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utils.py
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utils.py
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import os
import gc
import glob
import tqdm
import math
import imageio
import psutil
from pathlib import Path
import random
import shutil
import warnings
import tensorboardX
import numpy as np
import time
import cv2
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torch.distributed as dist
import torchvision.transforms.functional as TF
from torchmetrics import PearsonCorrCoef
from rich.console import Console
from torch_ema import ExponentialMovingAverage
from packaging import version as pver
def adjust_text_embeddings(embeddings, azimuth, opt):
text_z_list = []
weights_list = []
K = 0
for b in range(azimuth.shape[0]):
text_z_, weights_ = get_pos_neg_text_embeddings(embeddings, azimuth[b], opt)
K = max(K, weights_.shape[0])
text_z_list.append(text_z_)
weights_list.append(weights_)
# Interleave text_embeddings from different dirs to form a batch
text_embeddings = []
for i in range(K):
for text_z in text_z_list:
# if uneven length, pad with the first embedding
text_embeddings.append(text_z[i] if i < len(text_z) else text_z[0])
text_embeddings = torch.stack(text_embeddings, dim=0) # [B * K, 77, 768]
# Interleave weights from different dirs to form a batch
weights = []
for i in range(K):
for weights_ in weights_list:
weights.append(weights_[i] if i < len(weights_) else torch.zeros_like(weights_[0]))
weights = torch.stack(weights, dim=0) # [B * K]
return text_embeddings, weights
def get_pos_neg_text_embeddings(embeddings, azimuth_val, opt):
if azimuth_val >= -90 and azimuth_val < 90:
if azimuth_val >= 0:
r = 1 - azimuth_val / 90
else:
r = 1 + azimuth_val / 90
start_z = embeddings['front']
end_z = embeddings['side']
# if random.random() < 0.3:
# r = r + random.gauss(0, 0.08)
pos_z = r * start_z + (1 - r) * end_z
text_z = torch.cat([pos_z, embeddings['front'], embeddings['side']], dim=0)
if r > 0.8:
front_neg_w = 0.0
else:
front_neg_w = math.exp(-r * opt.front_decay_factor) * opt.negative_w
if r < 0.2:
side_neg_w = 0.0
else:
side_neg_w = math.exp(-(1-r) * opt.side_decay_factor) * opt.negative_w
weights = torch.tensor([1.0, front_neg_w, side_neg_w])
else:
if azimuth_val >= 0:
r = 1 - (azimuth_val - 90) / 90
else:
r = 1 + (azimuth_val + 90) / 90
start_z = embeddings['side']
end_z = embeddings['back']
# if random.random() < 0.3:
# r = r + random.gauss(0, 0.08)
pos_z = r * start_z + (1 - r) * end_z
text_z = torch.cat([pos_z, embeddings['side'], embeddings['front']], dim=0)
front_neg_w = opt.negative_w
if r > 0.8:
side_neg_w = 0.0
else:
side_neg_w = math.exp(-r * opt.side_decay_factor) * opt.negative_w / 2
weights = torch.tensor([1.0, side_neg_w, front_neg_w])
return text_z, weights.to(text_z.device)
def custom_meshgrid(*args):
# ref: https://pytorch.org/docs/stable/generated/torch.meshgrid.html?highlight=meshgrid#torch.meshgrid
if pver.parse(torch.__version__) < pver.parse('1.10'):
return torch.meshgrid(*args)
else:
return torch.meshgrid(*args, indexing='ij')
def safe_normalize(x, eps=1e-20):
return x / torch.sqrt(torch.clamp(torch.sum(x * x, -1, keepdim=True), min=eps))
@torch.cuda.amp.autocast(enabled=False)
def get_rays(poses, intrinsics, H, W, N=-1, error_map=None):
''' get rays
Args:
poses: [B, 4, 4], cam2world
intrinsics: [4]
H, W, N: int
error_map: [B, 128 * 128], sample probability based on training error
Returns:
rays_o, rays_d: [B, N, 3]
inds: [B, N]
'''
device = poses.device
B = poses.shape[0]
fx, fy, cx, cy = intrinsics
i, j = custom_meshgrid(torch.linspace(0, W-1, W, device=device), torch.linspace(0, H-1, H, device=device))
i = i.t().reshape([1, H*W]).expand([B, H*W]) + 0.5
j = j.t().reshape([1, H*W]).expand([B, H*W]) + 0.5
results = {}
if N > 0:
N = min(N, H*W)
if error_map is None:
inds = torch.randint(0, H*W, size=[N], device=device) # may duplicate
inds = inds.expand([B, N])
else:
# weighted sample on a low-reso grid
inds_coarse = torch.multinomial(error_map.to(device), N, replacement=False) # [B, N], but in [0, 128*128)
# map to the original resolution with random perturb.
inds_x, inds_y = inds_coarse // 128, inds_coarse % 128 # `//` will throw a warning in torch 1.10... anyway.
sx, sy = H / 128, W / 128
inds_x = (inds_x * sx + torch.rand(B, N, device=device) * sx).long().clamp(max=H - 1)
inds_y = (inds_y * sy + torch.rand(B, N, device=device) * sy).long().clamp(max=W - 1)
inds = inds_x * W + inds_y
results['inds_coarse'] = inds_coarse # need this when updating error_map
i = torch.gather(i, -1, inds)
j = torch.gather(j, -1, inds)
results['inds'] = inds
else:
inds = torch.arange(H*W, device=device).expand([B, H*W])
zs = - torch.ones_like(i)
xs = - (i - cx) / fx * zs
ys = (j - cy) / fy * zs
directions = torch.stack((xs, ys, zs), dim=-1)
# directions = safe_normalize(directions)
rays_d = directions @ poses[:, :3, :3].transpose(-1, -2) # (B, N, 3)
rays_o = poses[..., :3, 3] # [B, 3]
rays_o = rays_o[..., None, :].expand_as(rays_d) # [B, N, 3]
results['rays_o'] = rays_o
results['rays_d'] = rays_d
return results
def seed_everything(seed):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
#torch.backends.cudnn.deterministic = True
#torch.backends.cudnn.benchmark = True
@torch.jit.script
def linear_to_srgb(x):
return torch.where(x < 0.0031308, 12.92 * x, 1.055 * x ** 0.41666 - 0.055)
@torch.jit.script
def srgb_to_linear(x):
return torch.where(x < 0.04045, x / 12.92, ((x + 0.055) / 1.055) ** 2.4)
class Trainer(object):
def __init__(self,
argv, # command line args
name, # name of this experiment
opt, # extra conf
model, # network
guidance, # guidance network
criterion=None, # loss function, if None, assume inline implementation in train_step
optimizer=None, # optimizer
ema_decay=None, # if use EMA, set the decay
lr_scheduler=None, # scheduler
metrics=[], # metrics for evaluation, if None, use val_loss to measure performance, else use the first metric.
local_rank=0, # which GPU am I
world_size=1, # total num of GPUs
device=None, # device to use, usually setting to None is OK. (auto choose device)
mute=False, # whether to mute all print
fp16=False, # amp optimize level
max_keep_ckpt=2, # max num of saved ckpts in disk
workspace='workspace', # workspace to save logs & ckpts
best_mode='min', # the smaller/larger result, the better
use_loss_as_metric=True, # use loss as the first metric
report_metric_at_train=False, # also report metrics at training
use_checkpoint="latest", # which ckpt to use at init time
use_tensorboardX=True, # whether to use tensorboard for logging
scheduler_update_every_step=False, # whether to call scheduler.step() after every train step
):
self.argv = argv
self.name = name
self.opt = opt
self.mute = mute
self.metrics = metrics
self.local_rank = local_rank
self.world_size = world_size
self.workspace = workspace
self.ema_decay = ema_decay
self.fp16 = fp16
self.best_mode = best_mode
self.use_loss_as_metric = use_loss_as_metric
self.report_metric_at_train = report_metric_at_train
self.max_keep_ckpt = max_keep_ckpt
self.use_checkpoint = use_checkpoint
self.use_tensorboardX = use_tensorboardX
self.time_stamp = time.strftime("%Y-%m-%d_%H-%M-%S")
self.scheduler_update_every_step = scheduler_update_every_step
self.device = device if device is not None else torch.device(f'cuda:{local_rank}' if torch.cuda.is_available() else 'cpu')
self.console = Console()
model.to(self.device)
if self.world_size > 1:
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[local_rank])
self.model = model
# guide model
self.guidance = guidance
self.embeddings = {}
# text prompt / images
if self.guidance is not None:
for key in self.guidance:
for p in self.guidance[key].parameters():
p.requires_grad = False
self.embeddings[key] = {}
self.prepare_embeddings()
if isinstance(criterion, nn.Module):
criterion.to(self.device)
self.criterion = criterion
if self.opt.images is not None:
self.pearson = PearsonCorrCoef().to(self.device)
if optimizer is None:
self.optimizer = optim.Adam(self.model.parameters(), lr=0.001, weight_decay=5e-4) # naive adam
else:
self.optimizer = optimizer(self.model)
if lr_scheduler is None:
self.lr_scheduler = optim.lr_scheduler.LambdaLR(self.optimizer, lr_lambda=lambda epoch: 1) # fake scheduler
else:
self.lr_scheduler = lr_scheduler(self.optimizer)
if ema_decay is not None:
self.ema = ExponentialMovingAverage(self.model.parameters(), decay=ema_decay)
else:
self.ema = None
self.scaler = torch.cuda.amp.GradScaler(enabled=self.fp16)
# variable init
self.total_train_t = 0
self.epoch = 0
self.global_step = 0
self.local_step = 0
self.stats = {
"loss": [],
"valid_loss": [],
"results": [], # metrics[0], or valid_loss
"checkpoints": [], # record path of saved ckpt, to automatically remove old ckpt
"best_result": None,
}
# auto fix
if len(metrics) == 0 or self.use_loss_as_metric:
self.best_mode = 'min'
# workspace prepare
self.log_ptr = None
if self.workspace is not None:
os.makedirs(self.workspace, exist_ok=True)
self.log_path = os.path.join(workspace, f"log_{self.name}.txt")
self.log_ptr = open(self.log_path, "a+")
self.ckpt_path = os.path.join(self.workspace, 'checkpoints')
self.best_path = f"{self.ckpt_path}/{self.name}.pth"
os.makedirs(self.ckpt_path, exist_ok=True)
# Save a copy of image_config in the experiment workspace
if opt.image_config is not None:
shutil.copyfile(opt.image_config, os.path.join(self.workspace, os.path.basename(opt.image_config)))
# Save a copy of images in the experiment workspace
if opt.images is not None:
for image_file in opt.images:
shutil.copyfile(image_file, os.path.join(self.workspace, os.path.basename(image_file)))
self.log(f'[INFO] Cmdline: {self.argv}')
self.log(f'[INFO] opt: {self.opt}')
self.log(f'[INFO] Trainer: {self.name} | {self.time_stamp} | {self.device} | {"fp16" if self.fp16 else "fp32"} | {self.workspace}')
self.log(f'[INFO] #parameters: {sum([p.numel() for p in model.parameters() if p.requires_grad])}')
if self.workspace is not None:
if self.use_checkpoint == "scratch":
self.log("[INFO] Training from scratch ...")
elif self.use_checkpoint == "latest":
self.log("[INFO] Loading latest checkpoint ...")
self.load_checkpoint()
elif self.use_checkpoint == "latest_model":
self.log("[INFO] Loading latest checkpoint (model only)...")
self.load_checkpoint(model_only=True)
elif self.use_checkpoint == "best":
if os.path.exists(self.best_path):
self.log("[INFO] Loading best checkpoint ...")
self.load_checkpoint(self.best_path)
else:
self.log(f"[INFO] {self.best_path} not found, loading latest ...")
self.load_checkpoint()
else: # path to ckpt
self.log(f"[INFO] Loading {self.use_checkpoint} ...")
self.load_checkpoint(self.use_checkpoint)
# calculate the text embs.
@torch.no_grad()
def prepare_embeddings(self):
# text embeddings (stable-diffusion)
if self.opt.text is not None:
if 'SD' in self.guidance:
self.embeddings['SD']['default'] = self.guidance['SD'].get_text_embeds([self.opt.text])
self.embeddings['SD']['uncond'] = self.guidance['SD'].get_text_embeds([self.opt.negative])
for d in ['front', 'side', 'back']:
self.embeddings['SD'][d] = self.guidance['SD'].get_text_embeds([f"{self.opt.text}, {d} view"])
if 'IF' in self.guidance:
self.embeddings['IF']['default'] = self.guidance['IF'].get_text_embeds([self.opt.text])
self.embeddings['IF']['uncond'] = self.guidance['IF'].get_text_embeds([self.opt.negative])
for d in ['front', 'side', 'back']:
self.embeddings['IF'][d] = self.guidance['IF'].get_text_embeds([f"{self.opt.text}, {d} view"])
if 'clip' in self.guidance:
self.embeddings['clip']['text'] = self.guidance['clip'].get_text_embeds(self.opt.text)
if self.opt.images is not None:
h = int(self.opt.known_view_scale * self.opt.h)
w = int(self.opt.known_view_scale * self.opt.w)
# load processed image
for image in self.opt.images:
assert image.endswith('_rgba.png') # the rest of this code assumes that the _rgba image has been passed.
rgbas = [cv2.cvtColor(cv2.imread(image, cv2.IMREAD_UNCHANGED), cv2.COLOR_BGRA2RGBA) for image in self.opt.images]
rgba_hw = np.stack([cv2.resize(rgba, (w, h), interpolation=cv2.INTER_AREA).astype(np.float32) / 255 for rgba in rgbas])
rgb_hw = rgba_hw[..., :3] * rgba_hw[..., 3:] + (1 - rgba_hw[..., 3:])
self.rgb = torch.from_numpy(rgb_hw).permute(0,3,1,2).contiguous().to(self.device)
self.mask = torch.from_numpy(rgba_hw[..., 3] > 0.5).to(self.device)
print(f'[INFO] dataset: load image prompt {self.opt.images} {self.rgb.shape}')
# load depth
depth_paths = [image.replace('_rgba.png', '_depth.png') for image in self.opt.images]
depths = [cv2.imread(depth_path, cv2.IMREAD_UNCHANGED) for depth_path in depth_paths]
depth = np.stack([cv2.resize(depth, (w, h), interpolation=cv2.INTER_AREA) for depth in depths])
self.depth = torch.from_numpy(depth.astype(np.float32) / 255).to(self.device) # TODO: this should be mapped to FP16
print(f'[INFO] dataset: load depth prompt {depth_paths} {self.depth.shape}')
# load normal # TODO: don't load if normal loss is 0
normal_paths = [image.replace('_rgba.png', '_normal.png') for image in self.opt.images]
normals = [cv2.imread(normal_path, cv2.IMREAD_UNCHANGED) for normal_path in normal_paths]
normal = np.stack([cv2.resize(normal, (w, h), interpolation=cv2.INTER_AREA) for normal in normals])
self.normal = torch.from_numpy(normal.astype(np.float32) / 255).to(self.device)
print(f'[INFO] dataset: load normal prompt {normal_paths} {self.normal.shape}')
# encode embeddings for zero123
if 'zero123' in self.guidance:
rgba_256 = np.stack([cv2.resize(rgba, (256, 256), interpolation=cv2.INTER_AREA).astype(np.float32) / 255 for rgba in rgbas])
rgbs_256 = rgba_256[..., :3] * rgba_256[..., 3:] + (1 - rgba_256[..., 3:])
rgb_256 = torch.from_numpy(rgbs_256).permute(0,3,1,2).contiguous().to(self.device)
guidance_embeds = self.guidance['zero123'].get_img_embeds(rgb_256)
self.embeddings['zero123']['default'] = {
'zero123_ws' : self.opt.zero123_ws,
'c_crossattn' : guidance_embeds[0],
'c_concat' : guidance_embeds[1],
'ref_polars' : self.opt.ref_polars,
'ref_azimuths' : self.opt.ref_azimuths,
'ref_radii' : self.opt.ref_radii,
}
if 'clip' in self.guidance:
self.embeddings['clip']['image'] = self.guidance['clip'].get_img_embeds(self.rgb)
def __del__(self):
if self.log_ptr:
self.log_ptr.close()
def log(self, *args, **kwargs):
if self.local_rank == 0:
if not self.mute:
#print(*args)
self.console.print(*args, **kwargs)
if self.log_ptr:
print(*args, file=self.log_ptr)
self.log_ptr.flush() # write immediately to file
### ------------------------------
def train_step(self, data, save_guidance_path:Path=None):
"""
Args:
save_guidance_path: an image that combines the NeRF render, the added latent noise,
the denoised result and optionally the fully-denoised image.
"""
# perform RGBD loss instead of SDS if is image-conditioned
do_rgbd_loss = self.opt.images is not None and \
(self.global_step % self.opt.known_view_interval == 0)
# override random camera with fixed known camera
if do_rgbd_loss:
data = self.default_view_data
# experiment iterations ratio
# i.e. what proportion of this experiment have we completed (in terms of iterations) so far?
exp_iter_ratio = (self.global_step - self.opt.exp_start_iter) / (self.opt.exp_end_iter - self.opt.exp_start_iter)
# progressively relaxing view range
if self.opt.progressive_view:
r = min(1.0, self.opt.progressive_view_init_ratio + 2.0*exp_iter_ratio)
self.opt.phi_range = [self.opt.default_azimuth * (1 - r) + self.opt.full_phi_range[0] * r,
self.opt.default_azimuth * (1 - r) + self.opt.full_phi_range[1] * r]
self.opt.theta_range = [self.opt.default_polar * (1 - r) + self.opt.full_theta_range[0] * r,
self.opt.default_polar * (1 - r) + self.opt.full_theta_range[1] * r]
self.opt.radius_range = [self.opt.default_radius * (1 - r) + self.opt.full_radius_range[0] * r,
self.opt.default_radius * (1 - r) + self.opt.full_radius_range[1] * r]
self.opt.fovy_range = [self.opt.default_fovy * (1 - r) + self.opt.full_fovy_range[0] * r,
self.opt.default_fovy * (1 - r) + self.opt.full_fovy_range[1] * r]
# progressively increase max_level
if self.opt.progressive_level:
self.model.max_level = min(1.0, 0.25 + 2.0*exp_iter_ratio)
rays_o = data['rays_o'] # [B, N, 3]
rays_d = data['rays_d'] # [B, N, 3]
mvp = data['mvp'] # [B, 4, 4]
B, N = rays_o.shape[:2]
H, W = data['H'], data['W']
# When ref_data has B images > opt.batch_size
if B > self.opt.batch_size:
# choose batch_size images out of those B images
choice = torch.randperm(B)[:self.opt.batch_size]
B = self.opt.batch_size
rays_o = rays_o[choice]
rays_d = rays_d[choice]
mvp = mvp[choice]
if do_rgbd_loss:
ambient_ratio = 1.0
shading = 'lambertian' # use lambertian instead of albedo to get normal
as_latent = False
binarize = False
bg_color = torch.rand((B * N, 3), device=rays_o.device)
# add camera noise to avoid grid-like artifact
if self.opt.known_view_noise_scale > 0:
noise_scale = self.opt.known_view_noise_scale #* (1 - self.global_step / self.opt.iters)
rays_o = rays_o + torch.randn(3, device=self.device) * noise_scale
rays_d = rays_d + torch.randn(3, device=self.device) * noise_scale
elif exp_iter_ratio <= self.opt.latent_iter_ratio:
ambient_ratio = 1.0
shading = 'normal'
as_latent = True
binarize = False
bg_color = None
else:
if exp_iter_ratio <= self.opt.albedo_iter_ratio:
ambient_ratio = 1.0
shading = 'albedo'
else:
# random shading
ambient_ratio = self.opt.min_ambient_ratio + (1.0-self.opt.min_ambient_ratio) * random.random()
rand = random.random()
if rand >= (1.0 - self.opt.textureless_ratio):
shading = 'textureless'
else:
shading = 'lambertian'
as_latent = False
# random weights binarization (like mobile-nerf) [NOT WORKING NOW]
# binarize_thresh = min(0.5, -0.5 + self.global_step / self.opt.iters)
# binarize = random.random() < binarize_thresh
binarize = False
# random background
rand = random.random()
if self.opt.bg_radius > 0 and rand > 0.5:
bg_color = None # use bg_net
else:
bg_color = torch.rand(3).to(self.device) # single color random bg
outputs = self.model.render(rays_o, rays_d, mvp, H, W, staged=False, perturb=True, bg_color=bg_color, ambient_ratio=ambient_ratio, shading=shading, binarize=binarize)
pred_depth = outputs['depth'].reshape(B, 1, H, W)
pred_mask = outputs['weights_sum'].reshape(B, 1, H, W)
if 'normal_image' in outputs:
pred_normal = outputs['normal_image'].reshape(B, H, W, 3)
if as_latent:
# abuse normal & mask as latent code for faster geometry initialization (ref: fantasia3D)
pred_rgb = torch.cat([outputs['image'], outputs['weights_sum'].unsqueeze(-1)], dim=-1).reshape(B, H, W, 4).permute(0, 3, 1, 2).contiguous() # [B, 4, H, W]
else:
pred_rgb = outputs['image'].reshape(B, H, W, 3).permute(0, 3, 1, 2).contiguous() # [B, 3, H, W]
# known view loss
if do_rgbd_loss:
gt_mask = self.mask # [B, H, W]
gt_rgb = self.rgb # [B, 3, H, W]
gt_normal = self.normal # [B, H, W, 3]
gt_depth = self.depth # [B, H, W]
if len(gt_rgb) > self.opt.batch_size:
gt_mask = gt_mask[choice]
gt_rgb = gt_rgb[choice]
gt_normal = gt_normal[choice]
gt_depth = gt_depth[choice]
# color loss
gt_rgb = gt_rgb * gt_mask[:, None].float() + bg_color.reshape(B, H, W, 3).permute(0,3,1,2).contiguous() * (1 - gt_mask[:, None].float())
loss = self.opt.lambda_rgb * F.mse_loss(pred_rgb, gt_rgb)
# mask loss
loss = loss + self.opt.lambda_mask * F.mse_loss(pred_mask[:, 0], gt_mask.float())
# normal loss
if self.opt.lambda_normal > 0 and 'normal_image' in outputs:
valid_gt_normal = 1 - 2 * gt_normal[gt_mask] # [B, 3]
valid_pred_normal = 2 * pred_normal[gt_mask] - 1 # [B, 3]
lambda_normal = self.opt.lambda_normal * min(1, self.global_step / self.opt.iters)
loss = loss + lambda_normal * (1 - F.cosine_similarity(valid_pred_normal, valid_gt_normal).mean())
# relative depth loss
if self.opt.lambda_depth > 0:
valid_gt_depth = gt_depth[gt_mask] # [B,]
valid_pred_depth = pred_depth[:, 0][gt_mask] # [B,]
lambda_depth = self.opt.lambda_depth * min(1, self.global_step / self.opt.iters)
loss = loss + lambda_depth * (1 - self.pearson(valid_pred_depth, valid_gt_depth))
# # scale-invariant
# with torch.no_grad():
# A = torch.cat([valid_gt_depth, torch.ones_like(valid_gt_depth)], dim=-1) # [B, 2]
# X = torch.linalg.lstsq(A, valid_pred_depth).solution # [2, 1]
# valid_gt_depth = A @ X # [B, 1]
# lambda_depth = self.opt.lambda_depth #* min(1, self.global_step / self.opt.iters)
# loss = loss + lambda_depth * F.mse_loss(valid_pred_depth, valid_gt_depth)
# novel view loss
else:
loss = 0
if 'SD' in self.guidance:
# interpolate text_z
azimuth = data['azimuth'] # [-180, 180]
# ENHANCE: remove loop to handle batch size > 1
text_z = [self.embeddings['SD']['uncond']] * azimuth.shape[0]
if self.opt.perpneg:
text_z_comp, weights = adjust_text_embeddings(self.embeddings['SD'], azimuth, self.opt)
text_z.append(text_z_comp)
else:
for b in range(azimuth.shape[0]):
if azimuth[b] >= -90 and azimuth[b] < 90:
if azimuth[b] >= 0:
r = 1 - azimuth[b] / 90
else:
r = 1 + azimuth[b] / 90
start_z = self.embeddings['SD']['front']
end_z = self.embeddings['SD']['side']
else:
if azimuth[b] >= 0:
r = 1 - (azimuth[b] - 90) / 90
else:
r = 1 + (azimuth[b] + 90) / 90
start_z = self.embeddings['SD']['side']
end_z = self.embeddings['SD']['back']
text_z.append(r * start_z + (1 - r) * end_z)
text_z = torch.cat(text_z, dim=0)
if self.opt.perpneg:
loss = loss + self.guidance['SD'].train_step_perpneg(text_z, weights, pred_rgb, as_latent=as_latent, guidance_scale=self.opt.guidance_scale, grad_scale=self.opt.lambda_guidance,
save_guidance_path=save_guidance_path)
else:
loss = loss + self.guidance['SD'].train_step(text_z, pred_rgb, as_latent=as_latent, guidance_scale=self.opt.guidance_scale, grad_scale=self.opt.lambda_guidance,
save_guidance_path=save_guidance_path)
if 'IF' in self.guidance:
# interpolate text_z
azimuth = data['azimuth'] # [-180, 180]
# ENHANCE: remove loop to handle batch size > 1
text_z = [self.embeddings['IF']['uncond']] * azimuth.shape[0]
if self.opt.perpneg:
text_z_comp, weights = adjust_text_embeddings(self.embeddings['IF'], azimuth, self.opt)
text_z.append(text_z_comp)
else:
for b in range(azimuth.shape[0]):
if azimuth[b] >= -90 and azimuth[b] < 90:
if azimuth[b] >= 0:
r = 1 - azimuth[b] / 90
else:
r = 1 + azimuth[b] / 90
start_z = self.embeddings['IF']['front']
end_z = self.embeddings['IF']['side']
else:
if azimuth[b] >= 0:
r = 1 - (azimuth[b] - 90) / 90
else:
r = 1 + (azimuth[b] + 90) / 90
start_z = self.embeddings['IF']['side']
end_z = self.embeddings['IF']['back']
text_z.append(r * start_z + (1 - r) * end_z)
text_z = torch.cat(text_z, dim=0)
if self.opt.perpneg:
loss = loss + self.guidance['IF'].train_step_perpneg(text_z, weights, pred_rgb, guidance_scale=self.opt.guidance_scale, grad_scale=self.opt.lambda_guidance)
else:
loss = loss + self.guidance['IF'].train_step(text_z, pred_rgb, guidance_scale=self.opt.guidance_scale, grad_scale=self.opt.lambda_guidance)
if 'zero123' in self.guidance:
polar = data['polar']
azimuth = data['azimuth']
radius = data['radius']
loss = loss + self.guidance['zero123'].train_step(self.embeddings['zero123']['default'], pred_rgb, polar, azimuth, radius, guidance_scale=self.opt.guidance_scale,
as_latent=as_latent, grad_scale=self.opt.lambda_guidance, save_guidance_path=save_guidance_path)
if 'clip' in self.guidance:
# empirical, far view should apply smaller CLIP loss
lambda_guidance = 10 * (1 - abs(azimuth) / 180) * self.opt.lambda_guidance
loss = loss + self.guidance['clip'].train_step(self.embeddings['clip'], pred_rgb, grad_scale=lambda_guidance)
# regularizations
if not self.opt.dmtet:
if self.opt.lambda_opacity > 0:
loss_opacity = (outputs['weights_sum'] ** 2).mean()
loss = loss + self.opt.lambda_opacity * loss_opacity
if self.opt.lambda_entropy > 0:
alphas = outputs['weights'].clamp(1e-5, 1 - 1e-5)
# alphas = alphas ** 2 # skewed entropy, favors 0 over 1
loss_entropy = (- alphas * torch.log2(alphas) - (1 - alphas) * torch.log2(1 - alphas)).mean()
lambda_entropy = self.opt.lambda_entropy * min(1, 2 * self.global_step / self.opt.iters)
loss = loss + lambda_entropy * loss_entropy
if self.opt.lambda_2d_normal_smooth > 0 and 'normal_image' in outputs:
# pred_vals = outputs['normal_image'].reshape(B, H, W, 3).permute(0, 3, 1, 2).contiguous()
# smoothed_vals = TF.gaussian_blur(pred_vals.detach(), kernel_size=9)
# loss_smooth = F.mse_loss(pred_vals, smoothed_vals)
# total-variation
loss_smooth = (pred_normal[:, 1:, :, :] - pred_normal[:, :-1, :, :]).square().mean() + \
(pred_normal[:, :, 1:, :] - pred_normal[:, :, :-1, :]).square().mean()
loss = loss + self.opt.lambda_2d_normal_smooth * loss_smooth
if self.opt.lambda_orient > 0 and 'loss_orient' in outputs:
loss_orient = outputs['loss_orient']
loss = loss + self.opt.lambda_orient * loss_orient
if self.opt.lambda_3d_normal_smooth > 0 and 'loss_normal_perturb' in outputs:
loss_normal_perturb = outputs['loss_normal_perturb']
loss = loss + self.opt.lambda_3d_normal_smooth * loss_normal_perturb
else:
if self.opt.lambda_mesh_normal > 0:
loss = loss + self.opt.lambda_mesh_normal * outputs['normal_loss']
if self.opt.lambda_mesh_laplacian > 0:
loss = loss + self.opt.lambda_mesh_laplacian * outputs['lap_loss']
return pred_rgb, pred_depth, loss
def post_train_step(self):
# unscale grad before modifying it!
# ref: https://pytorch.org/docs/stable/notes/amp_examples.html#gradient-clipping
self.scaler.unscale_(self.optimizer)
# clip grad
if self.opt.grad_clip >= 0:
torch.nn.utils.clip_grad_value_(self.model.parameters(), self.opt.grad_clip)
if not self.opt.dmtet and self.opt.backbone == 'grid':
if self.opt.lambda_tv > 0:
lambda_tv = min(1.0, self.global_step / (0.5 * self.opt.iters)) * self.opt.lambda_tv
self.model.encoder.grad_total_variation(lambda_tv, None, self.model.bound)
if self.opt.lambda_wd > 0:
self.model.encoder.grad_weight_decay(self.opt.lambda_wd)
def eval_step(self, data):
rays_o = data['rays_o'] # [B, N, 3]
rays_d = data['rays_d'] # [B, N, 3]
mvp = data['mvp']
B, N = rays_o.shape[:2]
H, W = data['H'], data['W']
shading = data['shading'] if 'shading' in data else 'albedo'
ambient_ratio = data['ambient_ratio'] if 'ambient_ratio' in data else 1.0
light_d = data['light_d'] if 'light_d' in data else None
outputs = self.model.render(rays_o, rays_d, mvp, H, W, staged=True, perturb=False, bg_color=None, light_d=light_d, ambient_ratio=ambient_ratio, shading=shading)
pred_rgb = outputs['image'].reshape(B, H, W, 3)
pred_depth = outputs['depth'].reshape(B, H, W)
# dummy
loss = torch.zeros([1], device=pred_rgb.device, dtype=pred_rgb.dtype)
return pred_rgb, pred_depth, loss
def test_step(self, data, bg_color=None, perturb=False):
rays_o = data['rays_o'] # [B, N, 3]
rays_d = data['rays_d'] # [B, N, 3]
mvp = data['mvp']
B, N = rays_o.shape[:2]
H, W = data['H'], data['W']
if bg_color is not None:
bg_color = bg_color.to(rays_o.device)
shading = data['shading'] if 'shading' in data else 'albedo'
ambient_ratio = data['ambient_ratio'] if 'ambient_ratio' in data else 1.0
light_d = data['light_d'] if 'light_d' in data else None
outputs = self.model.render(rays_o, rays_d, mvp, H, W, staged=True, perturb=perturb, light_d=light_d, ambient_ratio=ambient_ratio, shading=shading, bg_color=bg_color)
pred_rgb = outputs['image'].reshape(B, H, W, 3)
pred_depth = outputs['depth'].reshape(B, H, W)
return pred_rgb, pred_depth, None
def save_mesh(self, loader=None, save_path=None):
if save_path is None:
save_path = os.path.join(self.workspace, 'mesh')
self.log(f"==> Saving mesh to {save_path}")
os.makedirs(save_path, exist_ok=True)
self.model.export_mesh(save_path, resolution=self.opt.mcubes_resolution, decimate_target=self.opt.decimate_target)
self.log(f"==> Finished saving mesh.")
### ------------------------------
def train(self, train_loader, valid_loader, test_loader, max_epochs):
if self.use_tensorboardX and self.local_rank == 0:
self.writer = tensorboardX.SummaryWriter(os.path.join(self.workspace, "run", self.name))
start_t = time.time()
for epoch in range(self.epoch + 1, max_epochs + 1):
self.epoch = epoch
self.train_one_epoch(train_loader, max_epochs)
if self.workspace is not None and self.local_rank == 0:
self.save_checkpoint(full=True, best=False)
if self.epoch % self.opt.eval_interval == 0:
self.evaluate_one_epoch(valid_loader)
self.save_checkpoint(full=False, best=True)
if self.epoch % self.opt.test_interval == 0 or self.epoch == max_epochs:
self.test(test_loader)
end_t = time.time()
self.total_train_t = end_t - start_t + self.total_train_t
self.log(f"[INFO] training takes {(self.total_train_t)/ 60:.4f} minutes.")
if self.use_tensorboardX and self.local_rank == 0:
self.writer.close()
def evaluate(self, loader, name=None):
self.use_tensorboardX, use_tensorboardX = False, self.use_tensorboardX
self.evaluate_one_epoch(loader, name)
self.use_tensorboardX = use_tensorboardX
def test(self, loader, save_path=None, name=None, write_video=True):
if save_path is None:
save_path = os.path.join(self.workspace, 'results')
if name is None:
name = f'{self.name}_ep{self.epoch:04d}'
os.makedirs(save_path, exist_ok=True)
self.log(f"==> Start Test, save results to {save_path}")
pbar = tqdm.tqdm(total=len(loader) * loader.batch_size, bar_format='{percentage:3.0f}% {n_fmt}/{total_fmt} [{elapsed}<{remaining}, {rate_fmt}]')
self.model.eval()
if write_video:
all_preds = []
all_preds_depth = []
with torch.no_grad():
for i, data in enumerate(loader):
with torch.cuda.amp.autocast(enabled=self.fp16):
preds, preds_depth, _ = self.test_step(data)
pred = preds[0].detach().cpu().numpy()
pred = (pred * 255).astype(np.uint8)
pred_depth = preds_depth[0].detach().cpu().numpy()
pred_depth = (pred_depth - pred_depth.min()) / (pred_depth.max() - pred_depth.min() + 1e-6)
pred_depth = (pred_depth * 255).astype(np.uint8)
if write_video:
all_preds.append(pred)
all_preds_depth.append(pred_depth)
else:
cv2.imwrite(os.path.join(save_path, f'{name}_{i:04d}_rgb.png'), cv2.cvtColor(pred, cv2.COLOR_RGB2BGR))
cv2.imwrite(os.path.join(save_path, f'{name}_{i:04d}_depth.png'), pred_depth)
pbar.update(loader.batch_size)
if write_video:
all_preds = np.stack(all_preds, axis=0)
all_preds_depth = np.stack(all_preds_depth, axis=0)
imageio.mimwrite(os.path.join(save_path, f'{name}_rgb.mp4'), all_preds, fps=25, quality=8, macro_block_size=1)
imageio.mimwrite(os.path.join(save_path, f'{name}_depth.mp4'), all_preds_depth, fps=25, quality=8, macro_block_size=1)
self.log(f"==> Finished Test.")
# [GUI] train text step.
def train_gui(self, train_loader, step=16):
self.model.train()
total_loss = torch.tensor([0], dtype=torch.float32, device=self.device)
loader = iter(train_loader)
for _ in range(step):
# mimic an infinite loop dataloader (in case the total dataset is smaller than step)
try:
data = next(loader)
except StopIteration:
loader = iter(train_loader)
data = next(loader)
# update grid every 16 steps
if self.model.cuda_ray and self.global_step % self.opt.update_extra_interval == 0:
with torch.cuda.amp.autocast(enabled=self.fp16):
self.model.update_extra_state()
self.global_step += 1
self.optimizer.zero_grad()
with torch.cuda.amp.autocast(enabled=self.fp16):
pred_rgbs, pred_depths, loss = self.train_step(data)
self.scaler.scale(loss).backward()
self.post_train_step()
self.scaler.step(self.optimizer)
self.scaler.update()
if self.scheduler_update_every_step:
self.lr_scheduler.step()
total_loss += loss.detach()
if self.ema is not None:
self.ema.update()
average_loss = total_loss.item() / step
if not self.scheduler_update_every_step:
if isinstance(self.lr_scheduler, torch.optim.lr_scheduler.ReduceLROnPlateau):
self.lr_scheduler.step(average_loss)
else:
self.lr_scheduler.step()
outputs = {
'loss': average_loss,
'lr': self.optimizer.param_groups[0]['lr'],
}
return outputs
# [GUI] test on a single image
def test_gui(self, pose, intrinsics, mvp, W, H, bg_color=None, spp=1, downscale=1, light_d=None, ambient_ratio=1.0, shading='albedo'):
# render resolution (may need downscale to for better frame rate)
rH = int(H * downscale)
rW = int(W * downscale)
intrinsics = intrinsics * downscale
pose = torch.from_numpy(pose).unsqueeze(0).to(self.device)
mvp = torch.from_numpy(mvp).unsqueeze(0).to(self.device)
rays = get_rays(pose, intrinsics, rH, rW, -1)
# from degree theta/phi to 3D normalized vec
light_d = np.deg2rad(light_d)
light_d = np.array([
np.sin(light_d[0]) * np.sin(light_d[1]),
np.cos(light_d[0]),
np.sin(light_d[0]) * np.cos(light_d[1]),
], dtype=np.float32)
light_d = torch.from_numpy(light_d).to(self.device)
data = {
'rays_o': rays['rays_o'],
'rays_d': rays['rays_d'],
'mvp': mvp,
'H': rH,
'W': rW,
'light_d': light_d,
'ambient_ratio': ambient_ratio,
'shading': shading,
}
self.model.eval()
if self.ema is not None:
self.ema.store()
self.ema.copy_to()
with torch.no_grad():
with torch.cuda.amp.autocast(enabled=self.fp16):
# here spp is used as perturb random seed!
preds, preds_depth, _ = self.test_step(data, bg_color=bg_color, perturb=False if spp == 1 else spp)
if self.ema is not None:
self.ema.restore()
# interpolation to the original resolution
if downscale != 1:
# have to permute twice with torch...
preds = F.interpolate(preds.permute(0, 3, 1, 2), size=(H, W), mode='nearest').permute(0, 2, 3, 1).contiguous()
preds_depth = F.interpolate(preds_depth.unsqueeze(1), size=(H, W), mode='nearest').squeeze(1)