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import numpy as np
import torch
import imageio
from my.utils.tqdm import tqdm
from my.utils.event import EventStorage, read_stats, get_event_storage
from my.utils.heartbeat import HeartBeat, get_heartbeat
from my.utils.debug import EarlyLoopBreak
from .utils import PSNR, Scrambler, every, at
from .data import load_blender
from .render import (
as_torch_tsrs, scene_box_filter, render_ray_bundle, render_one_view, rays_from_img
)
from .vis import vis, stitch_vis
device_glb = torch.device("cuda")
def all_train_rays(scene):
imgs, K, poses = load_blender("train", scene)
num_imgs = len(imgs)
ro, rd, rgbs = [], [], []
for i in tqdm(range(num_imgs)):
img, pose = imgs[i], poses[i]
H, W = img.shape[:2]
_ro, _rd = rays_from_img(H, W, K, pose)
ro.append(_ro)
rd.append(_rd)
rgbs.append(img.reshape(-1, 3))
ro, rd, rgbs = [
np.concatenate(xs, axis=0) for xs in (ro, rd, rgbs)
]
return ro, rd, rgbs
class OneTestView():
def __init__(self, scene):
imgs, K, poses = load_blender("test", scene)
self.imgs, self.K, self.poses = imgs, K, poses
self.i = 0
def render(self, model):
i = self.i
img, K, pose = self.imgs[i], self.K, self.poses[i]
with torch.no_grad():
aabb = model.aabb.T.cpu().numpy()
H, W = img.shape[:2]
rgbs, depth = render_one_view(model, aabb, H, W, K, pose)
psnr = PSNR.psnr(img, rgbs)
self.i = (self.i + 1) % len(self.imgs)
return img, rgbs, depth, psnr
def train(
model, n_epoch=2, bs=4096, lr=0.02, scene="lego"
):
fuse = EarlyLoopBreak(500)
aabb = model.aabb.T.numpy()
model = model.to(device_glb)
optim = torch.optim.Adam(model.parameters(), lr=lr)
test_view = OneTestView(scene)
all_ro, all_rd, all_rgbs = all_train_rays(scene)
with tqdm(total=(n_epoch * len(all_ro) // bs)) as pbar, \
HeartBeat(pbar) as hbeat, EventStorage() as metric:
ro, rd, t_min, t_max, intsct_inds = scene_box_filter(all_ro, all_rd, aabb)
rgbs = all_rgbs[intsct_inds]
for epc in range(n_epoch):
n = len(ro)
scrambler = Scrambler(n)
ro, rd, t_min, t_max, rgbs = scrambler.apply(ro, rd, t_min, t_max, rgbs)
num_batch = int(np.ceil(n / bs))
for i in range(num_batch):
if fuse.on_break():
break
s = i * bs
e = min(n, s + bs)
optim.zero_grad()
_ro, _rd, _t_min, _t_max, _rgbs = as_torch_tsrs(
model.device, ro[s:e], rd[s:e], t_min[s:e], t_max[s:e], rgbs[s:e]
)
pred, _, _ = render_ray_bundle(model, _ro, _rd, _t_min, _t_max)
loss = ((pred - _rgbs) ** 2).mean()
loss.backward()
optim.step()
pbar.update()
psnr = PSNR.psnr_from_mse(loss.item())
metric.put_scalars(psnr=psnr, d_scale=model.d_scale.item())
if every(pbar, step=50):
pbar.set_description(f"TRAIN: psnr {psnr:.2f}")
if every(pbar, percent=1):
gimg, rimg, depth, psnr = test_view.render(model)
pane = vis(
gimg, rimg, depth,
msg=f"psnr: {psnr:.2f}", return_buffer=True
)
metric.put_artifact(
"vis", ".png", lambda fn: imageio.imwrite(fn, pane)
)
if at(pbar, percent=30):
model.make_alpha_mask()
if every(pbar, percent=35):
target_xyz = (model.grid_size * 1.328).int().tolist()
model.resample(target_xyz)
optim = torch.optim.Adam(model.parameters(), lr=lr)
print(f"resamp the voxel to {model.grid_size}")
curr_lr = update_lr(pbar, optim, lr)
metric.put_scalars(lr=curr_lr)
metric.step()
hbeat.beat()
metric.put_artifact(
"ckpt", ".pt", lambda fn: torch.save(model.state_dict(), fn)
)
# metric.step(flush=True) # no need to flush since the test routine directly takes the model
metric.put_artifact(
"train_seq", ".mp4",
lambda fn: stitch_vis(fn, read_stats(metric.output_dir, "vis")[1])
)
with EventStorage("test"):
final_psnr = test(model, scene)
metric.put("test_psnr", final_psnr)
metric.step()
hbeat.done()
def update_lr(pbar, optimizer, init_lr):
i, N = pbar.n, pbar.total
factor = 0.1 ** (1 / N)
lr = init_lr * (factor ** i)
for param_group in optimizer.param_groups:
param_group['lr'] = lr
return lr
def last_ckpt():
ts, ckpts = read_stats("./", "ckpt")
if len(ckpts) > 0:
fname = ckpts[-1]
last = torch.load(fname, map_location="cpu")
print(f"loaded ckpt from iter {ts[-1]}")
return last
def __evaluate_ckpt(model, scene):
# this is for external script that needs to evaluate an checkpoint
# currently not used
metric = get_event_storage()
state = last_ckpt()
if state is not None:
model.load_state_dict(state)
model.to(device_glb)
with EventStorage("test"):
final_psnr = test(model, scene)
metric.put("test_psnr", final_psnr)
def test(model, scene):
fuse = EarlyLoopBreak(5)
metric = get_event_storage()
hbeat = get_heartbeat()
aabb = model.aabb.T.cpu().numpy()
model = model.to(device_glb)
imgs, K, poses = load_blender("test", scene)
num_imgs = len(imgs)
stats = []
for i in (pbar := tqdm(range(num_imgs))):
if fuse.on_break():
break
img, pose = imgs[i], poses[i]
H, W = img.shape[:2]
rgbs, depth = render_one_view(model, aabb, H, W, K, pose)
psnr = PSNR.psnr(img, rgbs)
stats.append(psnr)
metric.put_scalars(psnr=psnr)
pbar.set_description(f"TEST: mean psnr {np.mean(stats):.2f}")
plot = vis(img, rgbs, depth, msg=f"PSNR: {psnr:.2f}", return_buffer=True)
metric.put_artifact("test_vis", ".png", lambda fn: imageio.imwrite(fn, plot))
metric.step()
hbeat.beat()
metric.put_artifact(
"test_seq", ".mp4",
lambda fn: stitch_vis(fn, read_stats(metric.output_dir, "test_vis")[1])
)
final_psnr = np.mean(stats)
metric.put("final_psnr", final_psnr)
metric.step()
return final_psnr
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