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| import argparse | |
| import logging | |
| import os | |
| import os.path as osp | |
| import time | |
| import cv2 | |
| import matplotlib.pyplot as plt | |
| import numpy as np | |
| import torch | |
| import torch.nn as nn | |
| from basicsr.utils import (get_env_info, get_root_logger, get_time_str, | |
| img2tensor, scandir, tensor2img) | |
| from basicsr.utils.options import copy_opt_file, dict2str | |
| from omegaconf import OmegaConf | |
| from PIL import Image | |
| from pytorch_lightning import seed_everything | |
| from dataset_coco import dataset_coco, dataset_coco_mask_color_sig | |
| from dist_util import get_bare_model, init_dist, master_only | |
| from ldm.models.diffusion.ddim import DDIMSampler | |
| from ldm.models.diffusion.dpm_solver import DPMSolverSampler | |
| from ldm.models.diffusion.plms import PLMSSampler | |
| from ldm.modules.encoders.adapter import Adapter | |
| from ldm.util import instantiate_from_config | |
| import mmcv | |
| from mmdet.apis import inference_detector, init_detector | |
| from mmpose.apis import (inference_top_down_pose_model, init_pose_model, process_mmdet_results, vis_pose_result) | |
| skeleton = [[15, 13], [13, 11], [16, 14], [14, 12], [11, 12], [5, 11], [6, 12], [5, 6], [5, 7], [6, 8], [7, 9], [8, 10], | |
| [1, 2], [0, 1], [0, 2], [1, 3], [2, 4], [3, 5], [4, 6]] | |
| pose_kpt_color = [[51, 153, 255], [51, 153, 255], [51, 153, 255], [51, 153, 255], [51, 153, 255], [0, 255, 0], | |
| [255, 128, 0], [0, 255, 0], [255, 128, 0], [0, 255, 0], [255, 128, 0], [0, 255, 0], [255, 128, 0], | |
| [0, 255, 0], [255, 128, 0], [0, 255, 0], [255, 128, 0]] | |
| pose_link_color = [[0, 255, 0], [0, 255, 0], [255, 128, 0], [255, 128, 0], | |
| [51, 153, 255], [51, 153, 255], [51, 153, 255], [51, 153, 255], [0, 255, 0], [255, 128, 0], | |
| [0, 255, 0], [255, 128, 0], [51, 153, 255], [51, 153, 255], [51, 153, 255], [51, 153, 255], | |
| [51, 153, 255], [51, 153, 255], [51, 153, 255]] | |
| def load_model_from_config(config, ckpt, verbose=False): | |
| print(f"Loading model from {ckpt}") | |
| pl_sd = torch.load(ckpt, map_location="cpu") | |
| if "global_step" in pl_sd: | |
| print(f"Global Step: {pl_sd['global_step']}") | |
| sd = pl_sd["state_dict"] | |
| model = instantiate_from_config(config.model) | |
| m, u = model.load_state_dict(sd, strict=False) | |
| if len(m) > 0 and verbose: | |
| print("missing keys:") | |
| print(m) | |
| if len(u) > 0 and verbose: | |
| print("unexpected keys:") | |
| print(u) | |
| model.cuda() | |
| model.eval() | |
| return model | |
| def mkdir_and_rename(path): | |
| """mkdirs. If path exists, rename it with timestamp and create a new one. | |
| Args: | |
| path (str): Folder path. | |
| """ | |
| if osp.exists(path): | |
| new_name = path + '_archived_' + get_time_str() | |
| print(f'Path already exists. Rename it to {new_name}', flush=True) | |
| os.rename(path, new_name) | |
| os.makedirs(path, exist_ok=True) | |
| os.makedirs(osp.join(experiments_root, 'models')) | |
| os.makedirs(osp.join(experiments_root, 'training_states')) | |
| os.makedirs(osp.join(experiments_root, 'visualization')) | |
| def load_resume_state(opt): | |
| resume_state_path = None | |
| if opt.auto_resume: | |
| state_path = osp.join('experiments', opt.name, 'training_states') | |
| if osp.isdir(state_path): | |
| states = list(scandir(state_path, suffix='state', recursive=False, full_path=False)) | |
| if len(states) != 0: | |
| states = [float(v.split('.state')[0]) for v in states] | |
| resume_state_path = osp.join(state_path, f'{max(states):.0f}.state') | |
| opt.resume_state_path = resume_state_path | |
| if resume_state_path is None: | |
| resume_state = None | |
| else: | |
| device_id = torch.cuda.current_device() | |
| resume_state = torch.load(resume_state_path, map_location=lambda storage, loc: storage.cuda(device_id)) | |
| return resume_state | |
| def imshow_keypoints(img, | |
| pose_result, | |
| skeleton=None, | |
| kpt_score_thr=0.1, | |
| pose_kpt_color=None, | |
| pose_link_color=None, | |
| radius=4, | |
| thickness=1): | |
| """Draw keypoints and links on an image. | |
| Args: | |
| img (ndarry): The image to draw poses on. | |
| pose_result (list[kpts]): The poses to draw. Each element kpts is | |
| a set of K keypoints as an Kx3 numpy.ndarray, where each | |
| keypoint is represented as x, y, score. | |
| kpt_score_thr (float, optional): Minimum score of keypoints | |
| to be shown. Default: 0.3. | |
| pose_kpt_color (np.array[Nx3]`): Color of N keypoints. If None, | |
| the keypoint will not be drawn. | |
| pose_link_color (np.array[Mx3]): Color of M links. If None, the | |
| links will not be drawn. | |
| thickness (int): Thickness of lines. | |
| """ | |
| img_h, img_w, _ = img.shape | |
| img = np.zeros(img.shape) | |
| for idx, kpts in enumerate(pose_result): | |
| if idx > 1: | |
| continue | |
| kpts = kpts['keypoints'] | |
| # print(kpts) | |
| kpts = np.array(kpts, copy=False) | |
| # draw each point on image | |
| if pose_kpt_color is not None: | |
| assert len(pose_kpt_color) == len(kpts) | |
| for kid, kpt in enumerate(kpts): | |
| x_coord, y_coord, kpt_score = int(kpt[0]), int(kpt[1]), kpt[2] | |
| if kpt_score < kpt_score_thr or pose_kpt_color[kid] is None: | |
| # skip the point that should not be drawn | |
| continue | |
| color = tuple(int(c) for c in pose_kpt_color[kid]) | |
| cv2.circle(img, (int(x_coord), int(y_coord)), radius, color, -1) | |
| # draw links | |
| if skeleton is not None and pose_link_color is not None: | |
| assert len(pose_link_color) == len(skeleton) | |
| for sk_id, sk in enumerate(skeleton): | |
| pos1 = (int(kpts[sk[0], 0]), int(kpts[sk[0], 1])) | |
| pos2 = (int(kpts[sk[1], 0]), int(kpts[sk[1], 1])) | |
| if (pos1[0] <= 0 or pos1[0] >= img_w or pos1[1] <= 0 or pos1[1] >= img_h or pos2[0] <= 0 | |
| or pos2[0] >= img_w or pos2[1] <= 0 or pos2[1] >= img_h or kpts[sk[0], 2] < kpt_score_thr | |
| or kpts[sk[1], 2] < kpt_score_thr or pose_link_color[sk_id] is None): | |
| # skip the link that should not be drawn | |
| continue | |
| color = tuple(int(c) for c in pose_link_color[sk_id]) | |
| cv2.line(img, pos1, pos2, color, thickness=thickness) | |
| return img | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument( | |
| "--prompt", | |
| type=str, | |
| nargs="?", | |
| default="An Iron man" | |
| ) | |
| parser.add_argument( | |
| "--neg_prompt", | |
| type=str, | |
| default="ugly, tiling, poorly drawn hands, poorly drawn feet, poorly drawn face, out of frame, extra limbs, disfigured, deformed, body out of frame, bad anatomy, watermark, signature, cut off, low contrast, underexposed, overexposed, bad art, beginner, amateur, distorted face" | |
| ) | |
| parser.add_argument( | |
| "--path_cond", | |
| type=str, | |
| default="examples/keypose/iron.png" | |
| ) | |
| parser.add_argument( | |
| "--type_in", | |
| type=str, | |
| default="sketch" | |
| ) | |
| parser.add_argument( | |
| "--bsize", | |
| type=int, | |
| default=8, | |
| help="the prompt to render" | |
| ) | |
| parser.add_argument( | |
| "--epochs", | |
| type=int, | |
| default=10000, | |
| help="the prompt to render" | |
| ) | |
| parser.add_argument( | |
| "--device", | |
| type=str, | |
| default="cuda" | |
| ) | |
| parser.add_argument( | |
| "--num_workers", | |
| type=int, | |
| default=8, | |
| help="the prompt to render" | |
| ) | |
| parser.add_argument( | |
| "--use_shuffle", | |
| type=bool, | |
| default=True, | |
| help="the prompt to render" | |
| ) | |
| parser.add_argument( | |
| "--dpm_solver", | |
| action='store_true', | |
| help="use dpm_solver sampling", | |
| ) | |
| parser.add_argument( | |
| "--plms", | |
| action='store_true', | |
| help="use plms sampling", | |
| ) | |
| parser.add_argument( | |
| "--auto_resume", | |
| action='store_true', | |
| help="use plms sampling", | |
| ) | |
| parser.add_argument( | |
| "--ckpt", | |
| type=str, | |
| default="models/sd-v1-4.ckpt", | |
| help="path to checkpoint of model", | |
| ) | |
| parser.add_argument( | |
| "--ckpt_ad", | |
| type=str, | |
| default='models/t2iadapter_keypose_sd14v1.pth' | |
| ) | |
| parser.add_argument( | |
| "--config", | |
| type=str, | |
| default="configs/stable-diffusion/test_keypose.yaml", | |
| help="path to config which constructs model", | |
| ) | |
| parser.add_argument( | |
| "--print_fq", | |
| type=int, | |
| default=100, | |
| help="path to config which constructs model", | |
| ) | |
| parser.add_argument( | |
| "--H", | |
| type=int, | |
| default=512, | |
| help="image height, in pixel space", | |
| ) | |
| parser.add_argument( | |
| "--W", | |
| type=int, | |
| default=512, | |
| help="image width, in pixel space", | |
| ) | |
| parser.add_argument( | |
| "--C", | |
| type=int, | |
| default=4, | |
| help="latent channels", | |
| ) | |
| parser.add_argument( | |
| "--f", | |
| type=int, | |
| default=8, | |
| help="downsampling factor", | |
| ) | |
| parser.add_argument( | |
| "--ddim_steps", | |
| type=int, | |
| default=50, | |
| help="number of ddim sampling steps", | |
| ) | |
| parser.add_argument( | |
| "--n_samples", | |
| type=int, | |
| default=10, | |
| help="how many samples to produce for each given prompt. A.k.a. batch size", | |
| ) | |
| parser.add_argument( | |
| "--ddim_eta", | |
| type=float, | |
| default=0.0, | |
| help="ddim eta (eta=0.0 corresponds to deterministic sampling", | |
| ) | |
| parser.add_argument( | |
| "--scale", | |
| type=float, | |
| default=7.5, | |
| help="unconditional guidance scale: eps = eps(x, empty) + scale * (eps(x, cond) - eps(x, empty))", | |
| ) | |
| parser.add_argument( | |
| "--gpus", | |
| default=[0,1,2,3], | |
| help="gpu idx", | |
| ) | |
| parser.add_argument( | |
| '--local_rank', | |
| default=-1, | |
| type=int, | |
| help='node rank for distributed training' | |
| ) | |
| parser.add_argument( | |
| '--launcher', | |
| default='pytorch', | |
| type=str, | |
| help='node rank for distributed training' | |
| ) | |
| ## mmpose part ## | |
| parser.add_argument( | |
| '--det_config', | |
| help='Config file for detection', | |
| default='models/faster_rcnn_r50_fpn_coco.py' | |
| ) | |
| parser.add_argument( | |
| '--det_checkpoint', | |
| help='Checkpoint file for detection', | |
| default='models/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth' | |
| ) | |
| parser.add_argument( | |
| '--pose_config', | |
| help='Config file for pose', | |
| default='models/hrnet_w48_coco_256x192.py' | |
| ) | |
| parser.add_argument( | |
| '--pose_checkpoint', | |
| help='Checkpoint file for pose', | |
| default='models/hrnet_w48_coco_256x192-b9e0b3ab_20200708.pth' | |
| ) | |
| parser.add_argument( | |
| '--det-cat-id', | |
| type=int, | |
| default=1, | |
| help='Category id for bounding box detection model' | |
| ) | |
| parser.add_argument( | |
| '--bbox-thr', | |
| type=float, | |
| default=0.2, | |
| help='Bounding box score threshold' | |
| ) | |
| opt = parser.parse_args() | |
| if __name__ == '__main__': | |
| # seed_everything(42) | |
| config = OmegaConf.load(f"{opt.config}") | |
| opt.name = config['name'] | |
| device=opt.device | |
| # stable diffusion | |
| model = load_model_from_config(config, f"{opt.ckpt}").to(device) | |
| # Adaptor | |
| model_ad = Adapter(cin=int(3*64), channels=[320, 640, 1280, 1280][:4], nums_rb=2, ksize=1, sk=True, use_conv=False).to(device) | |
| model_ad.load_state_dict(torch.load(opt.ckpt_ad)) | |
| experiments_root = osp.join('experiments', opt.name) | |
| # resume state | |
| resume_state = load_resume_state(opt) | |
| if resume_state is None: | |
| mkdir_and_rename(experiments_root) | |
| # copy the yml file to the experiment root | |
| copy_opt_file(opt.config, experiments_root) | |
| # WARNING: should not use get_root_logger in the above codes, including the called functions | |
| # Otherwise the logger will not be properly initialized | |
| log_file = osp.join(experiments_root, f"train_{opt.name}_{get_time_str()}.log") | |
| logger = get_root_logger(logger_name='basicsr', log_level=logging.INFO, log_file=log_file) | |
| logger.info(get_env_info()) | |
| logger.info(dict2str(config)) | |
| for v_idx in range(opt.n_samples): | |
| with torch.no_grad(): | |
| if opt.dpm_solver: | |
| sampler = DPMSolverSampler(model) | |
| elif opt.plms: | |
| sampler = PLMSSampler(model) | |
| else: | |
| sampler = DDIMSampler(model) | |
| c = model.get_learned_conditioning([opt.prompt]) | |
| # costumer input | |
| if opt.type_in == 'pose': | |
| pose = cv2.imread(opt.path_cond) | |
| elif opt.type_in == 'image': | |
| # im = cv2.imread(opt.path_cond) | |
| image = cv2.imread(opt.path_cond) | |
| det_config_mmcv = mmcv.Config.fromfile(opt.det_config) | |
| det_model = init_detector(det_config_mmcv, opt.det_checkpoint, device=device) | |
| pose_config_mmcv = mmcv.Config.fromfile(opt.pose_config) | |
| pose_model = init_pose_model(pose_config_mmcv, opt.pose_checkpoint, device=device) | |
| mmdet_results = inference_detector(det_model, opt.path_cond) | |
| # keep the person class bounding boxes. | |
| person_results = process_mmdet_results(mmdet_results, opt.det_cat_id) | |
| # optional | |
| return_heatmap = False | |
| dataset = pose_model.cfg.data['test']['type'] | |
| # e.g. use ('backbone', ) to return backbone feature | |
| output_layer_names = None | |
| pose_results, returned_outputs = inference_top_down_pose_model( | |
| pose_model, | |
| opt.path_cond, | |
| person_results, | |
| bbox_thr=opt.bbox_thr, | |
| format='xyxy', | |
| dataset=dataset, | |
| dataset_info=None, | |
| return_heatmap=return_heatmap, | |
| outputs=output_layer_names) | |
| # show the results | |
| pose = imshow_keypoints( | |
| image, | |
| pose_results, | |
| skeleton=skeleton, | |
| pose_kpt_color=pose_kpt_color, | |
| pose_link_color=pose_link_color, | |
| radius=2, | |
| thickness=2) | |
| else: | |
| raise TypeError('Wrong input condition.') | |
| pose = cv2.resize(pose,(512,512)) | |
| cv2.imwrite(os.path.join(experiments_root, 'visualization', 'pose_idx%04d.png'%(v_idx)), pose) | |
| pose = img2tensor(pose, bgr2rgb=True, float32=True)/255. | |
| pose = pose.unsqueeze(0) | |
| features_adapter = model_ad(pose.to(device)) | |
| shape = [opt.C, opt.H // opt.f, opt.W // opt.f] | |
| samples_ddim, intermediates = sampler.sample(S=opt.ddim_steps, | |
| conditioning=c, | |
| batch_size=1, | |
| shape=shape, | |
| verbose=False, | |
| unconditional_guidance_scale=opt.scale, | |
| unconditional_conditioning=model.get_learned_conditioning([opt.neg_prompt]), | |
| eta=opt.ddim_eta, | |
| x_T=None, | |
| features_adapter1=features_adapter, | |
| mode = 'pose' | |
| ) | |
| x_samples_ddim = model.decode_first_stage(samples_ddim) | |
| x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0) | |
| x_samples_ddim = x_samples_ddim.cpu().permute(0, 2, 3, 1).numpy() | |
| for id_sample, x_sample in enumerate(x_samples_ddim): | |
| x_sample = 255.*x_sample | |
| img = x_sample.astype(np.uint8) | |
| cv2.imwrite(os.path.join(experiments_root, 'visualization', 'sample_idx%04d_s%04d.png'%(v_idx, id_sample)), img[:,:,::-1]) |