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() from ..train_util_cvD import TrainLoop3DcvD class TrainLoop3DcvD_nvsD_noSR(TrainLoop3DcvD): def __init__(self, *, # model, rec_model, loss_class, data, eval_data, batch_size, microbatch, lr, ema_rate, log_interval, eval_interval, save_interval, resume_checkpoint, use_fp16=False, fp16_scale_growth=0.001, weight_decay=0, lr_anneal_steps=0, iterations=10001, load_submodule_name='', ignore_resume_opt=False, use_amp=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, use_amp=use_amp, SR_TRAINING=False, **kwargs) 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 = next(self.data) self.run_step(batch, 'g_step_rec') # pure VAE reconstruction 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: print('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_D(self, batch): # update D self.rec_model.requires_grad_(False) self.mp_trainer_cvD.zero_grad() self.ddp_nvs_cvD.requires_grad_(True) 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): novel_view_c = th.cat([ micro['c'][1:], micro['c'][:1] ]) latent = self.rec_model(img=micro['img_to_encoder'], behaviour='enc_dec_wo_triplane') cano_pred = self.rec_model(latent=latent, c=micro['c'], behaviour='triplane_dec', return_raw_only=True) nvs_pred = self.rec_model(latent=latent, c=novel_view_c, behaviour='triplane_dec', return_raw_only=True) # if 'image_sr' in nvs_pred: # d_loss_nvs = self.run_D_Diter( # real=th.cat([ # th.nn.functional.interpolate( # cano_pred['image_raw'], # size=cano_pred['image_sr'].shape[2:], # mode='bilinear', # align_corners=False, # antialias=True), # cano_pred['image_sr'], # ], dim=1), # fake=th.cat([ # th.nn.functional.interpolate( # nvs_pred['image_raw'], # size=nvs_pred['image_sr'].shape[2:], # mode='bilinear', # align_corners=False, # antialias=True), # nvs_pred['image_sr'], # ], dim=1), # D=self.ddp_nvs_cvD) # TODO, add SR for FFHQ # else: d_loss_nvs = self.run_D_Diter( real=cano_pred['image_raw'], fake=nvs_pred['image_raw'], D=self.ddp_nvs_cvD) # TODO, add SR for FFHQ log_rec3d_loss_dict( {'vision_aided_loss/D_nvs': d_loss_nvs}) self.mp_trainer_cvD.backward(d_loss_nvs) 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 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 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) 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) else: gt_depth = self.pool_64(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_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' ) print( '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) # only use novel view D 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() } with th.autocast(device_type='cuda', dtype=th.float16, enabled=self.mp_trainer_rec.use_amp): nvs_pred = self.rec_model( img=micro['img_to_encoder'], c=th.cat([ micro['c'][1:], micro['c'][:1], ])) # ! render novel views only for D loss # add cvD supervision # if 'image_sr' in nvs_pred: # # concat sr and raw results # vision_aided_loss = self.ddp_nvs_cvD( # nvs_pred['image_raw'], # # th.cat([ # # th.nn.functional.interpolate( # # size=nvs_pred['image_sr'].shape[2:], # # mode='bilinear', # # align_corners=False, # # antialias=True), # # # nvs_pred['image_sr'], # # ], dim=1), # for_G=True).mean() # else: vision_aided_loss = self.ddp_nvs_cvD( nvs_pred['image_raw'], for_G=True).mean() # [B, 1] shape loss = vision_aided_loss * self.loss_class.opt.nvs_cvD_lambda log_rec3d_loss_dict({ 'vision_aided_loss/G_nvs': 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_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 = nvs_pred['image_depth'] pred_depth = (pred_depth - pred_depth.min()) / ( pred_depth.max() - pred_depth.min()) pred_img = nvs_pred['image_raw'] gt_img = micro['img'] if 'image_sr' in nvs_pred: if nvs_pred['image_sr'].shape[-1] == 512: pred_img = th.cat([ self.pool_512(pred_img), nvs_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 nvs_pred['image_sr'].shape[-1] == 256: pred_img = th.cat([ self.pool_256(pred_img), nvs_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), nvs_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) else: gt_depth = self.pool_64(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) 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) Image.fromarray(vis).save( f'{logger.get_dir()}/{self.step+self.resume_step}_nvs.jpg' ) print( '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) 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 print(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 print(f'mark {model_name} loading ', flush=True) resume_state_dict = dist_util.load_state_dict( resume_checkpoint, map_location=map_location) print(f'mark {model_name} loading finished', flush=True) 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: print('ignore ', k) else: print('!!!! ignore key: ', k, ": ", v.size(),) # 'shape in model: ', model_state_dict[k].size()) 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()) print(f'synced {model_name} params')