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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 .nvsD_canoD import TrainLoop3DcvD_nvsD_canoD
class TrainLoop3DcvD_nvsD_canoD_multiview(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)
assert not self.mp_trainer_rec.use_amp, 'amp may lead to grad nan?'
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]
target_cano = {}
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()
}
for k, v in micro.items():
if k[:2] == 'nv':
orig_key = k.replace('nv_', '')
# target_nvs[orig_key] = v
target_cano[orig_key] = micro[orig_key]
# 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
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)
if 'image_sr' in cano_pred:
raise NotImplementedError()
# 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
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)
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)
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_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()
}
target_nvs = {}
for k, v in micro.items():
if k[:2] == 'nv':
orig_key = k.replace('nv_', '')
target_nvs[orig_key] = v
# target_cano[orig_key] = micro[orig_key]
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=micro['nv_c'],
) # predict novel view here
# 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:
raise NotImplementedError()
# 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
# 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
# ! add nv reconstruction loss
with self.rec_model.no_sync(): # type: ignore
loss, loss_dict, fg_mask = self.loss_class(
nvs_pred,
target_nvs,
step=self.step + self.resume_step,
test_mode=False,
return_fg_mask=True,
conf_sigma_l1=None,
conf_sigma_percl=None)
log_rec3d_loss_dict(loss_dict)
loss += vision_aided_loss * self.loss_class.opt.nvs_cvD_lambda
log_rec3d_loss_dict({
'vision_aided_loss/G_nvs':
vision_aided_loss * self.loss_class.opt.nvs_cvD_lambda,
**{f'{k}_nv': v for k, v in loss_dict.items()}
})
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'
)
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