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surface_rendering_main.py
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surface_rendering_main.py
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import os
import warnings
import hydra
import numpy as np
import torch
import tqdm
import imageio
from omegaconf import DictConfig
from PIL import Image
from pytorch3d.renderer import (
PerspectiveCameras,
look_at_view_transform
)
import matplotlib.pyplot as plt
from sampler import sampler_dict
from implicit import implicit_dict
from renderer import renderer_dict
from losses import eikonal_loss, sphere_loss, get_random_points, select_random_points
from ray_utils import (
sample_images_at_xy,
get_pixels_from_image,
get_random_pixels_from_image,
get_rays_from_pixels
)
from data_utils import (
dataset_from_config,
create_surround_cameras,
vis_grid,
vis_rays,
)
from dataset import (
get_nerf_datasets,
trivial_collate,
)
from render_functions import render_geometry
from render_functions import render_points_with_save
# Model class containing:
# 1) Implicit function defining the scene
# 2) Sampling scheme which generates sample points along rays
# 3) Renderer which can render an implicit function given a sampling scheme
class Model(torch.nn.Module):
def __init__(
self,
cfg
):
super().__init__()
# Get implicit function from config
self.implicit_fn = implicit_dict[cfg.implicit_function.type](
cfg.implicit_function
)
# Point sampling (raymarching) scheme
self.sampler = sampler_dict[cfg.sampler.type](
cfg.sampler
)
# Initialize implicit renderer
self.renderer = renderer_dict[cfg.renderer.type](
cfg.renderer
)
def forward(
self,
ray_bundle,
light_dir=None
):
# Call renderer with
# a) Implicit function
# b) Sampling routine
return self.renderer(
self.sampler,
self.implicit_fn,
ray_bundle,
light_dir
)
def render_images(
model,
cameras,
image_size,
save=False,
file_prefix='',
lights=None,
feat='color'
):
all_images = []
device = list(model.parameters())[0].device
for cam_idx, camera in enumerate(cameras):
print(f'Rendering image {cam_idx}')
with torch.no_grad():
torch.cuda.empty_cache()
# Get rays
camera = camera.to(device)
light_dir = None
# We assume the object is placed at the origin
origin = torch.tensor([0.0, 0.0, 0.0], device=device)
light_location = None if lights is None else lights[cam_idx].location.to(device)
if lights is not None:
light_dir = None #TODO: Use light location and origin to compute light direction
light_dir = torch.nn.functional.normalize(light_dir, dim=-1).view(-1, 3)
xy_grid = get_pixels_from_image(image_size, camera)
ray_bundle = get_rays_from_pixels(xy_grid, image_size, camera)
# Run model forward
out = model(ray_bundle, light_dir)
# Return rendered features (colors)
image = np.array(
out[feat].view(
image_size[1], image_size[0], 3
).detach().cpu()
)
all_images.append(image)
# Save
if save:
plt.imsave(
f'{file_prefix}_{cam_idx}.png',
image
)
return all_images
def render(
cfg,
):
# Create model
model = Model(cfg)
model = model.cuda(); model.eval()
# Render spiral
cameras = create_surround_cameras(3.0, n_poses=20, up=(0.0, 0.0, 1.0))
all_images = render_images(
model, cameras, cfg.data.image_size
)
imageio.mimsave('images/part_5.gif', [np.uint8(im * 255) for im in all_images])
def create_model(cfg):
# Create model
model = Model(cfg)
model.cuda(); model.train()
# Load checkpoints
optimizer_state_dict = None
start_epoch = 0
checkpoint_path = os.path.join(
hydra.utils.get_original_cwd(),
cfg.training.checkpoint_path
)
if len(cfg.training.checkpoint_path) > 0:
# Make the root of the experiment directory.
checkpoint_dir = os.path.split(checkpoint_path)[0]
os.makedirs(checkpoint_dir, exist_ok=True)
# Resume training if requested.
if cfg.training.resume and os.path.isfile(checkpoint_path):
print(f"Resuming from checkpoint {checkpoint_path}.")
loaded_data = torch.load(checkpoint_path)
model.load_state_dict(loaded_data["model"])
start_epoch = loaded_data["epoch"]
print(f" => resuming from epoch {start_epoch}.")
optimizer_state_dict = loaded_data["optimizer"]
# Initialize the optimizer.
optimizer = torch.optim.Adam(
model.parameters(),
lr=cfg.training.lr,
)
# Load the optimizer state dict in case we are resuming.
if optimizer_state_dict is not None:
optimizer.load_state_dict(optimizer_state_dict)
optimizer.last_epoch = start_epoch
# The learning rate scheduling is implemented with LambdaLR PyTorch scheduler.
def lr_lambda(epoch):
return cfg.training.lr_scheduler_gamma ** (
epoch / cfg.training.lr_scheduler_step_size
)
lr_scheduler = torch.optim.lr_scheduler.LambdaLR(
optimizer, lr_lambda, last_epoch=start_epoch - 1, verbose=False
)
return model, optimizer, lr_scheduler, start_epoch, checkpoint_path
def train_points(
cfg
):
# Create model
model, optimizer, lr_scheduler, start_epoch, checkpoint_path = create_model(cfg)
# Pretrain SDF
pretrain_sdf(cfg, model)
# Load pointcloud
point_cloud = np.load(cfg.data.point_cloud_path)
all_points = torch.Tensor(point_cloud["verts"][::2]).cuda().view(-1, 3)
all_points = all_points - torch.mean(all_points, dim=0).unsqueeze(0)
point_images = render_points_with_save(
all_points.unsqueeze(0), create_surround_cameras(3.0, n_poses=20, up=(0.0, 1.0, 0.0), focal_length=2.0),
cfg.data.image_size, file_prefix='points'
)
imageio.mimsave('images/part_6_input.gif', [np.uint8(im * 255) for im in point_images])
# Run the main training loop.
for epoch in range(0, cfg.training.num_epochs):
t_range = tqdm.tqdm(range(0, all_points.shape[0], cfg.training.batch_size))
for idx in t_range:
# Select random points from pointcloud
points = select_random_points(all_points, cfg.training.batch_size)
# Get distances and enforce point cloud loss
distances, gradients = model.implicit_fn.get_distance_and_gradient(points)
loss = None # TODO (Q6): Point cloud SDF loss on distances
point_loss = loss
# Sample random points in bounding box
eikonal_points = get_random_points(
cfg.training.batch_size, cfg.training.bounds, 'cuda'
)
# Get sdf gradients and enforce eikonal loss
eikonal_distances, eikonal_gradients = model.implicit_fn.get_distance_and_gradient(eikonal_points)
loss += torch.exp(-1e2 * torch.abs(eikonal_distances)).mean() * cfg.training.inter_weight
loss += eikonal_loss(eikonal_gradients) * cfg.training.eikonal_weight # TODO (Q6): Implement eikonal loss
# Take the training step.
optimizer.zero_grad()
loss.backward()
optimizer.step()
t_range.set_description(f'Epoch: {epoch:04d}, Loss: {point_loss:.06f}')
t_range.refresh()
# Checkpoint.
if (
epoch % cfg.training.checkpoint_interval == 0
and len(cfg.training.checkpoint_path) > 0
and epoch > 0
):
print(f"Storing checkpoint {checkpoint_path}.")
data_to_store = {
"model": model.state_dict(),
"optimizer": optimizer.state_dict(),
"epoch": epoch,
}
torch.save(data_to_store, checkpoint_path)
# Render
if (
epoch % cfg.training.render_interval == 0
and epoch > 0
):
try:
test_images = render_geometry(
model, create_surround_cameras(3.0, n_poses=20, up=(0.0, 1.0, 0.0), focal_length=2.0),
cfg.data.image_size, file_prefix='eikonal', thresh=0.002,
)
imageio.mimsave('images/part_6.gif', [np.uint8(im * 255) for im in test_images])
except Exception as e:
print("Empty mesh")
pass
def pretrain_sdf(
cfg,
model
):
optimizer = torch.optim.Adam(
model.parameters(),
lr=cfg.training.lr,
)
# Run the main training loop.
for iter in range(0, cfg.training.pretrain_iters):
points = get_random_points(
cfg.training.batch_size, cfg.training.bounds, 'cuda'
)
# Run model forward
distances = model.implicit_fn.get_distance(points)
loss = sphere_loss(distances, points, 1.0)
# Take the training step
optimizer.zero_grad()
loss.backward()
optimizer.step()
def train_images(
cfg
):
# Create model
model, optimizer, lr_scheduler, start_epoch, checkpoint_path = create_model(cfg)
# Load the training/validation data.
train_dataset, val_dataset, _ = get_nerf_datasets(
dataset_name=cfg.data.dataset_name,
image_size=[cfg.data.image_size[1], cfg.data.image_size[0]],
)
train_dataloader = torch.utils.data.DataLoader(
train_dataset,
batch_size=1,
shuffle=True,
num_workers=0,
collate_fn=lambda batch: batch,
)
# Pretrain SDF
pretrain_sdf(cfg, model)
# Run the main training loop.
for epoch in range(start_epoch, cfg.training.num_epochs):
t_range = tqdm.tqdm(enumerate(train_dataloader))
for iteration, batch in t_range:
image, camera, camera_idx = batch[0].values()
image = image.cuda().unsqueeze(0)
camera = camera.cuda()
# Sample rays
xy_grid = get_random_pixels_from_image(
cfg.training.batch_size, cfg.data.image_size, camera
)
ray_bundle = get_rays_from_pixels(
xy_grid, cfg.data.image_size, camera
)
rgb_gt = sample_images_at_xy(image, xy_grid)
# Run model forward
out = model(ray_bundle)
# Color loss
loss = torch.mean(torch.square(rgb_gt - out['color']))
image_loss = loss
# Sample random points in bounding box
eikonal_points = get_random_points(
cfg.training.batch_size, cfg.training.bounds, 'cuda'
)
# Get sdf gradients and enforce eikonal loss
eikonal_distances, eikonal_gradients = model.implicit_fn.get_distance_and_gradient(eikonal_points)
loss += torch.exp(-1e2 * torch.abs(eikonal_distances)).mean() * cfg.training.inter_weight
loss += eikonal_loss(eikonal_gradients) * cfg.training.eikonal_weight # TODO (2): Implement eikonal loss
# Take the training step.
optimizer.zero_grad()
loss.backward()
optimizer.step()
t_range.set_description(f'Epoch: {epoch:04d}, Loss: {image_loss:.06f}')
t_range.refresh()
# Adjust the learning rate.
lr_scheduler.step()
# Checkpoint.
if (
epoch % cfg.training.checkpoint_interval == 0
and len(cfg.training.checkpoint_path) > 0
and epoch > 0
):
print(f"Storing checkpoint {checkpoint_path}.")
data_to_store = {
"model": model.state_dict(),
"optimizer": optimizer.state_dict(),
"epoch": epoch,
}
torch.save(data_to_store, checkpoint_path)
# Render
if (
epoch % cfg.training.render_interval == 0
and epoch > 0
):
test_images = render_images(
model, create_surround_cameras(4.0, n_poses=20, up=(0.0, 0.0, 1.0), focal_length=2.0),
cfg.data.image_size, file_prefix='volsdf'
)
imageio.mimsave('images/part_7.gif', [np.uint8(im * 255) for im in test_images])
try:
test_images = render_geometry(
model, create_surround_cameras(4.0, n_poses=20, up=(0.0, 0.0, 1.0), focal_length=2.0),
cfg.data.image_size, file_prefix='volsdf_geometry'
)
imageio.mimsave('images/part_7_geometry.gif', [np.uint8(im * 255) for im in test_images])
except Exception as e:
print("Empty mesh")
pass
@hydra.main(config_path='configs', config_name='torus')
def main(cfg: DictConfig):
os.chdir(hydra.utils.get_original_cwd())
if cfg.type == 'render':
render(cfg)
elif cfg.type == 'train_points':
train_points(cfg)
elif cfg.type == 'train_images':
train_images(cfg)
if __name__ == "__main__":
main()