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evaluate_diffusion.py
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evaluate_diffusion.py
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import glob
from diffusers import UNetSpatioTemporalConditionModel
from src.dataset import DiffusionDataset
from src.xray_pipeline import XRayDiffusionPipeline
from diffusers.utils import load_image
import torch
from PIL import Image
import os
import numpy as np
import trimesh
import torchvision
import open3d as o3d
import torch.nn.functional as F
import shutil
from tqdm import tqdm
# from src.chamfer_distance import compute_trimesh_chamfer
from src.metrics import chamfer_distance_and_f_score
from scipy.sparse import csr_matrix
import argparse
def get_rays(directions, c2w):
# Rotate ray directions from camera coordinate to the world coordinate
rays_d = directions @ c2w[:3, :3].T # (H, W, 3)
rays_d = rays_d / (np.linalg.norm(rays_d, axis=-1, keepdims=True) + 1e-8)
# The origin of all rays is the camera origin in world coordinate
rays_o = np.broadcast_to(c2w[:3, 3], rays_d.shape) # (H, W, 3)
return rays_o, rays_d
def xray_to_pcd(GenDepths, GenNormals, GenColors):
camera_angle_x = 0.8575560450553894
image_width = GenDepths.shape[-1]
image_height = GenDepths.shape[-2]
fx = 0.5 * image_width / np.tan(0.5 * camera_angle_x)
rays_screen_coords = np.mgrid[0:image_height, 0:image_width].reshape(
2, image_height * image_width).T # [h, w, 2]
grid = rays_screen_coords.reshape(image_height, image_width, 2)
cx = image_width / 2.0
cy = image_height / 2.0
i, j = grid[..., 1], grid[..., 0]
directions = np.stack([(i-cx)/fx, -(j-cy)/fx, -np.ones_like(i)], -1) # (H, W, 3)
c2w = np.eye(4).astype(np.float32)
rays_origins, ray_directions = get_rays(directions, c2w)
rays_origins = rays_origins[None].repeat(GenDepths.shape[0], 0)
ray_directions = ray_directions[None].repeat(GenDepths.shape[0], 0)
GenDepths = GenDepths.transpose(0, 2, 3, 1)
GenNormals = GenNormals.transpose(0, 2, 3, 1)
GenColors = GenColors.transpose(0, 2, 3, 1)
valid_index = GenDepths[..., 0] > 0
rays_origins = rays_origins[valid_index]
ray_directions = ray_directions[valid_index]
GenDepths = GenDepths[valid_index]
normals = GenNormals[valid_index]
colors = GenColors[valid_index]
xyz = rays_origins + ray_directions * GenDepths
return xyz, normals, colors
def load_xray(xray_path):
loaded_data = np.load(xray_path)
loaded_sparse_matrix = csr_matrix((loaded_data['data'], loaded_data['indices'], loaded_data['indptr']), shape=loaded_data['shape'])
original_shape = (16, 1+3+3, 256, 256)
restored_array = loaded_sparse_matrix.toarray().reshape(original_shape)
return restored_array
if __name__ == "__main__":
parser = argparse.ArgumentParser("SVD Depth Inference")
parser.add_argument("--exp_diffusion", type=str, default="ShapeNetV2_Car", help="experiment name")
parser.add_argument("--data_root", type=str, default="Data/ShapeNetV2_Car", help="data root")
parser.add_argument("--model_id", type=str, default="stabilityai/stable-video-diffusion-img2vid")
args = parser.parse_args()
exp_name = args.exp_diffusion
model_id = args.model_id
xray_root = args.data_root
height = 64
width = 64
near = 0.6
far = 1.8
if "gso" in args.data_root.lower():
val_dataset = DiffusionDataset(xray_root, height, num_frames=8, near=near, far=far, phase="all")
else:
val_dataset = DiffusionDataset(xray_root, height, num_frames=8, near=near, far=far, phase="val")
pipe = XRayDiffusionPipeline.from_pretrained(model_id,
torch_dtype=torch.float16, variant="fp16").to("cuda")
# Get the most recent checkpoint
dirs = os.listdir(os.path.join("Output", exp_name))
dirs = [d for d in dirs if d.startswith("checkpoint")]
dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
ckpt_name = dirs[-1]
print("restore from", f"Output/{exp_name}/{ckpt_name}/unet")
pipe.unet = UNetSpatioTemporalConditionModel.from_pretrained(
f"Output/{exp_name}/{ckpt_name}",
subfolder="unet",
torch_dtype=torch.float16,
).to("cuda")
if os.path.exists(f"Output/{exp_name}/evaluate"):
shutil.rmtree(f"Output/{exp_name}/evaluate")
os.makedirs(f"Output/{exp_name}/evaluate", exist_ok=True)
all_chamfer_distance = []
all_f_score = []
progress_bar = tqdm(range(min(500, len(val_dataset))))
for i in progress_bar:
image_path = val_dataset[i]["image_path"]
uid = image_path.split("/")[-2]
with torch.no_grad():
image = load_image(image_path).resize((width * 8, height * 8), Image.BILINEAR)
mask = image.split()[-1]
mask = (np.array(mask) > 0).astype(np.float32)
if (mask.sum() / (mask.shape[0] * mask.shape[1])) < 0.05: # filter invalid image
continue
image_rgb = image.convert("RGB")
outputs = pipe(image_rgb,
height=height,
width=width,
num_frames=8,
decode_chunk_size=8,
motion_bucket_id=127,
fps=7,
noise_aug_strength=0.0,
output_type="latent").frames[0]
outputs = outputs.clamp(-1, 1) # clamp to [-1, 1]
# # save outputs to .png
# visuals = outputs[:, 4:7] * 0.5 + 0.5
# hits = outputs[:, -1:].expand(-1, 3, -1, -1) * 0.5 + 0.5
# visuals[hits < 0.5] = 1
# torchvision.utils.save_image(visuals, f"Output/{exp_name}/evaluate/{uid}.png", nrow=8, padding=0)
# save outputs to .pt
torch.save(outputs.detach().cpu(), f"Output/{exp_name}/evaluate/{uid}.pt")
GenDepths = (outputs[:, 0:1].cpu().numpy() * 0.5 + 0.5) * (far - near) + near
GenDepths[GenDepths <= near] = 0
GenDepths[GenDepths >= far] = 0
GenDepths_ori = GenDepths.copy()
for i in range(GenDepths.shape[0]-1):
GenDepths[i+1] = np.where(GenDepths_ori[i+1] < GenDepths_ori[i], 0, GenDepths_ori[i+1])
GenNormals = F.normalize(outputs[:, 1:4], dim=1).cpu().numpy()
GenColors = (outputs[:, 4:7].cpu().numpy() * 0.5 + 0.5)
gen_pts, gen_normals, gen_colors = xray_to_pcd(GenDepths, GenNormals, GenColors)
gen_pts = gen_pts - np.mean(gen_pts, axis=0)
pcd_gen = o3d.geometry.PointCloud()
pcd_gen.points = o3d.utility.Vector3dVector(gen_pts)
pcd_gen.normals = o3d.utility.Vector3dVector(gen_normals)
pcd_gen.colors = o3d.utility.Vector3dVector(gen_colors[..., :3])
gt_path = image_path.replace("images", "xrays").replace(".png", ".npz")
xray = load_xray(gt_path)[:8]
GtDepths = xray[:, 0:1]
GtNormals = xray[:, 1:4]
GtColors = xray[:, 4:7]
gt_pts, gt_normals, gt_colors = xray_to_pcd(GtDepths, GtNormals, GtColors)
gt_pts = gt_pts - np.mean(gt_pts, axis=0)
pcd_gt = o3d.geometry.PointCloud()
pcd_gt.points = o3d.utility.Vector3dVector(gt_pts)
pcd_gt.normals = o3d.utility.Vector3dVector(gt_normals)
pcd_gt.colors = o3d.utility.Vector3dVector(gt_colors)
# normalize gt_pts and gen_pts
chamfer_distance, f_score = chamfer_distance_and_f_score(gt_pts, gen_pts, threshold=0.01)
all_chamfer_distance += [chamfer_distance]
all_f_score += [f_score]
# save
image.save(f"Output/{exp_name}/evaluate/{uid}.png")
o3d.io.write_point_cloud(f"Output/{exp_name}/evaluate/{uid}_prd.ply", pcd_gen)
o3d.io.write_point_cloud(f"Output/{exp_name}/evaluate/{uid}_gt.ply", pcd_gt)
progress_bar.set_postfix({"CD": np.mean(all_chamfer_distance),
"[email protected]": np.mean(all_f_score)})
progress_bar.update(1)