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app.py
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2299 lines (2210 loc) · 118 KB
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"""This is the main app entrypoint for training and inference."""
import dataclasses
import itertools
import os
import random
import shutil
import subprocess
import time
import json
import copy
from typing import Optional, Union, Tuple, Sequence, Dict, Any
import numpy as np
import open3d as o3d
import torch
import torch.nn.functional as F
import tqdm
from torch import nn
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
import options
from data_readers.holostudio_reader import HolostudioDataset
from data_readers.video_reader import VideoReader
from data_readers import holostudio_reader_utils
from data_readers.nerf_reader import NerfSyntheticDataset
from models import adaptive_mlp
from models import adaptive_resnet
from models import mlp
from models import subnet
from models import video_layers
from models import wrapper_models
from models import mipnet
from models import smipnet
from protos_compiled import model_pb2
from utils import my_torch_utils
from utils import my_utils
from utils import nerf_utils
from utils import torch_checkpoint_manager
from utils import pytorch_psnr
from utils import pytorch_ssim
from options import (ADAPTIVE_MLP, MLP, ADAPTIVE_RESNET, LEARNED_RAY,
SUBNET, MIPNET, SMIPNET)
ADAPTIVE_NETWORKS = {ADAPTIVE_RESNET, SUBNET, MIPNET, SMIPNET}
class CheckpointKeys:
MODEL_STATE_DICT = "model_state_dict"
LATENT_CODE_STATE_DICT = "latent_code_state_dict"
OPTIMIZER_STATE_DICT = "optimizer_state_dict"
LATENT_OPTIMIZER_STATE_DICT = "latent_optimizer_state_dict"
AUX_MODEL_STATE_DICT = "aux_model_state_dict"
IMPORTANCE_MODEL_STATE_DICT = "importance_model_state_dict"
@dataclasses.dataclass
class InferenceOutputs:
model_output: Union[torch.Tensor,
adaptive_mlp.AdaptiveMLPOutputs,
adaptive_resnet.AdaptiveResnetOutputs,
subnet.SubNetOutputs]
loss: torch.Tensor
aux_output: Optional[torch.Tensor] = None
color_loss: Optional[torch.Tensor] = None
efficiency_loss: Optional[torch.Tensor] = None
importance_loss: Optional[torch.Tensor] = None
loadbalance_loss: Optional[torch.Tensor] = None
clustering_loss: Optional[torch.Tensor] = None
mm_importance_loss: Optional[torch.Tensor] = None
aux_loss: Optional[torch.Tensor] = None
ray_colors: Optional[torch.Tensor] = None
aux_discarding_keep: Optional[torch.Tensor] = None
@dataclasses.dataclass
class RenderFullImageOutputs:
full_image: torch.Tensor
aux_image: Optional[torch.Tensor] = None
expected_layers_image: Optional[torch.Tensor] = None
layer_outputs: Optional[torch.Tensor] = None
uvmap_outputs: Optional[torch.Tensor] = None
class App:
def __init__(self):
self.args = args = options.parse_options()
random.seed(args.random_seed)
np.random.seed(args.random_seed)
torch.manual_seed(args.random_seed)
self.device = my_torch_utils.find_torch_device(self.args.device)
self.rng = np.random.default_rng(args.random_seed)
if args.script_mode not in (options.GET_TRAIN_TIME,):
self.train_data, self.val_data, self.test_data = self.setup_datasets()
self.model, self.latent_code_layer, self.importance_model, self.aux_model = self.setup_model()
(self.checkpoint_manager, self.writer, self.optimizer, self.latent_optimizer,
self.lr_scheduler) = self.setup_checkpoints()
def start(self):
"""Start the program"""
args = self.args
try:
if args.script_mode == options.TRAIN:
self.run_training_loop()
elif args.script_mode == options.INFERENCE:
self.run_inference()
elif args.script_mode == options.VISUALIZE_CAMERAS:
self.visualize_cameras()
elif args.script_mode == options.SAVE_PROTO:
self.run_save_proto()
elif args.script_mode == options.BENCHMARK:
self.run_benchmark()
elif args.script_mode == options.VIEWER:
self.run_viewer()
elif args.script_mode == options.DEBUG:
self.run_debug()
elif args.script_mode in (options.EVAL, options.EVAL_MULTIRES,
options.EVAL_MEMORIZATION,
options.EVAL_MEMORIZATION_MULTIRES):
self.run_eval()
elif args.script_mode == options.GET_TRAIN_TIME:
self.run_get_train_time()
elif args.script_mode == options.RENDER_OCCUPANCY_MAP:
self.run_render_occupancy_map()
elif args.script_mode == options.RENDER_TRANSITION:
self.run_render_transition()
elif args.script_mode == options.RENDER_FOVEATION:
self.run_render_foveation()
else:
raise ValueError("Unknown script mode")
except KeyboardInterrupt:
pass
finally:
if hasattr(self, "writer") and self.writer is not None:
self.writer.close()
print("Done")
def setup_datasets(self):
args = self.args
if args.dataset == options.VIDEO:
raise NotImplementedError("Not implemented")
elif args.dataset == options.HOLOSTUDIO:
train_data = HolostudioDataset(
args.dataset_path, args.dataset_resize_factor,
max_frames=args.dataset_max_frames,
load_every_nth_view=args.dataset_loadeverynthview,
renderposes_height=args.dataset_render_poses_height,
mip_factors=args.dataset_mip_factors or (),
resize_interpolation=args.dataset_interp_mode,
dropout_poses=(args.dataset_ignore_poses or []) +
(args.dataset_val_poses or []) +
(args.dataset_test_poses or []),
renderposes_centeroffset=args.dataset_render_poses_centeroffset,
cache_lowres=args.cache_lowres
)
val_data = None
test_data = None
if args.dataset_val_poses:
val_data = HolostudioDataset(
args.dataset_path, args.dataset_resize_factor,
max_frames=args.dataset_max_frames,
renderposes_height=args.dataset_render_poses_height,
mip_factors=args.dataset_mip_factors or (),
resize_interpolation=args.dataset_interp_mode,
dropout_poses=(args.dataset_val_poses or []),
include_dropout_only=True,
renderposes_centeroffset=args.dataset_render_poses_centeroffset)
if args.dataset_test_poses:
test_data = HolostudioDataset(
args.dataset_path, args.dataset_resize_factor,
max_frames=args.dataset_max_frames,
renderposes_height=args.dataset_render_poses_height,
mip_factors=args.dataset_mip_factors or (),
resize_interpolation=args.dataset_interp_mode,
dropout_poses=(args.dataset_test_poses or []),
include_dropout_only=True,
renderposes_centeroffset=args.dataset_render_poses_centeroffset)
elif args.dataset == options.NERFSYNTHETIC:
train_data = NerfSyntheticDataset(
args.dataset_path, args.dataset_resize_factor,
max_frames=args.dataset_max_frames,
renderposes_height=args.dataset_render_poses_height,
mip_factors=args.dataset_mip_factors or (),
resize_interpolation=args.dataset_interp_mode,
dropout_poses=(args.dataset_ignore_poses or []) +
(args.dataset_val_poses or []) +
(args.dataset_test_poses or []))
val_data = None
test_data = None
if args.dataset_val_poses:
val_data = NerfSyntheticDataset(
args.dataset_path, args.dataset_resize_factor,
max_frames=args.dataset_max_frames,
renderposes_height=args.dataset_render_poses_height,
mip_factors=args.dataset_mip_factors or (),
resize_interpolation=args.dataset_interp_mode,
dropout_poses=(args.dataset_val_poses or []),
include_dropout_only=True)
if args.dataset_test_poses:
test_data = NerfSyntheticDataset(
args.dataset_path, args.dataset_resize_factor,
max_frames=args.dataset_max_frames,
renderposes_height=args.dataset_render_poses_height,
mip_factors=args.dataset_mip_factors or (),
resize_interpolation=args.dataset_interp_mode,
dropout_poses=(args.dataset_test_poses or []),
include_dropout_only=True)
else:
raise ValueError(f"Unknown dataset {args.dataset}")
return train_data, val_data, test_data
def setup_model(self) -> Tuple[nn.Module, nn.Module, Optional[nn.Module], Optional[nn.Module]]:
args = self.args
base_feature_size = 6
latent_code_dim = args.latent_code_dim if args.num_latent_codes >= 0 else 1
in_features = (base_feature_size + 2 * base_feature_size * args.positional_encoding_functions +
latent_code_dim)
out_features = 3 if not args.predict_alpha else 4
if args.model == ADAPTIVE_MLP:
model = adaptive_mlp.AdaptiveMLP(in_features=in_features,
out_features=out_features,
layers=args.model_layers,
hidden_features=args.model_width).to(self.device)
elif args.model == MLP:
model = mlp.MLP(in_features=in_features,
out_features=out_features,
layers=args.model_layers,
hidden_features=args.model_width,
use_layernorm=args.model_use_layernorm).to(self.device)
elif args.model == ADAPTIVE_RESNET:
model = adaptive_resnet.AdaptiveResnet(
in_features=in_features,
out_features=out_features,
output_every=args.model_output_every,
layers=args.model_layers,
hidden_features=args.model_width,
use_layernorm=args.model_use_layernorm
).to(self.device)
elif args.model == SUBNET:
subnet_factors = [
(x[0], x[1]) for x in args.subnet_factors] if args.subnet_factors else None
if args.subnet_interleave:
raise ValueError("Interleave deprecated")
model = subnet.SubNet(
in_features=in_features,
out_features=out_features,
layers=args.model_layers,
hidden_features=args.model_width,
factors=subnet_factors
).to(self.device)
elif args.model == MIPNET:
subnet_factors = [
(x[0], x[1]) for x in args.subnet_factors] if args.subnet_factors else None
model = mipnet.MipNet(
in_features=in_features,
out_features=out_features,
layers=args.model_layers,
hidden_features=args.model_width,
factors=subnet_factors,
share_gradients=args.mipnet_share_gradients
).to(self.device)
elif args.model == SMIPNET:
subnet_factors = [
(x[0], x[1]) for x in args.subnet_factors] if args.subnet_factors else None
model = smipnet.SMipNet(
in_features=in_features,
out_features=out_features,
layers=args.model_layers,
hidden_features=args.model_width,
factors=subnet_factors
).to(self.device)
else:
raise ValueError(f"Unknown model {args.model}")
aux_model = None
if args.use_aux_network:
aux_out_features = 2 if args.aux_encode_saliency else 1
aux_model = mlp.MLP(in_features=in_features,
out_features=aux_out_features,
layers=args.aux_layers,
hidden_features=args.aux_features,
use_layernorm=args.aux_layernorm).to(self.device)
importance_model = None
if args.use_importance_training:
importance_model = mlp.MLP(in_features=in_features,
out_features=1,
layers=args.importance_layers,
hidden_features=args.importance_features).to(self.device)
latent_code_layer = torch.nn.Identity()
if args.use_latent_codes:
num_latent_codes = len(self.train_data)
if args.num_latent_codes >= 0:
num_latent_codes = args.num_latent_codes
else:
num_latent_codes = num_latent_codes // np.abs(
args.num_latent_codes)
latent_code_layer = video_layers.LatentCodeLayer(
num_latent_codes,
len(self.train_data) - 1,
args.latent_code_dim).to(self.device)
return model, latent_code_layer, importance_model, aux_model
def setup_checkpoints(self):
args = self.args
checkpoint_manager = torch_checkpoint_manager.CheckpointManager(
args.checkpoints_dir,
max_to_keep=args.checkpoint_count)
writer = SummaryWriter(log_dir=os.path.join(
args.checkpoints_dir, "logs"))
model_parameters = list(self.model.parameters())
if args.use_importance_training:
model_parameters.extend(self.importance_model.parameters())
if args.use_aux_network:
model_parameters.extend(self.aux_model.parameters())
optimizer = torch.optim.Adam(model_parameters, lr=args.learning_rate)
latent_optimizer = None
if args.use_latent_codes:
latent_optimizer = torch.optim.Adam(
self.latent_code_layer.parameters(), lr=args.latent_learning_rate)
latest_checkpoint = checkpoint_manager.load_latest_checkpoint()
lr_scheduler = (torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=args.lr_schedule_gamma)
if args.lr_schedule_gamma > 0.0 else None)
if latest_checkpoint:
self.model.load_state_dict(
latest_checkpoint[CheckpointKeys.MODEL_STATE_DICT])
self.latent_code_layer.load_state_dict(
latest_checkpoint[CheckpointKeys.LATENT_CODE_STATE_DICT])
optimizer.load_state_dict(
latest_checkpoint[CheckpointKeys.OPTIMIZER_STATE_DICT])
if args.use_latent_codes:
latent_optimizer.load_state_dict(
latest_checkpoint[CheckpointKeys.LATENT_OPTIMIZER_STATE_DICT])
if self.importance_model:
self.importance_model.load_state_dict(
latest_checkpoint[CheckpointKeys.IMPORTANCE_MODEL_STATE_DICT])
if self.aux_model:
self.aux_model.load_state_dict(
latest_checkpoint[CheckpointKeys.AUX_MODEL_STATE_DICT])
return checkpoint_manager, writer, optimizer, latent_optimizer, lr_scheduler
def get_importance_map(self, height: int, width: int, focal: float, pose: torch.Tensor, inference_height: int = 64,
t: float = 0.0) -> torch.Tensor:
"""Performs inference on the importance map network to yield an importance map.
Args:
height: Final height of the importance map.
width: Final width of the importance map.
focal: Focal of the importance map.
pose: Target pose.
inference_height: Internal height.
t: Time.
Returns:
The importance map.
"""
args = self.args
if isinstance(self.importance_model, torch.nn.Module):
inference_width = int(round(inference_height * (width / height)))
my_focal = focal * (inference_height / height)
ray_origins, ray_directions = nerf_utils.get_ray_bundle(
inference_height, inference_width, my_focal, pose)
if args.model == LEARNED_RAY:
rays_plucker = self.model.ray_to_indices(
ray_origins, ray_directions)
else:
rays_plucker = my_torch_utils.convert_rays_to_plucker(
ray_origins, ray_directions)
rays_with_t = torch.cat(
(rays_plucker, t * torch.ones_like(rays_plucker[:, :, :1])), dim=-1).reshape(-1, 7)
rays_positional_encoding = nerf_utils.add_positional_encoding(
rays_with_t[:, :-1],
num_encoding_functions=args.positional_encoding_functions)
rays_with_positional = torch.cat(
(rays_positional_encoding, rays_with_t[:, -1:]), dim=-1)
if args.use_latent_codes:
features = self.latent_code_layer(rays_with_positional)
else:
features = rays_with_positional
importance_map_lowres = self.importance_model(
features).reshape(inference_height, inference_width)
importance_map = F.interpolate(importance_map_lowres[None, None], (height, width), mode="bilinear",
align_corners=False)
raw_importance_map = torch.sigmoid(
importance_map[0, 0, :, :, None].reshape(-1))
return my_utils.lerp(1.0, raw_importance_map, args.importance_lerp)
else:
raise ValueError("Unrecognized type of importance map.")
def render_full_image(self, batch_size: int, rays: torch.Tensor,
reference_image: Optional[torch.Tensor] = None,
early_stopping: bool = False,
lods: Optional[Sequence[int]] = None,
aux_discarding: bool = False,
extra_options: Dict[Any, Any] = None) -> RenderFullImageOutputs:
"""Renders a full image.
Args:
batch_size: Batch size to use.
rays: Full set of rays to use.
reference_image: Reference image.
early_stopping: Use early stopping.
lods: level of detail.
aux_discarding: Discard pixels using aux network.
extra_options: Extra options.
Returns:
The final render output.
"""
height, width, n_features = rays.shape
iterations = int(np.ceil(height * width / batch_size))
all_ray_outputs = []
expected_layers_outputs = []
layer_outputs = []
uvmap_outputs = []
aux_outputs = []
iterable = range(iterations) if iterations <= 1 else tqdm.tqdm(
range(iterations), leave=False)
for i in iterable:
ray_subset = rays.reshape((height * width, n_features))[
(i * batch_size):(
(i + 1) * batch_size)]
reference_image_subset = None
if reference_image is not None:
reference_image_subset = reference_image.reshape(
(height * width, 3))[(i * batch_size):((i + 1) * batch_size)]
run_outputs = self.do_inference(ray_subset,
early_stopping=early_stopping,
ray_colors=reference_image_subset,
lods=lods,
aux_discarding=aux_discarding,
extra_options=extra_options)
model_output = run_outputs.model_output
if isinstance(model_output, adaptive_mlp.AdaptiveMLPOutputs):
all_ray_outputs.append(
model_output.expected_output)
expected_layers_outputs.append(
model_output.expected_layers)
if model_output.layer_outputs is not None:
layer_outputs.append(
model_output.layer_outputs)
elif (isinstance(model_output, adaptive_resnet.AdaptiveResnetOutputs) or
isinstance(model_output, subnet.SubNetOutputs) or
isinstance(model_output, mipnet.MipNetOutputs) or
isinstance(model_output, smipnet.SMipNetOutputs)):
if aux_discarding:
output_tensor = model_output.outputs
padded_model_output = torch.zeros(
(ray_subset.shape[0], output_tensor.shape[1],
output_tensor.shape[2]),
device=output_tensor.device,
dtype=output_tensor.dtype)
padded_model_output[run_outputs.aux_discarding_keep] = output_tensor
all_ray_outputs.append(padded_model_output[:, -1])
layer_outputs.append(padded_model_output)
else:
all_ray_outputs.append(model_output.outputs[:, -1])
layer_outputs.append(model_output.outputs)
elif isinstance(model_output, torch.Tensor):
if aux_discarding:
padded_model_output = torch.zeros((ray_subset.shape[0], model_output.shape[1]),
device=model_output.device,
dtype=model_output.dtype)
padded_model_output[run_outputs.aux_discarding_keep] = model_output
all_ray_outputs.append(padded_model_output)
else:
all_ray_outputs.append(model_output)
else:
raise ValueError(f"Unknown type {type(model_output)}")
if run_outputs.aux_output is not None:
aux_outputs.append(run_outputs.aux_output)
all_ray_outputs = all_ray_outputs[0] if len(
all_ray_outputs) == 1 else torch.cat(all_ray_outputs, dim=0)
all_ray_outputs = all_ray_outputs.reshape(
(height, width, 3 if not self.args.predict_alpha else 4))
outputs = RenderFullImageOutputs(all_ray_outputs)
if expected_layers_outputs:
outputs.expected_layers_image = torch.cat(
expected_layers_outputs, dim=0).reshape(height, width, 1)
if layer_outputs:
outputs.layer_outputs = layer_outputs[0] if len(
layer_outputs) == 1 else torch.cat(layer_outputs, dim=0)
outputs.layer_outputs = outputs.layer_outputs.permute((1, 0, 2)).reshape(-1, height, width,
self.model.out_features)
if uvmap_outputs:
outputs.uvmap_outputs = torch.cat(uvmap_outputs, dim=0).reshape(
height, width, uvmap_outputs[0].shape[-1])
if aux_outputs:
outputs.aux_image = torch.cat(
aux_outputs, dim=0).reshape(height, width, -1)
if self.args.render_truncate_alpha > 0.0:
truncate_val = self.args.render_truncate_alpha
truncate_tensor = torch.tensor(
(False, False, False, True), dtype=torch.bool, device=self.device)
tf_mask = (0.0 * outputs.full_image).bool()
tf_mask[outputs.full_image[:, :, 3]
< truncate_val] = truncate_tensor
outputs.full_image[tf_mask] = truncate_val * \
torch.pow(outputs.full_image[tf_mask] / truncate_val, 3.0)
if layer_outputs:
tf_mask = (0.0 * outputs.layer_outputs).bool()
tf_mask[outputs.layer_outputs[:, :, :, 3]
< truncate_val] = truncate_tensor
outputs.layer_outputs[tf_mask] = (
truncate_val * torch.pow(outputs.layer_outputs[tf_mask] / truncate_val, 3.0))
return outputs
def render_full_image_multilod(self, batch_size: int, rays: torch.Tensor,
lodmap: torch.Tensor = None,
reference_image: Optional[torch.Tensor] = None,
early_stopping: bool = False,
aux_discarding: bool = False,
extra_options: Dict[Any, Any] = None) -> RenderFullImageOutputs:
"""Renders a full image with per-pixel LoD.
Args:
batch_size: Batch size to use.
lodmap: Level of detail per pixel.
rays: Full set of rays to use.
reference_image: Reference image.
early_stopping: Use early stopping.
aux_discarding: Discard pixels using aux network.
extra_options: Extra options.
Returns:
The final render output.
"""
height, width, n_features = rays.shape
iterations = int(np.ceil(height * width / batch_size))
all_ray_outputs = []
expected_layers_outputs = []
layer_outputs = []
uvmap_outputs = []
aux_outputs = []
iterable = range(iterations) if iterations <= 1 else tqdm.tqdm(
range(iterations), leave=False)
for i in iterable:
ray_subset = rays.reshape((height * width, n_features))[
(i * batch_size):(
(i + 1) * batch_size)]
lodmap_subset = lodmap.reshape(
(height * width))[(i * batch_size):((i + 1) * batch_size)]
reference_image_subset = None
if reference_image is not None:
reference_image_subset = reference_image.reshape(
(height * width, 3))[(i * batch_size):((i + 1) * batch_size)]
unique_lods = torch.unique(lodmap_subset).tolist()
lod_to_run_output = {}
model_output = None
run_outputs = None
for lod in unique_lods:
run_outputs = self.do_inference(ray_subset[lodmap_subset == lod],
early_stopping=early_stopping,
ray_colors=reference_image_subset,
lods=[lod],
aux_discarding=aux_discarding,
extra_options=extra_options)
model_output = run_outputs.model_output
lod_to_run_output[lod] = run_outputs
if (isinstance(model_output, adaptive_resnet.AdaptiveResnetOutputs) or
isinstance(model_output, subnet.SubNetOutputs) or
isinstance(model_output, mipnet.MipNetOutputs) or
isinstance(model_output, smipnet.SMipNetOutputs)):
if aux_discarding:
padded_model_output = torch.zeros(
(ray_subset.shape[0], model_output.outputs.shape[1],
model_output.outputs.shape[2]),
device=model_output.outputs.device,
dtype=model_output.outputs.dtype)
for lod in unique_lods:
m_run_output = lod_to_run_output[lod]
m_sub = torch.eq(lodmap_subset, lod)
m_sub[m_sub.clone()] = m_run_output.aux_discarding_keep
padded_model_output[m_sub] = m_run_output.model_output.outputs
# Proper way of doing this with a view:
# padded_model_output[lodmap_subset == lod][
# m_run_output.aux_discarding_keep] = m_run_output.model_output.outputs
all_ray_outputs.append(padded_model_output[:, -1])
layer_outputs.append(padded_model_output)
else:
padded_model_output = torch.zeros(
(ray_subset.shape[0], model_output.outputs.shape[1],
model_output.outputs.shape[2]),
device=model_output.outputs.device,
dtype=model_output.outputs.dtype)
for lod in unique_lods:
m_run_output = lod_to_run_output[lod]
padded_model_output[lodmap_subset ==
lod] = m_run_output.model_output.outputs
all_ray_outputs.append(padded_model_output[:, -1])
layer_outputs.append(padded_model_output)
else:
raise ValueError(f"Unknown type {type(model_output)}")
if run_outputs.aux_output is not None:
full_aux_output = torch.zeros(
(ray_subset.shape[0], run_outputs.aux_output.shape[1]),
device=model_output.outputs.device,
dtype=model_output.outputs.dtype)
for lod in unique_lods:
m_run_output = lod_to_run_output[lod]
full_aux_output[lodmap_subset ==
lod] = m_run_output.aux_output
aux_outputs.append(full_aux_output)
all_ray_outputs = torch.cat(all_ray_outputs, dim=0).reshape(
(height, width, 3 if not self.args.predict_alpha else 4))
outputs = RenderFullImageOutputs(all_ray_outputs)
if expected_layers_outputs:
outputs.expected_layers_image = torch.cat(
expected_layers_outputs, dim=0).reshape(height, width, 1)
if layer_outputs:
outputs.layer_outputs = torch.cat(layer_outputs, dim=0).permute((1, 0, 2)).reshape(
-1,
height, width, self.model.out_features)
if uvmap_outputs:
outputs.uvmap_outputs = torch.cat(uvmap_outputs, dim=0).reshape(
height, width, uvmap_outputs[0].shape[-1])
if aux_outputs:
outputs.aux_image = torch.cat(
aux_outputs, dim=0).reshape(height, width, -1)
if self.args.render_truncate_alpha > 0.0:
truncate_val = self.args.render_truncate_alpha
truncate_tensor = torch.tensor(
(False, False, False, True), dtype=torch.bool, device=self.device)
tf_mask = (0.0 * outputs.full_image).bool()
tf_mask[outputs.full_image[:, :, 3]
< truncate_val] = truncate_tensor
outputs.full_image[tf_mask] = truncate_val * \
torch.pow(outputs.full_image[tf_mask] / truncate_val, 3.0)
if layer_outputs:
tf_mask = (0.0 * outputs.layer_outputs).bool()
tf_mask[outputs.layer_outputs[:, :, :, 3]
< truncate_val] = truncate_tensor
outputs.layer_outputs[tf_mask] = (
truncate_val * torch.pow(outputs.layer_outputs[tf_mask] / truncate_val, 3.0))
return outputs
def do_validation_run(self, step: int):
"""Do a single validation run.
Args:
step: Current step.
Returns:
None.
"""
args = self.args
writer = self.writer
device = self.device
with torch.no_grad():
if (step == 1 or
args.validation_interval == 0 or
step % args.validation_interval == 0):
self.model.eval()
batch_size = args.val_batch_size
render_poses = self.train_data.get_render_poses(
args.dataset_render_poses)
val_images = []
val_expected_layers_images = []
val_mm_selection_image = []
val_images_es = []
val_images_layers_es = []
val_layers = []
render_image_times = []
render_image_times_es = []
aux_images = []
for i in tqdm.tqdm(range(args.dataset_render_poses), desc="Val", leave=False):
t = 0.0
height = self.train_data.height
width = self.train_data.width
if self.train_data.using_processed_camera_parameters:
ray_origins, ray_directions = self.train_data.camera_params_to_rays(
render_poses["intrinsics"][i].to(self.device),
render_poses["transforms"][i].to(self.device),
height, width
)
else:
focal = self.train_data.focal
pose = render_poses[i]
ray_origins, ray_directions = nerf_utils.get_ray_bundle(height, width, focal,
pose[:3, :4].to(device))
if args.model == LEARNED_RAY:
rays_plucker = self.model.ray_to_indices(
ray_origins, ray_directions)
else:
rays_plucker = my_torch_utils.convert_rays_to_plucker(
ray_origins, ray_directions)
rays_with_t = torch.cat(
(rays_plucker, t *
torch.ones_like(rays_plucker[:, :, :1])),
dim=-1)
t0 = time.time()
rendered_image_outputs = self.render_full_image(
batch_size, rays_with_t)
render_image_times.append(time.time() - t0)
val_images.append(rendered_image_outputs.full_image.cpu())
if i == 0 and rendered_image_outputs.layer_outputs is not None:
val_layers.append(
rendered_image_outputs.layer_outputs.cpu())
if rendered_image_outputs.aux_image is not None:
aux_images.append(
rendered_image_outputs.aux_image.cpu())
if args.model == ADAPTIVE_MLP:
expected_layers_image = expected_layers_image / \
(len(self.model.model_layers) - 1)
val_expected_layers_images.append(
expected_layers_image)
t0 = time.time()
rendered_image_es_outputs = self.render_full_image(batch_size,
rays_with_t,
early_stopping=True)
render_image_times_es.append(time.time() - t0)
val_images_es.append(
rendered_image_es_outputs.full_image)
expected_layers_image_es = rendered_image_es_outputs.expected_layers_image / (
len(self.model.model_layers) - 1)
val_images_layers_es.append(expected_layers_image_es)
val_images = torch.stack(val_images)
writer.add_scalar("val/10_render_time",
np.mean(render_image_times), step)
writer.add_images("val/10_output_images", torch.clamp(val_images[:, :, :, :3], 0, 1), step,
dataformats="NHWC")
if args.predict_alpha:
writer.add_images(
"val/11_output_alpha", val_images[:, :, :, 3:4], step, dataformats="NHWC")
writer.add_video("val/30_output_video", val_images[None, :, :, :, :3].permute((0, 1, 4, 2, 3)), step,
fps=args.video_framerate)
if args.model == ADAPTIVE_MLP:
writer.add_scalar(
"val/10_render_time_earlystopping", np.mean(render_image_times_es), step)
val_expected_layers_images = torch.stack(
val_expected_layers_images)
writer.add_images("val/11_output_expected_layers_images", val_expected_layers_images, step,
dataformats="NHWC")
val_images_es = torch.stack(val_images_es)
writer.add_images(
"val/20_output_images_es", val_images_es[:, :, :, :3], step, dataformats="NHWC")
val_images_layers_es = torch.stack(val_images_layers_es)
writer.add_images("val/21_output_layers_images_es",
val_images_layers_es, step, dataformats="NHWC")
if args.model in ADAPTIVE_NETWORKS:
if val_layers:
writer.add_images("val/31_output_layers_images", torch.clamp(val_layers[0][:, :, :, :3], 0, 1),
step, dataformats="NHWC")
if aux_images:
aux_images = torch.stack(aux_images)
writer.add_images(
"val/40_aux_images", aux_images[:, :, :, :1], step, dataformats="NHWC")
if args.aux_encode_saliency:
writer.add_images(
"val/40_aux_saliency", aux_images[:, :, :, 1:2], step, dataformats="NHWC")
self.model.train()
def compute_validation_psnr(self):
"""Computes the validation PSNR.
"""
args = self.args
device = self.device
cropped_psnr_values = [[]
for _ in range(self.model.num_outputs)]
if self.val_data is None:
raise ValueError("No validation data")
with torch.no_grad():
self.model.eval()
data_loader = DataLoader(self.val_data, batch_size=1, shuffle=False,
num_workers=args.dataloader_num_workers)
for frame_num, data in enumerate(tqdm.tqdm(data_loader, desc="Validation_PSNR", leave=False)):
frame_batch_size, height, width, data_image_channels = data["image"].shape
ray_colors = data["image"].to(device)
ray_masks = data["mask"].to(
device) if "mask" in data else None
ray_t = (torch.zeros_like(
data["image"][:, :, :, 0]) + data["t"][:, None, None]).to(device)
ray_origins = []
ray_directions = []
for i in range(frame_batch_size):
if self.train_data.using_processed_camera_parameters:
transform = data["transform"][i].to(device)
intrinsics = data["intrinsics"][i].to(device)
frame_ray_origins, frame_ray_directions = self.train_data.camera_params_to_rays(
intrinsics, transform, height, width)
else:
pose_target = data["pose"][0, :3, :4]
focal = self.train_data.focal
frame_ray_origins, frame_ray_directions = nerf_utils.get_ray_bundle(height, width,
focal,
pose_target)
ray_origins.append(frame_ray_origins)
ray_directions.append(frame_ray_directions)
ray_origins = torch.cat(ray_origins)
ray_directions = torch.cat(ray_directions)
batch_size = args.val_batch_size
if args.model == LEARNED_RAY:
rays_plucker = self.model.ray_to_indices(
ray_origins, ray_directions)
else:
rays_plucker = my_torch_utils.convert_rays_to_plucker(
ray_origins, ray_directions)
rays_with_t = torch.cat(
(rays_plucker, ray_t[0, :, :, None]), dim=-1)
for lod in tqdm.tqdm(range(self.model.num_outputs), desc="LoDs", leave=False):
rendered_image_outputs = self.render_full_image(
batch_size, rays_with_t, early_stopping=True, lods=[lod])
cropped_psnr_val = pytorch_psnr.cropped_psnr(
ray_colors[:1, :, :, :].permute((0, 3, 1, 2)),
rendered_image_outputs.full_image[None, :, :, :].permute(
(0, 3, 1, 2))
)
cropped_psnr_values[lod].append(
float(cropped_psnr_val))
self.model.train()
avg_cropped_psnr_values = [
np.mean(x) for x in cropped_psnr_values]
return avg_cropped_psnr_values[0]
def save_checkpoint(self, step: int, force: bool = False):
"""Saves a checkpoint.
Args:
step: The current step.
force: Force a checkpoint
"""
args = self.args
if (args.checkpoint_interval == 0 or
step % args.checkpoint_interval == 0 or force):
data_dict = {
CheckpointKeys.MODEL_STATE_DICT: self.model.state_dict(),
CheckpointKeys.LATENT_CODE_STATE_DICT: self.latent_code_layer.state_dict(),
CheckpointKeys.OPTIMIZER_STATE_DICT: self.optimizer.state_dict(),
}
if args.use_latent_codes:
data_dict[CheckpointKeys.LATENT_OPTIMIZER_STATE_DICT] = self.latent_optimizer.state_dict()
if args.use_importance_training:
data_dict[CheckpointKeys.IMPORTANCE_MODEL_STATE_DICT] = self.importance_model.state_dict()
if args.use_aux_network:
data_dict[CheckpointKeys.AUX_MODEL_STATE_DICT] = self.aux_model.state_dict()
self.checkpoint_manager.save_checkpoint(data_dict)
self.writer.flush()
def log_training_to_tensorboard(self, step: int, run_outputs: InferenceOutputs,
other_variables: dict,
epoch: Optional[int] = None):
"""Logs training to tensorboard.
Args:
step: The current global step.
run_outputs: Outputs of the run.
other_variables: Other variables to log.
epoch: The current epoch.
Returns:
None
"""
args = self.args
writer = self.writer
run_outputs_items = [(f.name, getattr(run_outputs, f.name))
for f in dataclasses.fields(run_outputs)]
for k, v in itertools.chain(run_outputs_items, other_variables.items()):
if isinstance(v, float) or isinstance(v, int):
writer.add_scalar("train/" + k, v, step)
elif isinstance(v, torch.Tensor) and v.numel() == 1:
writer.add_scalar("train/" + k, v.item(), step)
if epoch is not None:
writer.add_scalar("train/epoch", epoch, step)
if step % self.args.train_tensorboard_interval == 0:
with torch.no_grad():
self.writer.add_image("train/gt_image", other_variables["gt_image"][0],
self.checkpoint_manager.step,
dataformats="HWC")
def do_inference(self, rays: torch.Tensor,
early_stopping: bool = False,
ray_colors: Optional[torch.Tensor] = None,
ray_mask: Optional[torch.Tensor] = None,
ray_saliency: Optional[torch.Tensor] = None,
ray_mipcolors: Optional[torch.Tensor] = None,
lods: Optional[Sequence[int]] = None,
aux_discarding: bool = False,
extra_options: Dict[Any, Any] = None) -> InferenceOutputs:
"""Do a forward pass for inference and compute losses.
Args:
rays: Set of rays as an (N, D+1) tensor with t in last index.
early_stopping: Use early stopping.
ray_colors: Ground truth colors as (N, 3) tensor.
ray_mask: Optional value for mask at each ray.
ray_saliency: Ray saliency
ray_mipcolors: Mipped colors.
lods: Lods to render.
aux_discarding: Discard pixels using aux network.
extra_options: Dictionary of options to pass.
Returns:
A dict containing the run outputs.
"""
extra_options = {} if extra_options is None else extra_options
args = self.args
if args.positional_encoding_functions:
rays_positional_encoding = nerf_utils.add_positional_encoding(
rays[:, :-1],
num_encoding_functions=args.positional_encoding_functions)
rays_with_encoding = torch.cat(
(rays_positional_encoding, rays[:, -1:]), dim=-1)
else:
rays_with_encoding = rays
if args.use_latent_codes:
features = self.latent_code_layer(rays_with_encoding)
else:
features = rays_with_encoding
aux_model_output = None
aux_discarding_keep = None
if args.use_aux_network:
aux_model_output: torch.Tensor = self.aux_model(features)
if aux_discarding:
aux_discarding_keep = aux_model_output[:, 0] > 0.2
features = features[aux_discarding_keep]
if args.model == ADAPTIVE_MLP:
model_output: adaptive_mlp.AdaptiveMLPOutputs = self.model(
features, early_stopping)
elif args.model == ADAPTIVE_RESNET:
if lods:
model_output: adaptive_resnet.AdaptiveResnetOutputs = self.model(
features, lods)
else:
model_output: adaptive_resnet.AdaptiveResnetOutputs = self.model(
features)
elif args.model in (MLP,):
model_output: torch.Tensor = self.model(features)
elif args.model == SUBNET:
if lods:
model_output: subnet.SubNetOutputs = self.model(features, [self.model.factors[x] for x in lods],
**extra_options)
elif self.model.training and args.subnet_optimized_training:
model_output: subnet.SubNetOutputs = self.model(features, [
self.rng.choice(self.model.factors[:-1]),
self.model.factors[-1],
], **extra_options)
else:
model_output: subnet.SubNetOutputs = self.model(
features, **extra_options)
elif args.model == MIPNET:
if lods:
factors = [self.model.factors[x] +
(self.model.factors[x - 1] if x > 0 else (0, 0,)) for x in lods]
model_output: mipnet.MipNetOutputs = self.model(
features, factors)
else:
model_output: mipnet.MipNetOutputs = self.model(features)
elif args.model == SMIPNET:
if lods:
factors = [self.model.factors[x] for x in lods]
model_output: smipnet.SMipNetOutputs = self.model(
features, factors)
else:
model_output: smipnet.SMipNetOutputs = self.model(features)
else:
raise ValueError("Unknown model")
loss = None
color_loss = None
efficiency_loss = None
loadbalance_loss = None
clustering_loss = None
mm_importance_loss = None
aux_loss = None
if ray_colors is not None:
if args.use_importance_training:
raise NotImplementedError()
gt = ray_colors if args.predict_alpha else ray_colors[:, :3]
if args.predict_alpha and gt.shape[1] < 4:
gt = torch.cat((ray_colors, ray_mask[:, None]), dim=-1)
if args.model == ADAPTIVE_MLP and not early_stopping:
layer_outputs = model_output.layer_outputs
layer_stop_here_probabilities = model_output.layer_stop_here_probabilities
if args.lossfn_color == "l1":
pixel_loss = torch.abs(layer_outputs - gt[:, None, :])
elif args.lossfn_color == "l2":
pixel_loss = torch.pow(layer_outputs - gt[:, None, :], 2.0)
else:
raise ValueError("Unknown loss function")
color_loss = torch.mean(
pixel_loss * layer_stop_here_probabilities[:, :, None])
efficiency_loss = torch.mean(model_output.expected_layers)
loss = (color_loss +
args.efficiency_loss_lambda * efficiency_loss)
elif args.model in (ADAPTIVE_RESNET, SUBNET,):
if args.lossfn_color == "l1":
pixel_loss = torch.abs(model_output.outputs - gt[:, None])
elif args.lossfn_color == "l2":
pixel_loss = torch.pow(
model_output.outputs - gt[:, None], 2.0)
else:
raise ValueError("Unknown loss function")
if ray_mask is not None and args.lossfn_color_mask_factor > 0:
color_loss = torch.mean(
pixel_loss * (1.0 + args.lossfn_color_mask_factor * ray_mask[:, None]))
else:
color_loss = torch.mean(pixel_loss)