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 ldm.data.dataset_coco import dataset_coco_mask_color from dist_util import get_bare_model, get_dist_info, 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 from ldm.modules.structure_condition.model_edge import pidinet 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 @master_only 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 # else: # if opt['path'].get('resume_state'): # resume_state_path = opt['path']['resume_state'] 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)) # check_resume(opt, resume_state['iter']) return resume_state parser = argparse.ArgumentParser() 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( "--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( "--config", type=str, default="configs/stable-diffusion/train_sketch.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=1, 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=0, type=int, help='node rank for distributed training' ) parser.add_argument( '--launcher', default='pytorch', type=str, help='node rank for distributed training' ) parser.add_argument( '--l_cond', default=4, type=int, help='number of scales' ) opt = parser.parse_args() if __name__ == '__main__': config = OmegaConf.load(f"{opt.config}") opt.name = config['name'] # distributed setting init_dist(opt.launcher) torch.backends.cudnn.benchmark = True device='cuda' torch.cuda.set_device(opt.local_rank) # dataset path_json_train = 'coco_stuff/mask/annotations/captions_train2017.json' path_json_val = 'coco_stuff/mask/annotations/captions_val2017.json' train_dataset = dataset_coco_mask_color(path_json_train, root_path_im='coco/train2017', root_path_mask='coco_stuff/mask/train2017_color', image_size=512 ) train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset) val_dataset = dataset_coco_mask_color(path_json_val, root_path_im='coco/val2017', root_path_mask='coco_stuff/mask/val2017_color', image_size=512 ) train_dataloader = torch.utils.data.DataLoader( train_dataset, batch_size=opt.bsize, shuffle=(train_sampler is None), num_workers=opt.num_workers, pin_memory=True, sampler=train_sampler) val_dataloader = torch.utils.data.DataLoader( val_dataset, batch_size=1, shuffle=False, num_workers=1, pin_memory=False) # edge_generator net_G = pidinet() ckp = torch.load('models/table5_pidinet.pth', map_location='cpu')['state_dict'] net_G.load_state_dict({k.replace('module.',''):v for k, v in ckp.items()}) net_G.cuda() # stable diffusion model = load_model_from_config(config, f"{opt.ckpt}").to(device) # sketch encoder model_ad = Adapter(channels=[320, 640, 1280, 1280][:4], nums_rb=2, ksize=1, sk=True, use_conv=False).to(device) # to gpus model_ad = torch.nn.parallel.DistributedDataParallel( model_ad, device_ids=[opt.local_rank], output_device=opt.local_rank) model = torch.nn.parallel.DistributedDataParallel( model, device_ids=[opt.local_rank], output_device=opt.local_rank) # device_ids=[torch.cuda.current_device()]) net_G = torch.nn.parallel.DistributedDataParallel( net_G, device_ids=[opt.local_rank], output_device=opt.local_rank) # device_ids=[torch.cuda.current_device()]) # optimizer params = list(model_ad.parameters()) optimizer = torch.optim.AdamW(params, lr=config['training']['lr']) 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) start_epoch = 0 current_iter = 0 # 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)) else: # 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)) resume_optimizers = resume_state['optimizers'] optimizer.load_state_dict(resume_optimizers) logger.info(f"Resuming training from epoch: {resume_state['epoch']}, " f"iter: {resume_state['iter']}.") start_epoch = resume_state['epoch'] current_iter = resume_state['iter'] # copy the yml file to the experiment root copy_opt_file(opt.config, experiments_root) # training logger.info(f'Start training from epoch: {start_epoch}, iter: {current_iter}') for epoch in range(start_epoch, opt.epochs): train_dataloader.sampler.set_epoch(epoch) # train for _, data in enumerate(train_dataloader): current_iter += 1 with torch.no_grad(): edge = net_G(data['im'].cuda(non_blocking=True))[-1] edge = edge>0.5 edge = edge.float() c = model.module.get_learned_conditioning(data['sentence']) z = model.module.encode_first_stage((data['im']*2-1.).cuda(non_blocking=True)) z = model.module.get_first_stage_encoding(z) optimizer.zero_grad() model.zero_grad() features_adapter = model_ad(edge) l_pixel, loss_dict = model(z, c=c, features_adapter = features_adapter) l_pixel.backward() optimizer.step() if (current_iter+1)%opt.print_fq == 0: logger.info(loss_dict) # save checkpoint rank, _ = get_dist_info() if (rank==0) and ((current_iter+1)%config['training']['save_freq'] == 0): save_filename = f'model_ad_{current_iter+1}.pth' save_path = os.path.join(experiments_root, 'models', save_filename) save_dict = {} model_ad_bare = get_bare_model(model_ad) state_dict = model_ad_bare.state_dict() for key, param in state_dict.items(): if key.startswith('module.'): # remove unnecessary 'module.' key = key[7:] save_dict[key] = param.cpu() torch.save(save_dict, save_path) # save state state = {'epoch': epoch, 'iter': current_iter+1, 'optimizers': optimizer.state_dict()} save_filename = f'{current_iter+1}.state' save_path = os.path.join(experiments_root, 'training_states', save_filename) torch.save(state, save_path) # val rank, _ = get_dist_info() if rank==0: for data in val_dataloader: with torch.no_grad(): if opt.dpm_solver: sampler = DPMSolverSampler(model.module) elif opt.plms: sampler = PLMSSampler(model.module) else: sampler = DDIMSampler(model.module) print(data['im'].shape) c = model.module.get_learned_conditioning(data['sentence']) edge = net_G(data['im'].cuda(non_blocking=True))[-1] edge = edge>0.5 edge = edge.float() im_edge = tensor2img(edge) cv2.imwrite(os.path.join(experiments_root, 'visualization', 'edge_%04d.png'%epoch), im_edge) features_adapter = model_ad(edge) shape = [opt.C, opt.H // opt.f, opt.W // opt.f] samples_ddim, _ = sampler.sample(S=opt.ddim_steps, conditioning=c, batch_size=opt.n_samples, shape=shape, verbose=False, unconditional_guidance_scale=opt.scale, unconditional_conditioning=model.module.get_learned_conditioning(opt.n_samples * [""]), eta=opt.ddim_eta, x_T=None, features_adapter=features_adapter) x_samples_ddim = model.module.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) img = cv2.putText(img.copy(), data['sentence'][0], (10,30), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0,255,0), 2) cv2.imwrite(os.path.join(experiments_root, 'visualization', 'sample_e%04d_s%04d.png'%(epoch, id_sample)), img[:,:,::-1]) break