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evaluate_depth.py
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import os
import time
import torch
import torch.nn.functional as F
import numpy as np
from PIL import Image
from glob import glob
from dataloader.depth import augmentation as transforms
from dataloader.depth.datasets import ScannetDataset, DemonDataset
from loss.depth_loss import compute_errors
from utils.utils import InputPadder
from utils.visualization import viz_depth_tensor
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
IMAGENET_MEAN = [0.485, 0.456, 0.406]
IMAGENET_STD = [0.229, 0.224, 0.225]
@torch.no_grad()
def validate_scannet(model,
padding_factor=16,
inference_size=None,
attn_type='swin',
attn_splits_list=None,
prop_radius_list=None,
num_reg_refine=1,
num_depth_candidates=64,
count_time=False,
eval_min_depth=0.5,
eval_max_depth=10,
min_depth=0.5,
max_depth=10,
save_vis_depth=False,
save_dir=None,
):
model.eval()
val_transform_list = [transforms.ToTensor(),
transforms.Normalize(mean=IMAGENET_MEAN, std=IMAGENET_STD)
]
val_transform = transforms.Compose(val_transform_list)
val_dataset = ScannetDataset(transforms=val_transform,
mode='test',
)
num_samples = len(val_dataset)
print('=> %d samples found in the validation set' % num_samples)
error_names = ['abs_rel', 'sq_rel', 'rmse', 'rmse_log', 'a1', 'a2', 'a3']
error_sum = [0.] * len(error_names)
if count_time:
total_time = 0
num_runs = 100
valid_samples = 0
if save_vis_depth:
assert save_dir is not None
if not os.path.exists(save_dir):
os.makedirs(save_dir, exist_ok=True)
for i, sample in enumerate(val_dataset):
if i % 500 == 0:
print('=> Validating %d/%d' % (i, num_samples))
img_ref = sample['img_ref'].to(device).unsqueeze(0) # [1, 3, H, W]
img_tgt = sample['img_tgt'].to(device).unsqueeze(0) # [1, 3, H, W]
intrinsics = sample['intrinsics'].to(device).unsqueeze(0) # [1, 3, 3]
pose = sample['pose'].to(device).unsqueeze(0) # [1, 4, 4]
gt_depth = sample['depth'].to(device) # [H, W]
if inference_size is None:
padder = InputPadder(img_ref.shape, padding_factor=padding_factor, mode='kitti')
img_ref, img_tgt = padder.pad(img_ref, img_tgt)
else:
ori_size = img_ref.shape[-2:]
img_ref = F.interpolate(img_ref, size=inference_size, mode='bilinear',
align_corners=True)
img_tgt = F.interpolate(img_tgt, size=inference_size, mode='bilinear',
align_corners=True)
mask = (gt_depth > eval_min_depth) & (gt_depth < eval_max_depth)
# only evaluate on valid gt data
mask = mask & (sample['valid'].to(device) > 0.5)
if not mask.any():
continue
valid_samples += 1
if count_time and i >= 5:
torch.cuda.synchronize()
time_start = time.perf_counter()
with torch.no_grad():
pred_depth = model(img_ref, img_tgt,
attn_type=attn_type,
attn_splits_list=attn_splits_list,
prop_radius_list=prop_radius_list,
num_reg_refine=num_reg_refine,
intrinsics=intrinsics,
pose=pose,
min_depth=1. / max_depth,
max_depth=1. / min_depth,
num_depth_candidates=num_depth_candidates,
task='depth',
)['flow_preds'][-1] # [1, H, W]
if count_time and i >= 5:
torch.cuda.synchronize()
total_time += time.perf_counter() - time_start
if i >= num_runs + 4:
break
# remove padding
if inference_size is None:
pred_depth = padder.unpad(pred_depth)[0] # [H, W]
else:
# resize back
pred_depth = F.interpolate(pred_depth.unsqueeze(1), size=ori_size, mode='bilinear',
align_corners=True).squeeze(1)[0] # [H, W]
# NOTE: no scale depth magnitude when resize
if save_vis_depth:
filename = os.path.join(save_dir, '%04d_depth_pred.png' % valid_samples)
viz_inv_depth = viz_depth_tensor(1. / pred_depth.cpu(),
return_numpy=True,
colormap='plasma') # [H, W, 3] uint8
Image.fromarray(viz_inv_depth).save(filename)
gt_depth = gt_depth.cpu().numpy()
pred_depth = pred_depth.cpu().numpy()
mask = mask.cpu().numpy()
metrics = list(compute_errors(gt_depth[mask], pred_depth[mask]))
error_sum = [error_sum[i] + metrics[i] for i in range(len(error_sum))]
error_mean = [error / num_samples for error in error_sum]
results = dict(zip(error_names, error_mean))
if count_time:
print('Time: %.6fs' % (total_time / num_runs))
return results
@torch.no_grad()
def validate_demon(model,
padding_factor=16,
inference_size=None,
attn_type='swin',
attn_splits_list=None,
prop_radius_list=None,
num_reg_refine=1,
num_depth_candidates=64,
count_time=False,
eval_min_depth=0.5,
eval_max_depth=10,
min_depth=0.5,
max_depth=10,
save_vis_depth=False,
save_dir=None,
demon_split='rgbd',
debug=False,
):
model.eval()
val_transform_list = [transforms.ToTensor(),
transforms.Normalize(mean=IMAGENET_MEAN, std=IMAGENET_STD)
]
val_transform = transforms.Compose(val_transform_list)
val_dataset = DemonDataset(transforms=val_transform,
mode=demon_split + '_test',
)
num_samples = len(val_dataset)
print('=> %d samples found in the validation set' % num_samples)
error_names = ['abs_rel', 'sq_rel', 'rmse', 'rmse_log', 'a1', 'a2', 'a3']
error_sum = [0.] * len(error_names)
if count_time:
total_time = 0
num_runs = 100
valid_samples = 0
if save_vis_depth:
assert save_dir is not None
if not os.path.exists(save_dir):
os.makedirs(save_dir, exist_ok=True)
for i, sample in enumerate(val_dataset):
if debug and i > 10:
break
if i % 500 == 0:
print('=> Validating %d/%d' % (i, num_samples))
img_ref = sample['img_ref'].to(device).unsqueeze(0) # [1, 3, H, W]
img_tgt = sample['img_tgt'].to(device).unsqueeze(0) # [1, 3, H, W]
intrinsics = sample['intrinsics'].to(device).unsqueeze(0) # [1, 3, 3]
pose = sample['pose'].to(device).unsqueeze(0) # [1, 4, 4]
gt_depth = sample['depth'].to(device) # [H, W]
if inference_size is None:
padder = InputPadder(img_ref.shape, padding_factor=padding_factor, mode='kitti')
img_ref, img_tgt = padder.pad(img_ref, img_tgt)
else:
ori_size = img_ref.shape[-2:]
img_ref = F.interpolate(img_ref, size=inference_size, mode='bilinear',
align_corners=True)
img_tgt = F.interpolate(img_tgt, size=inference_size, mode='bilinear',
align_corners=True)
mask = (gt_depth > eval_min_depth) & (gt_depth < eval_max_depth)
# only evaluate on valid gt data
mask = mask & (sample['valid'].to(device) > 0.5)
if not mask.any():
continue
valid_samples += 1
if count_time and i >= 5:
torch.cuda.synchronize()
time_start = time.perf_counter()
with torch.no_grad():
pred_depth = model(img_ref, img_tgt,
attn_type=attn_type,
attn_splits_list=attn_splits_list,
prop_radius_list=prop_radius_list,
num_reg_refine=num_reg_refine,
intrinsics=intrinsics,
pose=pose,
min_depth=1. / max_depth,
max_depth=1. / min_depth,
num_depth_candidates=num_depth_candidates,
task='depth',
)['flow_preds'][-1] # [1, H, W]
if count_time and i >= 5:
torch.cuda.synchronize()
total_time += time.perf_counter() - time_start
if i >= num_runs + 4:
break
# remove padding
if inference_size is None:
pred_depth = padder.unpad(pred_depth)[0] # [H, W]
else:
# resize back
pred_depth = F.interpolate(pred_depth.unsqueeze(1), size=ori_size, mode='bilinear',
align_corners=True).squeeze(1)[0] # [H, W]
if save_vis_depth:
filename = os.path.join(save_dir, '%04d.png' % valid_samples)
viz_inv_depth = viz_depth_tensor(1. / pred_depth.cpu(),
return_numpy=True,
colormap='plasma') # [H, W, 3] uint8
Image.fromarray(viz_inv_depth).save(filename)
gt_depth = gt_depth.cpu().numpy()
pred_depth = pred_depth.cpu().numpy()
mask = mask.cpu().numpy()
metrics = list(compute_errors(gt_depth[mask], pred_depth[mask]))
error_sum = [error_sum[i] + metrics[i] for i in range(len(error_sum))]
error_mean = [error / num_samples for error in error_sum]
results = dict(zip(error_names, error_mean))
if count_time:
print('Time: %.6fs' % (total_time / num_runs))
return results
@torch.no_grad()
def inference_depth(model,
inference_dir=None,
output_path='output',
padding_factor=16,
inference_size=None,
attn_type='swin',
attn_splits_list=None,
prop_radius_list=None,
num_reg_refine=1,
num_depth_candidates=64,
min_depth=0.5,
max_depth=10,
depth_from_argmax=False,
pred_bidir_depth=False,
):
model.eval()
val_transform_list = [transforms.ToTensor(),
transforms.Normalize(mean=IMAGENET_MEAN, std=IMAGENET_STD)
]
val_transform = transforms.Compose(val_transform_list)
valid_samples = 0
fixed_inference_size = inference_size
if not os.path.exists(output_path):
os.makedirs(output_path)
# assume scannet dataset file structure
imgs = sorted(glob(os.path.join(inference_dir, 'color', '*.jpg')) +
glob(os.path.join(inference_dir, 'color', '*.png')))
poses = sorted(glob(os.path.join(inference_dir, 'pose', '*.txt')))
intrinsics_file = glob(os.path.join(inference_dir, 'intrinsic', '*.txt'))[0]
assert len(imgs) == len(poses)
num_samples = len(imgs)
for i in range(len(imgs) - 1):
if i % 50 == 0:
print('=> Predicting %d/%d' % (i, num_samples))
img_ref = np.array(Image.open(imgs[i]).convert('RGB')).astype(np.float32)
img_tgt = np.array(Image.open(imgs[i + 1]).convert('RGB')).astype(np.float32)
intrinsics = np.loadtxt(intrinsics_file).astype(np.float32).reshape((4, 4))[:3, :3] # [3, 3]
pose_ref = np.loadtxt(poses[i], delimiter=' ').astype(np.float32).reshape((4, 4))
pose_tgt = np.loadtxt(poses[i + 1], delimiter=' ').astype(np.float32).reshape((4, 4))
# relative pose
pose = np.linalg.inv(pose_tgt) @ pose_ref
sample = {'img_ref': img_ref,
'img_tgt': img_tgt,
'intrinsics': intrinsics,
'pose': pose,
}
sample = val_transform(sample)
img_ref = sample['img_ref'].to(device).unsqueeze(0) # [1, 3, H, W]
img_tgt = sample['img_tgt'].to(device).unsqueeze(0) # [1, 3, H, W]
intrinsics = sample['intrinsics'].to(device).unsqueeze(0) # [1, 3, 3]
pose = sample['pose'].to(device).unsqueeze(0) # [1, 4, 4]
nearest_size = [int(np.ceil(img_ref.size(-2) / padding_factor)) * padding_factor,
int(np.ceil(img_ref.size(-1) / padding_factor)) * padding_factor]
# resize to nearest size or specified size
inference_size = nearest_size if fixed_inference_size is None else fixed_inference_size
ori_size = img_ref.shape[-2:]
if inference_size[0] != ori_size[0] or inference_size[1] != ori_size[1]:
img_ref = F.interpolate(img_ref, size=inference_size, mode='bilinear',
align_corners=True)
img_tgt = F.interpolate(img_tgt, size=inference_size, mode='bilinear',
align_corners=True)
valid_samples += 1
with torch.no_grad():
pred_depth = model(img_ref, img_tgt,
attn_type=attn_type,
attn_splits_list=attn_splits_list,
prop_radius_list=prop_radius_list,
num_reg_refine=num_reg_refine,
intrinsics=intrinsics,
pose=pose,
min_depth=1. / max_depth,
max_depth=1. / min_depth,
num_depth_candidates=num_depth_candidates,
pred_bidir_depth=pred_bidir_depth,
depth_from_argmax=depth_from_argmax,
task='depth',
)['flow_preds'][-1] # [1, H, W]
# remove padding
if inference_size[0] != ori_size[0] or inference_size[1] != ori_size[1]:
# resize back
pred_depth = F.interpolate(pred_depth.unsqueeze(1), size=ori_size, mode='bilinear',
align_corners=True).squeeze(1) # [1, H, W]
pr_depth = pred_depth[0]
filename = os.path.join(output_path, os.path.basename(imgs[i])[:-4] + '.png')
viz_inv_depth = viz_depth_tensor(1. / pr_depth.cpu(),
return_numpy=True) # [H, W, 3] uint8
Image.fromarray(viz_inv_depth).save(filename)
if pred_bidir_depth:
assert pred_depth.size(0) == 2
pr_depth_bwd = pred_depth[1]
filename = os.path.join(output_path, os.path.basename(imgs[i])[:-4] + '_bwd.png')
viz_inv_depth = viz_depth_tensor(1. / pr_depth_bwd.cpu(),
return_numpy=True) # [H, W, 3] uint8
Image.fromarray(viz_inv_depth).save(filename)
print('Done!')