c[0] - 1
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trans = get_affine_transform(c, s, r, self.image_size)
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input = cv2.warpAffine(
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data_numpy,
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trans,
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(int(self.image_size[0]), int(self.image_size[1])),
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flags=cv2.INTER_LINEAR)
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cv2.imshow('input', input)
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cv2.waitKey()
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if self.transform:
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input = self.transform(input)
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for i in range(self.num_joints):
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if joints_vis[i, 0] > 0.0:
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joints[i, 0:2] = affine_transform(joints[i, 0:2], trans)
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target, target_weight = self.generate_target(joints, joints_vis)
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target = torch.from_numpy(target)
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target_weight = torch.from_numpy(target_weight)
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meta = {
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'image': image_file,
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'filename': filename,
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'imgnum': imgnum,
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'joints': joints,
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'joints_vis': joints_vis,
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'center': c,
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'scale': s,
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'rotation': r,
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'score': score
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}
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return input, target, target_weight, meta
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def select_data(self, db):
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db_selected = []
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for rec in db:
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num_vis = 0
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joints_x = 0.0
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joints_y = 0.0
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for joint, joint_vis in zip(
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rec['joints_3d'], rec['joints_3d_vis']):
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if joint_vis[0] <= 0:
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continue
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num_vis += 1
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joints_x += joint[0]
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joints_y += joint[1]
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if num_vis == 0:
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continue
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joints_x, joints_y = joints_x / num_vis, joints_y / num_vis
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area = rec['scale'][0] * rec['scale'][1] * (self.pixel_std**2)
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joints_center = np.array([joints_x, joints_y])
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bbox_center = np.array(rec['center'])
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diff_norm2 = np.linalg.norm((joints_center-bbox_center), 2)
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ks = np.exp(-1.0*(diff_norm2**2) / ((0.2)**2*2.0*area))
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metric = (0.2 / 16) * num_vis + 0.45 - 0.2 / 16
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if ks > metric:
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db_selected.append(rec)
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logger.info('=> num db: {}'.format(len(db)))
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logger.info('=> num selected db: {}'.format(len(db_selected)))
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return db_selected
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def generate_target(self, joints, joints_vis):
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'''
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:param joints: [num_joints, 3]
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:param joints_vis: [num_joints, 3]
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:return: target, target_weight(1: visible, 0: invisible)
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'''
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target_weight = np.ones((self.num_joints, 1), dtype=np.float32)
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target_weight[:, 0] = joints_vis[:, 0]
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assert self.target_type == 'gaussian', \
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'Only support gaussian map now!'
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if self.target_type == 'gaussian':
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target = np.zeros((self.num_joints,
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self.heatmap_size[1],
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self.heatmap_size[0]),
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dtype=np.float32)
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tmp_size = self.sigma * 3
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for joint_id in range(self.num_joints):
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feat_stride = self.image_size / self.heatmap_size
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mu_x = int(joints[joint_id][0] / feat_stride[0] + 0.5)
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mu_y = int(joints[joint_id][1] / feat_stride[1] + 0.5)
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# Check that any part of the gaussian is in-bounds
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ul = [int(mu_x - tmp_size), int(mu_y - tmp_size)]
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br = [int(mu_x + tmp_size + 1), int(mu_y + tmp_size + 1)]
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if ul[0] >= self.heatmap_size[0] or ul[1] >= self.heatmap_size[1] \
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or br[0] < 0 or br[1] < 0:
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# If not, just return the image as is
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target_weight[joint_id] = 0
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continue
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# # Generate gaussian
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size = 2 * tmp_size + 1
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x = np.arange(0, size, 1, np.float32)
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y = x[:, np.newaxis]
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x0 = y0 = size // 2
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# The gaussian is not normalized, we want the center value to equal 1
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g = np.exp(- ((x - x0) ** 2 + (y - y0) ** 2) / (2 * self.sigma ** 2))
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# Usable gaussian range
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g_x = max(0, -ul[0]), min(br[0], self.heatmap_size[0]) - ul[0]
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g_y = max(0, -ul[1]), min(br[1], self.heatmap_size[1]) - ul[1]
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# Image range
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img_x = max(0, ul[0]), min(br[0], self.heatmap_size[0])
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img_y = max(0, ul[1]), min(br[1], self.heatmap_size[1])
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v = target_weight[joint_id]
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if v > 0.5:
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target[joint_id][img_y[0]:img_y[1], img_x[0]:img_x[1]] = \
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g[g_y[0]:g_y[1], g_x[0]:g_x[1]]
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if self.use_different_joints_weight:
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target_weight = np.multiply(target_weight, self.joints_weight)
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return target, target_weight
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