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render_mask.py
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render_mask.py
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import io
import os
import json
import sys
import torch
import torch.nn.functional as F
import torchvision
import matplotlib.pyplot as plt
from PIL import Image
import seaborn as sns
import numpy as np
from scene import Scene, GaussianModel
from scene.index_decoder import *
from gaussian_renderer import render
from utils.lem_utils import *
from utils.general_utils import safe_state
import configargparse
from arguments import ModelParams, PipelineParams, OptimizationParams
def draw_rele_distrib(rele, kde=True):
rele = rele.view(-1).detach().to("cpu").numpy()
plt.figure()
if kde:
sns.kdeplot(rele, color='blue', label='rele')
else:
plt.hist(rele, bins=30, color='blue', alpha=0.5, label='rele')
plt.legend(loc='upper right')
# create a file-like object from the figure, to convert to PIL
buf = io.BytesIO()
plt.savefig(buf, format='png')
buf.seek(0)
img = Image.open(buf)
plt.close()
return img
def rendering_mask(dataset, opt, pipe, checkpoint, codebook_pth, test_set, texts_dict, a, scale, com_type, device="cuda"):
gaussians = GaussianModel(dataset.sh_degree, dataset.semantic_features_dim, dataset.points_num_limit)
scene = Scene(dataset, gaussians, test_set=test_set, is_test=True)
index_decoder = IndexDecoder(dataset.semantic_features_dim, dataset.codebook_size).to(device)
(model_params, first_iter) = torch.load(checkpoint)
gaussians.restore(model_params, opt)
index_decoder_ckpt = os.path.join(os.path.dirname(checkpoint), "index_decoder_" + os.path.basename(checkpoint))
index_decoder.load_state_dict(torch.load(index_decoder_ckpt))
codebook = read_codebook(codebook_pth)
clip_rele = CLIPRelevance(device=device)
ouptut_dir = os.path.dirname(checkpoint)
eval_name = f"open_new_eval_{com_type}_s{scale}_a{str(a).replace('.', '')}"
gt_images_pth = f"{ouptut_dir}/{eval_name}/gt_images"
pred_images_pth = f"{ouptut_dir}/{eval_name}/pred_images"
pred_segs_pth = f"{ouptut_dir}/{eval_name}/pred_segs"
rele_pth = f"{ouptut_dir}/{eval_name}/relevancy"
os.makedirs(gt_images_pth, exist_ok=True)
os.makedirs(pred_images_pth, exist_ok=True)
os.makedirs(pred_segs_pth, exist_ok=True)
os.makedirs(rele_pth, exist_ok=True)
bg_color = [1, 1, 1] if dataset.white_background else [0, 0, 0]
background = torch.tensor(bg_color, dtype=torch.float32, device="cuda")
viewpoint_stack = scene.getTestCameras().copy()
for cam in viewpoint_stack:
render_pkg = render(cam, gaussians, pipe, background)
gt_image = cam.original_image
image = render_pkg["render"].detach()
torchvision.utils.save_image(gt_image, f"{gt_images_pth}/{cam.image_name}.png")
torchvision.utils.save_image(image, f"{pred_images_pth}/{cam.image_name}.png")
os.makedirs(f"{pred_segs_pth}/{cam.image_name}", exist_ok=True)
os.makedirs(f"{pred_segs_pth}/{cam.image_name}/distr", exist_ok=True)
os.makedirs(f"{rele_pth}/{cam.image_name}/array", exist_ok=True)
os.makedirs(f"{rele_pth}/{cam.image_name}/images", exist_ok=True)
semantic_features = render_pkg["semantic_features"].detach()
norm_semantic_features = F.normalize(semantic_features, p=2, dim=0)
with torch.no_grad():
indices = index_decoder(norm_semantic_features.unsqueeze(0))
index_tensor = torch.argmax(indices, dim=1).squeeze()
if com_type == "argmax":
# argmax
clip_features = F.embedding(index_tensor, codebook[:, :512])
elif com_type == "softmax":
temp = 1 # ->0 = argmax, ->+inf = unifrom
prob_tensor = torch.softmax(indices / temp, dim=1).permute(0, 2, 3, 1) # (N, C=128, H, W)
clip_features = (prob_tensor @ codebook[:, :512]).squeeze()
seg_indices = -1 * torch.ones_like(index_tensor)
for i in range(len(list(texts_dict.keys()))):
text = list(texts_dict.keys())[i]
if type(texts_dict[text]) is list:
rele0 = clip_rele.get_relevancy(clip_features, texts_dict[text][0], scale).squeeze()[..., 0]
rele1 = clip_rele.get_relevancy(clip_features, texts_dict[text][1], scale).squeeze()[..., 0]
rele = torch.logical_or((rele0 >= a).float(), (rele1 >= a).float())
else:
rele = clip_rele.get_relevancy(clip_features, texts_dict[text], negatives=None, scale=scale).squeeze()[..., 0]
# norm
# rele = (rele - rele.min()) / (rele.max() - rele.min())
rele_distr_img = draw_rele_distrib(rele)
msk = (rele >= a)
np.save(f"{rele_pth}/{cam.image_name}/array/{text}.npy", rele.detach().cpu().numpy())
torchvision.utils.save_image(rele, f"{rele_pth}/{cam.image_name}/images/{text}.png")
torchvision.utils.save_image(msk.float(), f"{pred_segs_pth}/{cam.image_name}/{text}.png")
rele_distr_img.save(f"{pred_segs_pth}/{cam.image_name}/distr/{text}.png")
seg_indices[msk] = i
with open(f"{pred_segs_pth}/texts_dict.json", "w") as f:
json.dump(texts_dict, f, indent=4)
torch.cuda.empty_cache()
if __name__ == "__main__":
# Set up command line argument parser
parser = configargparse.ArgParser(description="Training script parameters")
lp = ModelParams(parser)
op = OptimizationParams(parser)
pp = PipelineParams(parser)
parser.add('--config', required=True, is_config_file=True, help='config file path')
parser.add_argument('--detect_anomaly', action='store_true', default=False)
parser.add_argument("--mode", type=str, default="search", choices=["search"])
parser.add_argument("--start_checkpoint", type=str, default = None)
parser.add_argument("--codebook", type=str, default = None)
parser.add_argument("--test_set", nargs="+", type=str, default=[])
parser.add_argument("--texts", nargs="+", type=str, default=[])
parser.add_argument("--alpha", type=float, default=0.5)
parser.add_argument("--scale", type=float, default=100)
parser.add_argument("--com_type", type=str, default="")
args = parser.parse_args(sys.argv[1:])
print("Optimizing " + args.model_path)
# Initialize system state (RNG)
safe_state(False)
torch.autograd.set_detect_anomaly(args.detect_anomaly)
texts_dict = {}
for i in range(len(args.texts)):
texts_dict[args.texts[i]] = args.texts[i]
rendering_mask(lp.extract(args), op.extract(args), pp.extract(args),
args.start_checkpoint, args.codebook,
args.test_set, texts_dict, args.alpha, args.scale, args.com_type)
# All done
print("Rendering done.")