import functools import json import os from pathlib import Path from pdb import set_trace as st import torchvision import blobfile as bf import imageio import numpy as np import torch as th import torch.distributed as dist import torchvision from PIL import Image from torch.nn.parallel.distributed import DistributedDataParallel as DDP from tqdm import tqdm from guided_diffusion.fp16_util import MixedPrecisionTrainer from guided_diffusion import dist_util, logger from guided_diffusion.train_util import (calc_average_loss, log_rec3d_loss_dict, find_resume_checkpoint) from torch.optim import AdamW from .train_util import TrainLoopBasic, TrainLoop3DRec import vision_aided_loss from dnnlib.util import calculate_adaptive_weight def get_blob_logdir(): # You can change this to be a separate path to save checkpoints to # a blobstore or some external drive. return logger.get_dir() class TrainLoop3DcvD(TrainLoop3DRec): def __init__( self, *, rec_model, loss_class, # diffusion, data, eval_data, batch_size, microbatch, lr, ema_rate, log_interval, eval_interval, save_interval, resume_checkpoint, use_fp16=False, fp16_scale_growth=1e-3, # schedule_sampler=None, weight_decay=0.0, lr_anneal_steps=0, iterations=10001, load_submodule_name='', ignore_resume_opt=False, use_amp=False, cvD_name='cvD', model_name='rec', # SR_TRAINING=True, SR_TRAINING=False, **kwargs): super().__init__(rec_model=rec_model, loss_class=loss_class, data=data, eval_data=eval_data, batch_size=batch_size, microbatch=microbatch, lr=lr, ema_rate=ema_rate, log_interval=log_interval, eval_interval=eval_interval, save_interval=save_interval, resume_checkpoint=resume_checkpoint, use_fp16=use_fp16, fp16_scale_growth=fp16_scale_growth, weight_decay=weight_decay, lr_anneal_steps=lr_anneal_steps, iterations=iterations, load_submodule_name=load_submodule_name, ignore_resume_opt=ignore_resume_opt, model_name=model_name, use_amp=use_amp, cvD_name=cvD_name, **kwargs) # self.rec_model = self.ddp_model # device = loss_class.device device = dist_util.dev() # * create vision aided model # TODO, load model self.nvs_cvD = vision_aided_loss.Discriminator( cv_type='clip', loss_type='multilevel_sigmoid_s', device=device).to(device) self.nvs_cvD.cv_ensemble.requires_grad_(False) # Freeze feature extractor # self.nvs_cvD.train() # # SR_TRAINING = False cvD_model_params=list(self.nvs_cvD.decoder.parameters()) self.SR_TRAINING = SR_TRAINING # SR_TRAINING = True if SR_TRAINING: # width, patch_size = self.nvs_cvD.cv_ensemble vision_width, vision_patch_size = [self.nvs_cvD.cv_ensemble.models[0].model.conv1.weight.shape[k] for k in [0, -1]] self.nvs_cvD.cv_ensemble.models[0].model.conv1 = th.nn.Conv2d(in_channels=6, out_channels=vision_width, kernel_size=vision_patch_size, stride=vision_patch_size, bias=False).to(dist_util.dev()) self.nvs_cvD.cv_ensemble.models[0].model.conv1.requires_grad_(True) cvD_model_params += list(self.nvs_cvD.cv_ensemble.models[0].model.conv1.parameters()) # change normalization metrics self.nvs_cvD.cv_ensemble.models[0].image_mean = self.nvs_cvD.cv_ensemble.models[0].image_mean.repeat(2) self.nvs_cvD.cv_ensemble.models[0].image_std = self.nvs_cvD.cv_ensemble.models[0].image_std.repeat(2) # logger.log(f'nvs_cvD_model_params: {cvD_model_params}') self._load_and_sync_parameters(model=self.nvs_cvD, model_name='cvD') self.mp_trainer_cvD = MixedPrecisionTrainer( model=self.nvs_cvD, use_fp16=self.use_fp16, fp16_scale_growth=fp16_scale_growth, model_name=cvD_name, use_amp=use_amp, model_params=cvD_model_params ) # cvD_lr = 4e-5*(lr/1e-5) # cvD_lr = 4e-4*(lr/1e-5) cvD_lr = 1e-4*(lr/1e-5) * self.loss_class.opt.nvs_D_lr_mul # cvD_lr = 1e-5*(lr/1e-5) self.opt_cvD = AdamW( self.mp_trainer_cvD.master_params, lr=cvD_lr, betas=(0, 0.999), eps=1e-8) # dlr in biggan cfg logger.log(f'cpt_cvD lr: {cvD_lr}') if self.use_ddp: self.ddp_nvs_cvD = DDP( self.nvs_cvD, device_ids=[dist_util.dev()], output_device=dist_util.dev(), broadcast_buffers=False, bucket_cap_mb=128, find_unused_parameters=False, ) else: self.ddp_nvs_cvD = self.nvs_cvD th.cuda.empty_cache() def run_step(self, batch, step='g_step'): # self.forward_backward(batch) if step == 'g_step_rec': self.forward_G_rec(batch) took_step_g_rec = self.mp_trainer_rec.optimize(self.opt) if took_step_g_rec: self._update_ema() # g_ema elif step == 'g_step_nvs': self.forward_G_nvs(batch) took_step_g_nvs = self.mp_trainer_rec.optimize(self.opt) if took_step_g_nvs: self._update_ema() # g_ema elif step == 'd_step': self.forward_D(batch) _ = self.mp_trainer_cvD.optimize(self.opt_cvD) self._anneal_lr() self.log_step() def run_loop(self): while (not self.lr_anneal_steps or self.step + self.resume_step < self.lr_anneal_steps): # let all processes sync up before starting with a new epoch of training dist_util.synchronize() # batch, cond = next(self.data) # if batch is None: batch = next(self.data) self.run_step(batch, 'g_step_rec') batch = next(self.data) self.run_step(batch, 'g_step_nvs') batch = next(self.data) self.run_step(batch, 'd_step') if self.step % self.log_interval == 0 and dist_util.get_rank( ) == 0: out = logger.dumpkvs() # * log to tensorboard for k, v in out.items(): self.writer.add_scalar(f'Loss/{k}', v, self.step + self.resume_step) if self.step % self.eval_interval == 0 and self.step != 0: if dist_util.get_rank() == 0: self.eval_loop() # self.eval_novelview_loop() # let all processes sync up before starting with a new epoch of training dist_util.synchronize() if self.step % self.save_interval == 0: self.save() self.save(self.mp_trainer_cvD, 'cvD') dist_util.synchronize() # Run for a finite amount of time in integration tests. if os.environ.get("DIFFUSION_TRAINING_TEST", "") and self.step > 0: return self.step += 1 if self.step > self.iterations: logger.log('reached maximum iterations, exiting') # Save the last checkpoint if it wasn't already saved. if (self.step - 1) % self.save_interval != 0: self.save() self.save(self.mp_trainer_cvD, 'cvD') exit() # Save the last checkpoint if it wasn't already saved. if (self.step - 1) % self.save_interval != 0: self.save() self.save(self.mp_trainer_cvD, 'cvD') # def forward_backward(self, batch, *args, **kwargs): # blur_sigma = max(1 - cur_nimg / (self.blur_fade_kimg * 1e3), 0) * self.blur_init_sigma if self.blur_fade_kimg > 0 else 0 def run_D_Diter(self, real, fake, D=None): # Dmain: Minimize logits for generated images and maximize logits for real images. if D is None: D = self.ddp_nvs_cvD lossD = D(real, for_real=True).mean() + D( fake, for_real=False).mean() return lossD def forward_D(self, batch): # update D self.mp_trainer_cvD.zero_grad() self.ddp_nvs_cvD.requires_grad_(True) self.rec_model.requires_grad_(False) batch_size = batch['img'].shape[0] # * sample a new batch for D training for i in range(0, batch_size, self.microbatch): micro = { k: v[i:i + self.microbatch].to(dist_util.dev()).contiguous() for k, v in batch.items() } with th.autocast(device_type='cuda', dtype=th.float16, enabled=self.mp_trainer_cvD.use_amp): # pred = self.rec_model(img=micro['img_to_encoder'], # c=micro['c']) # pred: (B, 3, 64, 64) pred = self.rec_model( img=micro['img_to_encoder'], c=th.cat([ micro['c'][1:], micro['c'][:1], # half novel view, half orig view ])) real_logits_cv = self.run_D_Diter( real=micro['img_to_encoder'], fake=pred['image_raw']) # TODO, add SR for FFHQ log_rec3d_loss_dict({'vision_aided_loss/D': real_logits_cv}) self.mp_trainer_cvD.backward(real_logits_cv) def forward_G_rec(self, batch): # update G self.mp_trainer_rec.zero_grad() self.rec_model.requires_grad_(True) self.ddp_nvs_cvD.requires_grad_(False) batch_size = batch['img'].shape[0] for i in range(0, batch_size, self.microbatch): micro = { k: v[i:i + self.microbatch].to(dist_util.dev()).contiguous() for k, v in batch.items() } last_batch = (i + self.microbatch) >= batch_size # VQ3D novel view d loss # duplicated_for_nvs = th.cat([ # micro['img_to_encoder'][:batch_size - 2], # micro['img_to_encoder'][:2] # ], 0) with th.autocast(device_type='cuda', dtype=th.float16, enabled=self.mp_trainer_rec.use_amp): pred = self.rec_model( img=micro['img_to_encoder'], c=micro['c'] ) # render novel view for first half of the batch for D loss target_for_rec = micro pred_for_rec = pred # pred_for_rec = { # k: v[:batch_size - 2] if v is not None else None # for k, v in pred.items() # } # target_for_rec = { # k: v[:batch_size - 2] if v is not None else None # for k, v in target.items() # } if last_batch or not self.use_ddp: loss, loss_dict = self.loss_class(pred_for_rec, target_for_rec, test_mode=False) else: with self.rec_model.no_sync(): # type: ignore loss, loss_dict = self.loss_class(pred_for_rec, target_for_rec, test_mode=False) # add cvD supervision vision_aided_loss = self.ddp_nvs_cvD( pred_for_rec['image_raw'], for_G=True).mean() # [B, 1] shape last_layer = self.rec_model.module.decoder.triplane_decoder.decoder.net[ # type: ignore -1].weight # type: ignore d_weight = calculate_adaptive_weight( loss, vision_aided_loss, last_layer, # disc_weight_max=0.1) * 0.1 # disc_weight_max=0.1) * 0.05 disc_weight_max=1) loss += vision_aided_loss * d_weight loss_dict.update({ 'vision_aided_loss/G_rec': vision_aided_loss, 'd_weight': d_weight }) log_rec3d_loss_dict(loss_dict) self.mp_trainer_rec.backward(loss) # ! move to other places, add tensorboard if dist_util.get_rank() == 0 and self.step % 500 == 0: with th.no_grad(): # gt_vis = th.cat([batch['img'], batch['depth']], dim=-1) gt_depth = micro['depth'] if gt_depth.ndim == 3: gt_depth = gt_depth.unsqueeze(1) gt_depth = (gt_depth - gt_depth.min()) / (gt_depth.max() - gt_depth.min()) # if True: pred_depth = pred['image_depth'] pred_depth = (pred_depth - pred_depth.min()) / ( pred_depth.max() - pred_depth.min()) pred_img = pred['image_raw'] gt_img = micro['img'] if 'image_sr' in pred: if pred['image_sr'].shape[-1] == 512: pred_img = th.cat( [self.pool_512(pred_img), pred['image_sr']], dim=-1) gt_img = th.cat( [self.pool_512(micro['img']), micro['img_sr']], dim=-1) pred_depth = self.pool_512(pred_depth) gt_depth = self.pool_512(gt_depth) elif pred['image_sr'].shape[-1] == 256: pred_img = th.cat( [self.pool_256(pred_img), pred['image_sr']], dim=-1) gt_img = th.cat( [self.pool_256(micro['img']), micro['img_sr']], dim=-1) pred_depth = self.pool_256(pred_depth) gt_depth = self.pool_256(gt_depth) else: pred_img = th.cat( [self.pool_128(pred_img), pred['image_sr']], dim=-1) gt_img = th.cat( [self.pool_128(micro['img']), micro['img_sr']], dim=-1) gt_depth = self.pool_128(gt_depth) pred_depth = self.pool_128(pred_depth) gt_vis = th.cat( [gt_img, gt_depth.repeat_interleave(3, dim=1)], dim=-1) # TODO, fail to load depth. range [0, 1] pred_vis = th.cat( [pred_img, pred_depth.repeat_interleave(3, dim=1)], dim=-1) # B, 3, H, W vis = th.cat([gt_vis, pred_vis], dim=-2)[0].permute( 1, 2, 0).cpu() # ! pred in range[-1, 1] # vis_grid = torchvision.utils.make_grid(vis) # HWC vis = vis.numpy() * 127.5 + 127.5 vis = vis.clip(0, 255).astype(np.uint8) Image.fromarray(vis).save( f'{logger.get_dir()}/{self.step+self.resume_step}_rec.jpg' ) logger.log( 'log vis to: ', f'{logger.get_dir()}/{self.step+self.resume_step}_rec.jpg' ) def forward_G_nvs(self, batch): # update G self.mp_trainer_rec.zero_grad() self.rec_model.requires_grad_(True) self.ddp_nvs_cvD.requires_grad_(False) batch_size = batch['img'].shape[0] for i in range(0, batch_size, self.microbatch): micro = { k: v[i:i + self.microbatch].to(dist_util.dev()).contiguous() for k, v in batch.items() } # last_batch = (i + self.microbatch) >= batch_size # VQ3D novel view d loss # duplicated_for_nvs = th.cat([ # micro['img_to_encoder'][batch_size // 2:], # micro['img_to_encoder'][:batch_size // 2] # ], 0) with th.autocast(device_type='cuda', dtype=th.float16, enabled=self.mp_trainer_rec.use_amp): pred = self.rec_model( # img=duplicated_for_nvs, c=micro['c'] img=micro['img_to_encoder'], c=th.cat([ micro['c'][1:], micro['c'][:1], ]) ) # render novel view for first half of the batch for D loss # add cvD supervision vision_aided_loss = self.ddp_nvs_cvD( pred['image_raw'], for_G=True).mean() # [B, 1] shape # loss = vision_aided_loss * 0.01 # loss = vision_aided_loss * 0.005 # loss = vision_aided_loss * 0.1 loss = vision_aided_loss * 0.01 log_rec3d_loss_dict({ 'vision_aided_loss/G_nvs': vision_aided_loss, }) self.mp_trainer_rec.backward(loss) # ! move to other places, add tensorboard if dist_util.get_rank() == 0 and self.step % 500 == 0: with th.no_grad(): # gt_vis = th.cat([batch['img'], batch['depth']], dim=-1) gt_depth = micro['depth'] if gt_depth.ndim == 3: gt_depth = gt_depth.unsqueeze(1) gt_depth = (gt_depth - gt_depth.min()) / (gt_depth.max() - gt_depth.min()) # if True: pred_depth = pred['image_depth'] pred_depth = (pred_depth - pred_depth.min()) / ( pred_depth.max() - pred_depth.min()) pred_img = pred['image_raw'] gt_img = micro['img'] if 'image_sr' in pred: pred_img = th.cat( [self.pool_512(pred_img), pred['image_sr']], dim=-1) gt_img = th.cat( [self.pool_512(micro['img']), micro['img_sr']], dim=-1) pred_depth = self.pool_512(pred_depth) gt_depth = self.pool_512(gt_depth) gt_vis = th.cat( [gt_img, gt_depth.repeat_interleave(3, dim=1)], dim=-1) # TODO, fail to load depth. range [0, 1] pred_vis = th.cat( [pred_img, pred_depth.repeat_interleave(3, dim=1)], dim=-1) # B, 3, H, W # vis = th.cat([gt_vis, pred_vis], dim=-2)[0].permute( # 1, 2, 0).cpu() # ! pred in range[-1, 1] vis = th.cat([gt_vis, pred_vis], dim=-2) vis = torchvision.utils.make_grid( vis, normalize=True, scale_each=True, value_range=(-1, 1)).cpu().permute(1, 2, 0) # H W 3 vis = vis.numpy() * 255 vis = vis.clip(0, 255).astype(np.uint8) # logger.log(vis.shape) Image.fromarray(vis).save( f'{logger.get_dir()}/{self.step+self.resume_step}_nvs.jpg' ) logger.log( 'log vis to: ', f'{logger.get_dir()}/{self.step+self.resume_step}_nvs.jpg' ) def save(self, mp_trainer=None, model_name='rec'): if mp_trainer is None: mp_trainer = self.mp_trainer_rec def save_checkpoint(rate, params): state_dict = mp_trainer.master_params_to_state_dict(params) if dist_util.get_rank() == 0: logger.log(f"saving model {model_name} {rate}...") if not rate: filename = f"model_{model_name}{(self.step+self.resume_step):07d}.pt" else: filename = f"ema_{model_name}_{rate}_{(self.step+self.resume_step):07d}.pt" with bf.BlobFile(bf.join(get_blob_logdir(), filename), "wb") as f: th.save(state_dict, f) save_checkpoint(0, mp_trainer.master_params) if model_name == 'ddpm': for rate, params in zip(self.ema_rate, self.ema_params): save_checkpoint(rate, params) dist.barrier() def _load_and_sync_parameters(self, model=None, model_name='rec'): resume_checkpoint, self.resume_step = find_resume_checkpoint( self.resume_checkpoint, model_name) or self.resume_checkpoint if model is None: model = self.rec_model # default model in the parent class logger.log(resume_checkpoint) if resume_checkpoint and Path(resume_checkpoint).exists(): if dist_util.get_rank() == 0: logger.log( f"loading model from checkpoint: {resume_checkpoint}...") map_location = { 'cuda:%d' % 0: 'cuda:%d' % dist_util.get_rank() } # configure map_location properly logger.log(f'mark {model_name} loading ', ) resume_state_dict = dist_util.load_state_dict( resume_checkpoint, map_location=map_location) logger.log(f'mark {model_name} loading finished', ) model_state_dict = model.state_dict() for k, v in resume_state_dict.items(): if k in model_state_dict.keys() and v.size( ) == model_state_dict[k].size(): model_state_dict[k] = v # elif 'IN' in k and model_name == 'rec' and getattr(model.decoder, 'decomposed_IN', False): # model_state_dict[k.replace('IN', 'superresolution.norm.norm_layer')] = v # decomposed IN elif 'attn.wk' in k or 'attn.wv' in k: # old qkv logger.log('ignore ', k) elif 'decoder.vit_decoder.blocks' in k: # st() # load from 2D ViT pre-trained into 3D ViT blocks. assert len(model.decoder.vit_decoder.blocks[0].vit_blks) == 2 # assert depth=2 here. fusion_ca_depth = len(model.decoder.vit_decoder.blocks[0].vit_blks) vit_subblk_index = int(k.split('.')[3]) vit_blk_keyname = ('.').join(k.split('.')[4:]) fusion_blk_index = vit_subblk_index // fusion_ca_depth fusion_blk_subindex = vit_subblk_index % fusion_ca_depth model_state_dict[f'decoder.vit_decoder.blocks.{fusion_blk_index}.vit_blks.{fusion_blk_subindex}.{vit_blk_keyname}'] = v # logger.log('load 2D ViT weight: {}'.format(f'decoder.vit_decoder.blocks.{fusion_blk_index}.vit_blks.{fusion_blk_subindex}.{vit_blk_keyname}')) elif 'IN' in k: logger.log('ignore ', k) elif 'quant_conv' in k: logger.log('ignore ', k) else: logger.log('!!!! ignore key: ', k, ": ", v.size(),) if k in model_state_dict: logger.log('shape in model: ', model_state_dict[k].size()) else: logger.log(k, 'not in model_state_dict') model.load_state_dict(model_state_dict, strict=True) del model_state_dict if dist_util.get_world_size() > 1: dist_util.sync_params(model.parameters()) logger.log(f'synced {model_name} params')