File size: 6,762 Bytes
d661b19 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 |
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)
print(n_epoch, len(all_ro), bs)
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]
print(len(ro))
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
|