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from pathlib import Path
import torch
import numpy as np
import cv2
import os
import subprocess
import open3d as o3d
from tqdm import tqdm
from pgnd.utils import get_root
from pgnd.ffmpeg import make_video
import sys
root: Path = get_root(__file__)
from diff_gaussian_rasterization import GaussianRasterizer
from diff_gaussian_rasterization import GaussianRasterizationSettings as Camera
from gs.helpers import setup_camera
from gs.external import densify
from gs.train_utils import get_custom_dataset, initialize_params, initialize_optimizer, get_loss, report_progress, get_batch
def Rt_to_w2c(R, t):
w2c = np.concatenate([np.concatenate([R, t.reshape(3, 1)], axis=1), np.array([[0, 0, 0, 1]])], axis=0)
w2c = np.linalg.inv(w2c)
return w2c
class GSTrainer:
def __init__(self, config, device):
self.config = config
self.device = device
self.clear()
def clear(self, clear_params=True):
# training data
self.init_pt_cld = None
self.metadata = None
self.img_list = None
self.seg_list = None
# training results
if clear_params:
self.params = None
@torch.no_grad
def render(self, render_data, cam_id, bg=[0.7, 0.7, 0.7]):
render_data = {k: v.to(self.device) for k, v in render_data.items()}
w, h = self.metadata['w'], self.metadata['h']
k = self.metadata['k'][cam_id]
w2c = self.metadata['w2c'][cam_id]
cam = setup_camera(w, h, k, w2c, self.config['near'], self.config['far'], bg)
im, _, depth, = GaussianRasterizer(raster_settings=cam)(**render_data)
return im, depth
def update_state_env(self, pcd, env, imgs, masks): # RGB
R_list, t_list = env.get_extrinsics()
intr_list = env.get_intrinsics()
rgb_list = []
seg_list = []
for c in range(env.n_fixed_cameras):
rgb_list.append(imgs[c] * masks[c][:, :, None])
seg_list.append(masks[c] * 1.0)
self.update_state(pcd, rgb_list, seg_list, R_list, t_list, intr_list)
def update_state_no_env(self, pcd, imgs, masks, R_list, t_list, intr_list, n_cameras=4): # RGB
rgb_list = []
seg_list = []
for c in range(n_cameras):
rgb_list.append(imgs[c] * masks[c][:, :, None])
seg_list.append(masks[c] * 1.0)
self.update_state(pcd, rgb_list, seg_list, R_list, t_list, intr_list)
def update_state(self, pcd, img_list, seg_list, R_list, t_list, intr_list):
pts = np.array(pcd.points).astype(np.float32)
colors = np.array(pcd.colors).astype(np.float32)
seg = np.ones_like(pts[:, 0:1])
self.init_pt_cld = np.concatenate([pts, colors, seg], axis=1)
w, h = img_list[0].shape[1], img_list[0].shape[0]
assert np.all([img.shape[1] == w and img.shape[0] == h for img in img_list])
self.metadata = {
'w': w,
'h': h,
'k': [intr for intr in intr_list],
'w2c': [Rt_to_w2c(R, t) for R, t in zip(R_list, t_list)]
}
self.img_list = img_list
self.seg_list = seg_list
def train(self, vis_dir):
params, variables = initialize_params(self.init_pt_cld, self.metadata)
optimizer = initialize_optimizer(params, variables)
dataset = get_custom_dataset(self.img_list, self.seg_list, self.metadata)
todo_dataset = []
num_iters = self.config['num_iters']
loss_weights = {'im': self.config['weight_im'], 'seg': self.config['weight_seg']}
densify_params = {
'grad_thresh': self.config['grad_thresh'],
'remove_thresh': self.config['remove_threshold'],
'remove_thresh_5k': self.config['remove_thresh_5k'],
'scale_scene_radius': self.config['scale_scene_radius']
}
progress_bar = tqdm(range(num_iters), dynamic_ncols=True)
for i in range(num_iters):
curr_data = get_batch(todo_dataset, dataset)
loss, variables = get_loss(params, curr_data, variables, loss_weights)
loss.backward()
with torch.no_grad():
params, variables, num_pts = densify(params, variables, optimizer, i, **densify_params)
report_progress(params, dataset[0], i, progress_bar, num_pts, vis_dir=vis_dir)
optimizer.step()
optimizer.zero_grad(set_to_none=True)
progress_bar.close()
params = {k: v.detach() for k, v in params.items()}
self.params = params
def rollout_and_render(self, dm, action, vis_dir=None, save_images=True, overwrite_params=True, remove_black=False):
assert vis_dir is not None
assert self.params is not None
xyz_0 = self.params['means3D']
rgb_0 = self.params['rgb_colors']
quat_0 = torch.nn.functional.normalize(self.params['unnorm_rotations'])
opa_0 = torch.sigmoid(self.params['logit_opacities'])
scales_0 = torch.exp(self.params['log_scales'])
low_opa_idx = opa_0[:, 0] < 0.1
xyz_0 = xyz_0[~low_opa_idx]
rgb_0 = rgb_0[~low_opa_idx]
quat_0 = quat_0[~low_opa_idx]
opa_0 = opa_0[~low_opa_idx]
scales_0 = scales_0[~low_opa_idx]
if remove_black:
low_color_idx = rgb_0.sum(dim=-1) < 0.5
xyz_0 = xyz_0[~low_color_idx]
rgb_0 = rgb_0[~low_color_idx]
quat_0 = quat_0[~low_color_idx]
opa_0 = opa_0[~low_color_idx]
scales_0 = scales_0[~low_color_idx]
eef_xyz_start = action[0]
eef_xyz_end = action[1]
dist_thresh = 0.005 # 5mm
n_steps = int((eef_xyz_end - eef_xyz_start).norm().item() / dist_thresh)
eef_xyz = torch.lerp(eef_xyz_start, eef_xyz_end, torch.linspace(0, 1, n_steps).to(self.device)[:, None])
eef_xyz_pad = torch.cat([eef_xyz, eef_xyz_end[None].repeat(dm.n_his, 1)], dim=0)
eef_xyz_pad = eef_xyz_pad[:, None] # (n_steps, 1, 3)
n_steps = eef_xyz_pad.shape[0]
inlier_idx_all = np.arange(len(xyz_0)) # no outlier removal
xyz, rgb, quat, opa, xyz_bones, eef = dm.rollout(
xyz_0, rgb_0, quat_0, opa_0, eef_xyz_pad, n_steps, inlier_idx_all)
# interpolate smoothly
change_points = (xyz - torch.concatenate([xyz[0:1], xyz[:-1]], dim=0)).norm(dim=-1).sum(dim=-1).nonzero().squeeze(1)
change_points = torch.cat([torch.tensor([0]), change_points])
for i in range(1, len(change_points)):
start = change_points[i - 1]
end = change_points[i]
if end - start < 2: # 0 or 1
continue
xyz[start:end] = torch.lerp(xyz[start][None], xyz[end][None], torch.linspace(0, 1, end - start + 1).to(xyz.device)[:, None, None])[:-1]
rgb[start:end] = torch.lerp(rgb[start][None], rgb[end][None], torch.linspace(0, 1, end - start + 1).to(rgb.device)[:, None, None])[:-1]
quat[start:end] = torch.lerp(quat[start][None], quat[end][None], torch.linspace(0, 1, end - start + 1).to(quat.device)[:, None, None])[:-1]
opa[start:end] = torch.lerp(opa[start][None], opa[end][None], torch.linspace(0, 1, end - start + 1).to(opa.device)[:, None, None])[:-1]
xyz_bones[start:end] = torch.lerp(xyz_bones[start][None], xyz_bones[end][None], torch.linspace(0, 1, end - start + 1).to(xyz_bones.device)[:, None, None])[:-1]
eef[start:end] = torch.lerp(eef[start][None], eef[end][None], torch.linspace(0, 1, end - start + 1).to(eef.device)[:, None, None])[:-1]
for _ in range(3):
xyz[1:-1] = (xyz[:-2] + 2 * xyz[1:-1] + xyz[2:]) / 4
# quat[1:-1] = (quat[:-2] + 2 * quat[1:-1] + quat[2:]) / 4
quat = torch.nn.functional.normalize(quat, dim=-1)
rendervar_list = []
visvar_list = []
# im_list = []
for t in range(n_steps):
rendervar = {
'means3D': xyz[t],
'colors_precomp': rgb[t],
'rotations': quat[t],
'opacities': opa[t],
'scales': scales_0,
'means2D': torch.zeros_like(xyz[t]),
}
rendervar_list.append(rendervar)
visvar = {
'xyz_bones': xyz_bones[t].numpy(), # params['means3D'][t][fps_idx].detach().cpu().numpy(),
'eef': eef[t].numpy(), # eef_xyz[t].detach().cpu().numpy(),
}
visvar_list.append(visvar)
if save_images:
im, _ = self.render(rendervar, 0, bg=[0, 0, 0])
im = im.cpu().numpy().transpose(1, 2, 0)
im = (im * 255).astype(np.uint8)
# im_list.append(im)
cv2.imwrite(os.path.join(vis_dir, f'{t:04d}.png'), im[:, :, ::-1].copy())
if save_images:
make_video(vis_dir, os.path.join(os.path.dirname(vis_dir), f"{vis_dir.split('/')[-1]}.mp4"), '%04d.png', 5)
if overwrite_params:
self.params['means3D'] = xyz[-1].to(self.device)
self.params['rgb_colors'] = rgb[-1].to(self.device)
self.params['unnorm_rotations'] = quat[-1].to(self.device)
self.params['logit_opacities'] = torch.logit(opa[-1]).to(self.device)
self.params['log_scales'] = torch.log(scales_0).to(self.device)
self.params['means2D'] = torch.zeros_like(xyz[-1]).to(self.device)
return rendervar_list, visvar_list
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