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""" utils
"""
import os
import torch
import numpy as np
def load_checkpoint(fpath, model):
print("loading checkpoint... {}".format(fpath))
ckpt = torch.load(fpath, map_location="cpu")["model"]
load_dict = {}
for k, v in ckpt.items():
if k.startswith("module."):
k_ = k.replace("module.", "")
load_dict[k_] = v
else:
load_dict[k] = v
model.load_state_dict(load_dict)
print("loading checkpoint... / done")
return model
def compute_normal_error(pred_norm, gt_norm):
pred_error = torch.cosine_similarity(pred_norm, gt_norm, dim=1)
pred_error = torch.clamp(pred_error, min=-1.0, max=1.0)
pred_error = torch.acos(pred_error) * 180.0 / np.pi
pred_error = pred_error.unsqueeze(1) # (B, 1, H, W)
return pred_error
def compute_normal_metrics(total_normal_errors):
total_normal_errors = total_normal_errors.detach().cpu().numpy()
num_pixels = total_normal_errors.shape[0]
metrics = {
"mean": np.average(total_normal_errors),
"median": np.median(total_normal_errors),
"rmse": np.sqrt(np.sum(total_normal_errors * total_normal_errors) / num_pixels),
"a1": 100.0 * (np.sum(total_normal_errors < 5) / num_pixels),
"a2": 100.0 * (np.sum(total_normal_errors < 7.5) / num_pixels),
"a3": 100.0 * (np.sum(total_normal_errors < 11.25) / num_pixels),
"a4": 100.0 * (np.sum(total_normal_errors < 22.5) / num_pixels),
"a5": 100.0 * (np.sum(total_normal_errors < 30) / num_pixels),
}
return metrics
def pad_input(orig_H, orig_W):
if orig_W % 32 == 0:
l = 0
r = 0
else:
new_W = 32 * ((orig_W // 32) + 1)
l = (new_W - orig_W) // 2
r = (new_W - orig_W) - l
if orig_H % 32 == 0:
t = 0
b = 0
else:
new_H = 32 * ((orig_H // 32) + 1)
t = (new_H - orig_H) // 2
b = (new_H - orig_H) - t
return l, r, t, b
def get_intrins_from_fov(new_fov, H, W, device):
# NOTE: top-left pixel should be (0,0)
if W >= H:
new_fu = (W / 2.0) / np.tan(np.deg2rad(new_fov / 2.0))
new_fv = (W / 2.0) / np.tan(np.deg2rad(new_fov / 2.0))
else:
new_fu = (H / 2.0) / np.tan(np.deg2rad(new_fov / 2.0))
new_fv = (H / 2.0) / np.tan(np.deg2rad(new_fov / 2.0))
new_cu = (W / 2.0) - 0.5
new_cv = (H / 2.0) - 0.5
new_intrins = torch.tensor(
[[new_fu, 0, new_cu], [0, new_fv, new_cv], [0, 0, 1]],
dtype=torch.float32,
device=device,
)
return new_intrins
def get_intrins_from_txt(intrins_path, device):
# NOTE: top-left pixel should be (0,0)
with open(intrins_path, "r") as f:
intrins_ = f.readlines()[0].split()[0].split(",")
intrins_ = [float(i) for i in intrins_]
fx, fy, cx, cy = intrins_
intrins = torch.tensor(
[[fx, 0, cx], [0, fy, cy], [0, 0, 1]], dtype=torch.float32, device=device
)
return intrins
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