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 flip_yaw(pose_matrix): flipped = pose_matrix.clone() flipped[:, 0, 1] *= -1 flipped[:, 0, 2] *= -1 flipped[:, 1, 0] *= -1 flipped[:, 2, 0] *= -1 flipped[:, 0, 3] *= -1 # st() return flipped 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 # from .nvD import class TrainLoop3DcvD_nvsD_canoD(TrainLoop3DcvD): # class TrainLoop3DcvD_nvsD_canoD(): def __init__(self, *, 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, **kwargs) device = dist_util.dev() self.cano_cvD = vision_aided_loss.Discriminator( cv_type='clip', loss_type='multilevel_sigmoid_s', device=device).to(device) self.cano_cvD.cv_ensemble.requires_grad_( False) # Freeze feature extractor # self.cano_cvD.train() cvD_model_params = list(self.cano_cvD.parameters()) SR_TRAINING = False if SR_TRAINING: # replace the conv1 with 6 channel input # width, patch_size = self.nvs_cvD.cv_ensemble vision_width, vision_patch_size = [ self.cano_cvD.cv_ensemble.models[0].model.conv1.weight.shape[k] for k in [0, -1] ] self.cano_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()) cvD_model_params += list( self.cano_cvD.cv_ensemble.models[0].model.conv1.parameters()) self.cano_cvD.cv_ensemble.models[ 0].image_mean = self.cano_cvD.cv_ensemble.models[ 0].image_mean.repeat(2) self.cano_cvD.cv_ensemble.models[ 0].image_std = self.cano_cvD.cv_ensemble.models[ 0].image_std.repeat(2) # logger.log(f'cano_cvD_model_params: {cvD_model_params}') self._load_and_sync_parameters(model=self.cano_cvD, model_name='cano_cvD') self.mp_trainer_canonical_cvD = MixedPrecisionTrainer( model=self.cano_cvD, use_fp16=self.use_fp16, fp16_scale_growth=fp16_scale_growth, model_name='canonical_cvD', use_amp=use_amp, model_params=cvD_model_params) # cano_lr = 2e-5 * (lr / 1e-5) # D_lr=2e-4 in cvD by default # cano_lr = 5e-5 * (lr / 1e-5) # D_lr=2e-4 in cvD by default cano_lr = 2e-4 * ( lr / 1e-5) # D_lr=2e-4 in cvD by default. 1e-4 still overfitting self.opt_cano_cvD = AdamW( self.mp_trainer_canonical_cvD.master_params, lr=cano_lr, # same as the G betas=(0, 0.999), eps=1e-8) # dlr in biggan cfg logger.log(f'cpt_cano_cvD lr: {cano_lr}') if self.use_ddp: self.ddp_cano_cvD = DDP( self.cano_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_cano_cvD = self.cano_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 == 'd_step_rec': self.forward_D(batch, behaviour='rec') # _ = self.mp_trainer_cvD.optimize(self.opt_cvD) _ = self.mp_trainer_canonical_cvD.optimize(self.opt_cano_cvD) 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_nvs': self.forward_D(batch, behaviour='nvs') _ = self.mp_trainer_cvD.optimize(self.opt_cvD) # _ = self.mp_trainer_canonical_cvD.optimize(self.opt_cano_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) if self.novel_view_poses is None: self.novel_view_poses = th.roll(batch['c'], 1, 0).to( dist_util.dev()) # save for eval visualization use self.run_step(batch, 'g_step_rec') # if self.step % 2 == 0: batch = next(self.data) self.run_step(batch, 'd_step_rec') # if self.step % 2 == 1: batch = next(self.data) self.run_step(batch, 'g_step_nvs') batch = next(self.data) self.run_step(batch, 'd_step_nvs') 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 self.step % self.eval_interval == 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 th.cuda.empty_cache() dist_util.synchronize() if self.step % self.save_interval == 0: self.save() self.save(self.mp_trainer_cvD, self.mp_trainer_cvD.model_name) self.save(self.mp_trainer_canonical_cvD, self.mp_trainer_canonical_cvD.model_name) 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, self.mp_trainer_cvD.model_name) self.save(self.mp_trainer_canonical_cvD, self.mp_trainer_canonical_cvD.model_name) 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_canonical_cvD, 'cvD') def forward_D(self, batch, behaviour): # update D self.mp_trainer_canonical_cvD.zero_grad() self.mp_trainer_cvD.zero_grad() self.rec_model.requires_grad_(False) # self.ddp_model.requires_grad_(False) # update two D if behaviour == 'nvs': self.ddp_nvs_cvD.requires_grad_(True) self.ddp_cano_cvD.requires_grad_(False) else: # update rec canonical D self.ddp_nvs_cvD.requires_grad_(False) self.ddp_cano_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_canonical_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') # TODO, optimize with one encoder, and two triplane decoder # FIXME: quit autocast to runbackward if behaviour == 'rec': if 'image_sr' in cano_pred: # try concat them in batch # d_loss = self.run_D_Diter( # real=th.cat([ # th.nn.functional.interpolate( # micro['img'], # size=micro['img_sr'].shape[2:], # mode='bilinear', # align_corners=False, # antialias=True), # micro['img_sr'], # ], # dim=1), # fake=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), # D=self.ddp_cano_cvD) # TODO, add SR for FFHQ d_loss = self.run_D_Diter( real= micro['img_sr'], fake=cano_pred['image_sr'], D=self.ddp_cano_cvD) # TODO, add SR for FFHQ else: d_loss = self.run_D_Diter( real=micro['img'], fake=cano_pred['image_raw'], D=self.ddp_cano_cvD) # TODO, add SR for FFHQ log_rec3d_loss_dict( {'vision_aided_loss/D_cano': d_loss}) # self.mp_trainer_canonical_cvD.backward(d_loss) else: assert behaviour == 'nvs' nvs_pred = self.rec_model(latent=latent, c=novel_view_c, behaviour='triplane_dec') if 'image_sr' in nvs_pred: d_loss = self.run_D_Diter( real=cano_pred['image_sr'], # th.cat([ # th.nn.functional.interpolate( # cano_pred['image_raw'], # size=cano_pred['image_sr'].shape[2:], # mode='bilinear', # align_corners=False, # antialias=True), # ], # dim=1), fake= nvs_pred['image_sr'], # th.cat([ # th.nn.functional.interpolate( # nvs_pred['image_raw'], # size=nvs_pred['image_sr'].shape[2:], # mode='bilinear', # align_corners=False, # antialias=True), # ], # dim=1), D=self.ddp_nvs_cvD) # TODO, add SR for FFHQ else: d_loss = 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}) # self.mp_trainer_cvD.backward(d_loss) if behaviour == 'rec': self.mp_trainer_canonical_cvD.backward(d_loss) else: assert behaviour == 'nvs' self.mp_trainer_cvD.backward(d_loss) def forward_G_rec(self, batch): # update G self.mp_trainer_rec.zero_grad() self.rec_model.requires_grad_(True) self.ddp_cano_cvD.requires_grad_(False) 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 cano_pred = pred # if last_batch or not self.use_ddp: # loss, loss_dict = self.loss_class(cano_pred, # target_for_rec, # test_mode=False, # step=self.step + # self.resume_step) # else: with self.rec_model.no_sync(): # type: ignore loss, loss_dict, fg_mask = self.loss_class(cano_pred, target_for_rec, test_mode=False, step=self.step + self.resume_step, return_fg_mask=True) # cano_pred_img = cano_pred['image_raw'] if self.loss_class.opt.symmetry_loss: pose, intrinsics = micro['c'][:, :16].reshape( -1, 4, 4), micro['c'][:, 16:] flipped_pose = flip_yaw(pose) mirror_c = th.cat( [flipped_pose.reshape(-1, 16), intrinsics], -1) nvs_pred = self.rec_model(latent={ k: v for k, v in pred.items() if 'latent' in k }, c=mirror_c, behaviour='triplane_dec', return_raw_only=True) # cano_pred_img = th.cat([cano_pred_img, nvs_pred['image_raw']], 0) # concat data for supervision nvs_gt = { k: th.flip(target_for_rec[k], [-1]) for k in ['img'] # fliplr leads to wrong color; B 3 H W shape } flipped_fg_mask = th.flip(fg_mask, [-1]) if 'conf_sigma' in pred: conf_sigma = th.flip(pred['conf_sigma'], [-1]) conf_sigma = th.nn.AdaptiveAvgPool2d(fg_mask.shape[-2:])(conf_sigma) # dynamically resize to target img size else: conf_sigma=None with self.rec_model.no_sync(): # type: ignore loss_symm, loss_dict_symm = self.loss_class.calc_2d_rec_loss( nvs_pred['image_raw'], nvs_gt['img'], flipped_fg_mask, # test_mode=True, test_mode=False, step=self.step + self.resume_step, conf_sigma=conf_sigma, ) loss += (loss_symm * 1.0) # as in unsup3d # if conf_sigma is not None: # conf_loss = th.nn.functional.mse_loss(conf_sigma, flipped_fg_mask) * 0.2 # loss += conf_loss # a log that regularizes all confidence to 1 # loss_dict[f'conf_loss'] = conf_loss for k, v in loss_dict_symm.items(): loss_dict[f'{k}_symm'] = v # add cvD supervision # ! TODO if 'image_sr' in cano_pred: # concat both resolution # vision_aided_loss = self.ddp_cano_cvD( # 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), # 6 channel input # for_G=True).mean() # [B, 1] shape vision_aided_loss = self.ddp_cano_cvD( cano_pred['image_sr'], for_G=True).mean() # [B, 1] shape else: vision_aided_loss = self.ddp_cano_cvD( cano_pred['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 = th.tensor(self.loss_class.opt.rec_cvD_lambda).to( dist_util.dev()) # d_weight = calculate_adaptive_weight( # loss, # vision_aided_loss, # last_layer, # disc_weight_max=0.1) * self.loss_class.opt.rec_cvD_lambda loss += vision_aided_loss * d_weight loss_dict.update({ 'vision_aided_loss/G_rec': (vision_aided_loss * d_weight).detach(), 'd_weight': d_weight }) log_rec3d_loss_dict(loss_dict) self.mp_trainer_rec.backward( loss) # no nvs cvD loss, following VQ3D # DDP some parameters no grad: # for name, p in self.ddp_model.named_parameters(): # if p.grad is None: # print(f"(in rec)found rec unused param: {name}") # ! 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_img = self.pool_64(gt_img) # 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' # ) 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) def norm_depth(pred_depth): # to [-1,1] # pred_depth = pred['image_depth'] pred_depth = (pred_depth - pred_depth.min()) / ( pred_depth.max() - pred_depth.min()) return -(pred_depth * 2 - 1) pred_img = pred['image_raw'].clip(-1,1) gt_img = micro['img'] # infer novel view also pred_nv_img = self.rec_model( img=micro['img_to_encoder'], c=self.novel_view_poses) # pred: (B, 3, 64, 64) # if 'depth' in micro: gt_depth = micro['depth'] if gt_depth.ndim == 3: gt_depth = gt_depth.unsqueeze(1) gt_depth = norm_depth(gt_depth) # gt_depth = (gt_depth - gt_depth.min()) / (gt_depth.max() - # gt_depth.min()) # if True: if 'image_depth' in pred: # pred_depth = pred['image_depth'] # pred_depth = (pred_depth - pred_depth.min()) / ( # pred_depth.max() - pred_depth.min()) pred_depth = norm_depth(pred['image_depth']) pred_nv_depth = norm_depth( pred_nv_img['image_depth']) else: pred_depth = th.zeros_like(gt_depth) pred_nv_depth = th.zeros_like(gt_depth) 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) pred_nv_depth = self.pool_512(pred_nv_depth) gt_depth = self.pool_512(gt_depth) pred_vis_nv = th.cat( [self.pool_512(pred_nv_img['image_raw']), pred_nv_img['image_sr']], dim=-1) 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) if gt_img.shape[-1] == 64: gt_depth = self.pool_64(gt_depth) elif gt_img.shape[-1] == 128: gt_depth = self.pool_128(gt_depth) # else: # gt_depth = self.pool_64(gt_depth) # st() pred_vis = th.cat( [pred_img, pred_depth.repeat_interleave(3, dim=1)], dim=-1) # B, 3, H, W pred_vis_nv = th.cat([ # pred_nv_img['image_raw'].clip(-1,1), pred_vis_nv, pred_nv_depth.repeat_interleave(3, dim=1) ], dim=-1) # B, 3, H, W pred_vis = th.cat([pred_vis, pred_vis_nv], dim=-2) # cat in H dim gt_vis = th.cat( [gt_img, gt_depth.repeat_interleave(3, dim=1)], dim=-1) # TODO, fail to load depth. range [0, 1] # vis = th.cat([gt_vis, pred_vis], dim=-2)[0].permute( vis = th.cat([gt_vis, pred_vis], dim=-2) # .permute( # 0, 2, 3, 1).cpu() vis_tensor = torchvision.utils.make_grid( vis, nrow=vis.shape[-1] // 64) # HWC torchvision.utils.save_image( vis_tensor, f'{logger.get_dir()}/{self.step+self.resume_step}.jpg', normalize=True, value_range=(-1,1)) # 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}.jpg') logger.log( 'log vis to: ', f'{logger.get_dir()}/{self.step+self.resume_step}.jpg') def forward_G_nvs(self, batch): # update G self.mp_trainer_rec.zero_grad() self.rec_model.requires_grad_(True) self.ddp_cano_cvD.requires_grad_(False) 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( # # pred_nv['image_sr'], # # 0.5 * pred_nv['image_sr'] + 0.5 * th.nn.functional.interpolate(pred_nv['image_raw'], size=pred_nv['image_sr'].shape[2:], mode='bilinear'), # 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), # for_G=True).mean() # ! for debugging vision_aided_loss = self.ddp_nvs_cvD( # pred_nv['image_sr'], # 0.5 * pred_nv['image_sr'] + 0.5 * th.nn.functional.interpolate(pred_nv['image_raw'], size=pred_nv['image_sr'].shape[2:], mode='bilinear'), nvs_pred['image_sr'], for_G=True).mean() # ! for debugging # supervise sr only # vision_aided_loss = self.ddp_nvs_cvD( # # pred_nv['image_sr'], # # 0.5 * pred_nv['image_sr'] + 0.5 * th.nn.functional.interpolate(pred_nv['image_raw'], size=pred_nv['image_sr'].shape[2:], mode='bilinear'), # th.cat([nvs_pred['image_sr'], # th.nn.functional.interpolate(nvs_pred['image_raw'], size=nvs_pred['image_sr'].shape[2:], mode='bilinear', # align_corners=False, # antialias=True),]), # for_G=True).mean() # ! for debugging # pred_nv['image_raw'], for_G=True).mean() # [B, 1] shape 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 # vision_aided_loss * self.loss_class.opt.nvs_cvD_lambda, }) self.mp_trainer_rec.backward(loss) # ! move to other places, add tensorboard # if dist_util.get_rank() == 0 and self.step % 500 == 0: if dist_util.get_rank() == 0 and self.step % 500 == 1: with th.no_grad(): # gt_vis = th.cat([batch['img'], batch['depth']], dim=-1) def norm_depth(pred_depth): # to [-1,1] # pred_depth = pred['image_depth'] pred_depth = (pred_depth - pred_depth.min()) / ( pred_depth.max() - pred_depth.min()) return -(pred_depth * 2 - 1) gt_depth = micro['depth'] if gt_depth.ndim == 3: gt_depth = gt_depth.unsqueeze(1) gt_depth = norm_depth(gt_depth) # if True: # pred_depth = nvs_pred['image_depth'] # pred_depth = (pred_depth - pred_depth.min()) / ( # pred_depth.max() - pred_depth.min()) pred_depth = norm_depth(nvs_pred['image_depth']) 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) if gt_img.shape[-1] == 64: gt_depth = self.pool_64(gt_depth) elif gt_img.shape[-1] == 128: gt_depth = self.pool_128(gt_depth) # else: # gt_img = self.pool_64(gt_img) # 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 = 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) # print(vis.shape) 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' ) class TrainLoop3DcvD_nvsD_canoD_eg3d(TrainLoop3DcvD_nvsD_canoD): def __init__(self, *, 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, **kwargs) self.rendering_kwargs = self.rec_model.module.decoder.triplane_decoder.rendering_kwargs # type: ignore self._prepare_nvs_pose() # for eval novelview visualization @th.inference_mode() def eval_novelview_loop(self): # novel view synthesis given evaluation camera trajectory # for i in range(0, len(c_list), 1): # TODO, larger batch size for eval for i, batch in enumerate(tqdm(self.eval_data)): micro = {k: v.to(dist_util.dev()) for k, v in batch.items()} video_out = imageio.get_writer( f'{logger.get_dir()}/video_novelview_{self.step+self.resume_step}_batch_{i}.mp4', mode='I', fps=60, codec='libx264') for idx, c in enumerate(self.all_nvs_params): pred = self.rec_model(img=micro['img_to_encoder'], c=c.unsqueeze(0).repeat_interleave(micro['img'].shape[0], 0)) # pred: (B, 3, 64, 64) # c=micro['c']) # pred: (B, 3, 64, 64) # normalize depth # if True: pred_depth = pred['image_depth'] pred_depth = (pred_depth - pred_depth.min()) / (pred_depth.max() - pred_depth.min()) if 'image_sr' in pred: if pred['image_sr'].shape[-1] == 512: pred_vis = th.cat([ micro['img_sr'], self.pool_512(pred['image_raw']), pred['image_sr'], self.pool_512(pred_depth).repeat_interleave(3, dim=1) ], dim=-1) elif pred['image_sr'].shape[-1] == 256: pred_vis = th.cat([ micro['img_sr'], self.pool_256(pred['image_raw']), pred['image_sr'], self.pool_256(pred_depth).repeat_interleave(3, dim=1) ], dim=-1) else: pred_vis = th.cat([ micro['img_sr'], self.pool_128(pred['image_raw']), self.pool_128(pred['image_sr']), self.pool_128(pred_depth).repeat_interleave(3, dim=1) ], dim=-1) else: # st() pred_vis = th.cat([ self.pool_128(micro['img']), self.pool_128(pred['image_raw']), self.pool_128(pred_depth).repeat_interleave(3, dim=1) ], dim=-1) # B, 3, H, W # ! cooncat h dim pred_vis = pred_vis.permute(0,2,3,1).flatten(0,1) # H W 3 # vis = pred_vis.permute(0, 2, 3, 1).cpu().numpy() # vis = pred_vis.permute(1,2,0).cpu().numpy() vis = pred_vis.cpu().numpy() vis = vis * 127.5 + 127.5 vis = vis.clip(0, 255).astype(np.uint8) # for j in range(vis.shape[0]): # video_out.append_data(vis[j]) video_out.append_data(vis) video_out.close() th.cuda.empty_cache() def _prepare_nvs_pose(self): from nsr.camera_utils import LookAtPoseSampler, FOV_to_intrinsics device = dist_util.dev() fov_deg = 18.837 # for ffhq/afhq intrinsics = FOV_to_intrinsics(fov_deg, device=device) all_nvs_params = [] pitch_range = 0.25 yaw_range = 0.35 num_keyframes = 10 # how many nv poses to sample from w_frames = 1 cam_pivot = th.Tensor(self.rendering_kwargs.get('avg_camera_pivot')).to(device) cam_radius = self.rendering_kwargs.get('avg_camera_radius') for frame_idx in range(num_keyframes): cam2world_pose = LookAtPoseSampler.sample(3.14/2 + yaw_range * np.sin(2 * 3.14 * frame_idx / (num_keyframes * w_frames)), 3.14/2 -0.05 + pitch_range * np.cos(2 * 3.14 * frame_idx / (num_keyframes * w_frames)), cam_pivot, radius=cam_radius, device=device) camera_params = th.cat([cam2world_pose.reshape(-1, 16), intrinsics.reshape(-1, 9)], 1) all_nvs_params.append(camera_params) self.all_nvs_params = th.cat(all_nvs_params, 0)