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eval.py
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eval.py
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import argparse
import numpy as np
import torch
import random
import time
from tqdm import tqdm
from torch.utils.data import DataLoader
from models import Tandem
from models.datasets import MVSDataset
from models.module import eval_errors
from models.utils.helpers import tensor2numpy, to_device
from models.utils import epoch_end_mean
import cv2
import pickle
parser = argparse.ArgumentParser()
parser.add_argument("ckpt", type=str, help="Path to pytorch lightning ckpt.")
parser.add_argument("--data_dir", help="Path to replica data.", type=str, default='data')
parser.add_argument("--num_save_images", help="Number of images to be saved for viz.", type=int, default=10)
parser.add_argument("--seed", help="Seed.", type=int, default=1)
parser.add_argument("--device", help="Torch device.", type=str, choices=('cpu', 'cuda'), default='cuda')
parser.add_argument("--batch_size", help="Batch size.", type=int, default=4)
parser.add_argument("--num_workers", help="Number of workers.", type=int, default=4)
parser.add_argument("--tuples_ext", help="Tuples Extension.", type=str, default="dso_gs")
parser.add_argument("--pose_ext", help="Pose Extension.", type=str, default="dso", choices=("dso", "gt"))
parser.add_argument("--height", help="Image height.", type=int, default=480)
parser.add_argument("--width", help="Image width.", type=int, default=640)
parser.add_argument("--depth_min", help="Depth minimum.", type=float, default=0.01)
parser.add_argument("--depth_max", help="Depth maximum.", type=float, default=10.0)
parser.add_argument("--split", help="Split file", type=str, default="val")
def main(args: argparse.Namespace):
# Seed
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
device = torch.device(args.device)
model = Tandem.load_from_checkpoint(args.ckpt) # type: Tandem
model = model.to(device)
model.eval()
outputs_to_dict = model.cva_mvsnet.outputs_to_dict
dataset = MVSDataset(
root_dir=args.data_dir,
split=args.split,
pose_ext=args.pose_ext,
tuples_ext=args.tuples_ext,
ignore_pose_scale=args.pose_ext == "gt",
height=args.height,
width=args.width,
tuples_default_flag=False,
tuples_default_frame_num=-1,
tuples_default_frame_dist=-1,
depth_min=args.depth_min,
depth_max=args.depth_max,
dtype="float32",
transform=None,
)
loader = DataLoader(dataset, batch_size=args.batch_size, shuffle=False, drop_last=False, num_workers=6)
errors = []
if args.num_save_images > 0:
image_save_ids = tuple((np.arange(args.num_save_images) * (len(dataset) // args.num_save_images)).tolist())
else:
image_save_ids = tuple()
images = []
start = time.time()
num_processed = 0
try:
with torch.no_grad():
for batch_idx, batch in enumerate(tqdm(loader)):
batch = to_device(batch, device=device)
outputs = outputs_to_dict(model(batch))
errors.append(eval_errors(outputs=outputs, batch=batch))
num_processed += args.batch_size
for i, idx in enumerate(range(batch_idx * args.batch_size, (batch_idx + 1) * args.batch_size)):
if idx in image_save_ids:
gt = tensor2numpy(batch['depth']['stage3'][i]).astype(np.float64) / args.depth_max
est = tensor2numpy(outputs['stage3']['depth'][i]).astype(np.float64) / args.depth_max
images.append(np.concatenate((gt, est), axis=0))
except KeyboardInterrupt:
pass
elapsed = time.time() - start
fps = num_processed / elapsed
ms_per_frame = 1000.0 / fps
errors = epoch_end_mean(errors)
errors = tensor2numpy(errors)
# Save errors
with open(args.ckpt.rstrip('.ckpt') + '.pkl', 'wb') as fp:
pickle.dump(obj=errors, file=fp)
# Save images
if len(images) > 0:
image = np.concatenate(images, axis=1)
if not np.all((image >= 0) & (image <= 1)):
print(f"Image out of bounds: min/max/median = {np.amin(image)}/{np.amax(image)}/{np.median(image)}")
image = np.clip(image, 0, 1)
image = (image * float(np.iinfo(np.uint16).max)).astype(np.uint16)
cv2.imwrite(args.ckpt.rstrip('.ckpt') + '.png', image)
# Save output to file too
with open(args.ckpt.rstrip('.ckpt') + '.txt', 'w') as fp:
# Stage Table
error_names = ('abs_rel', 'abs', 'sq_rel', 'rmse', 'rmse_log', 'a1', 'a2', 'a3')
header = ' ' * 14 + ("{:>8s} " * len(error_names)).format(*error_names)
fmt_str = "{:>11s}: " + "{:8.3f} " * len(error_names)
print(header, file=fp)
for stage in errors:
err = tuple(errors[stage][n].item() for n in error_names)
print(fmt_str.format(stage.upper(), *err), file=fp)
# Performance
print(f"Performance: {fps:5.2f} FPS, {int(ms_per_frame):5d} ms per image.", file=fp)
# Eigen String
print(
f"Eigen et. al (delta <1.25, <1.25**2, <1.25**3): {errors['stage3']['d1'].item()} {errors['stage3']['d2'].item()} {errors['stage3']['d3'].item()}",
file=fp)
# Google Sheets String
name = args.ckpt.rstrip(".ckpt")
header = " " * (len(name) + 3)
header += ("{:>8s} " * (len(error_names) + 5)).format(
*error_names, 'width', 'height', 'd_min', 'd_max', 'seed')[:-3]
fmt_str = "{:>10s} " + "{:8.4f} " * len(error_names) + "{:8d} {:8d} {:8.4f} {:8.4f} {:8d}"
print("\nPaste last line into Google Sheets", file=fp)
print("" + header, file=fp)
err = tuple(errors['stage3'][n].item() for n in error_names)
print(fmt_str.format(name, *err, args.width, args.height, args.depth_min, args.depth_max, args.seed), file=fp)
if __name__ == "__main__":
main(parser.parse_args())