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evaluate_depth.py
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evaluate_depth.py
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from __future__ import absolute_import, division, print_function
import os
import cv2
import numpy as np
import torch
from torch.utils.data import DataLoader
from layers import disp_to_depth
from utils import readlines, compute_errors, batch_post_process_disparity
from uncertainty_utils import save_uncertainty_visualization, save_visualization
from uncertainty_metrics import compute_errors_uncertainty
from options import MonodepthOptions
import datasets
import networks
cv2.setNumThreads(0) # This speeds up evaluation 5x on our unix systems (OpenCV 3.3.1)
splits_dir = os.path.join(os.path.dirname(__file__), "splits")
# Models which were trained with stereo supervision were trained with a nominal
# baseline of 0.1 units. The KITTI rig has a baseline of 54cm. Therefore,
# to convert our stereo predictions to real-world scale we multiply our depths by 5.4.
STEREO_SCALE_FACTOR = 5.4
def update_dict(global_dict, new_dict):
for key, value in new_dict.items():
if key in global_dict:
global_dict[key].append(value)
else:
global_dict[key] = [value]
def evaluate(opt):
"""Evaluates a pretrained model using a specified test set
"""
MIN_DEPTH = 1e-3
MAX_DEPTH = 80
assert sum((opt.eval_mono, opt.eval_stereo)) == 1, \
"Please choose mono or stereo evaluation by setting either --eval_mono or --eval_stereo"
if opt.ext_disp_to_eval is None:
opt.load_weights_folder = os.path.expanduser(opt.load_weights_folder)
assert os.path.isdir(opt.load_weights_folder), \
"Cannot find a folder at {}".format(opt.load_weights_folder)
print("-> Loading weights from {}".format(opt.load_weights_folder))
filenames = readlines(os.path.join(splits_dir, opt.eval_split, "test_files.txt"))
encoder_path = os.path.join(opt.load_weights_folder, "encoder.pth")
decoder_path = os.path.join(opt.load_weights_folder, "depth.pth")
encoder_dict = torch.load(encoder_path)
img_ext = '.png' # if opt.png else '.jpg'
dataset = datasets.KITTIRAWDataset(opt.data_path, filenames,
encoder_dict['height'], encoder_dict['width'],
[0], 4, is_train=False, img_ext=img_ext)
dataloader = DataLoader(dataset, 16, shuffle=False, num_workers=opt.num_workers,
pin_memory=True, drop_last=False)
encoder = networks.ResnetEncoder(opt.num_layers, False)
depth_decoder = networks.DepthDecoder(
encoder.num_ch_enc, opt.scales, uncertainty=opt.uncertainty, uncert_act=opt.uncert_act)
model_dict = encoder.state_dict()
encoder.load_state_dict({k: v for k, v in encoder_dict.items() if k in model_dict})
depth_decoder.load_state_dict(torch.load(decoder_path))
encoder.cuda()
encoder.eval()
depth_decoder.cuda()
depth_decoder.eval()
pred_disps = []
if opt.uncertainty:
pred_std = []
if opt.save_visu:
os.makedirs(opt.visu_dir, exist_ok=True)
print("-> Computing predictions with size {}x{}".format(
encoder_dict['width'], encoder_dict['height']))
with torch.no_grad():
for data in dataloader:
input_color = data[("color", 0, 0)].cuda()
if opt.post_process:
# Post-processed results require each image to have two forward passes
input_color = torch.cat((input_color, torch.flip(input_color, [3])), 0)
output = depth_decoder(encoder(input_color))
pred_disp, _ = disp_to_depth(output[("disp", 0)], opt.min_depth, opt.max_depth)
pred_disp = pred_disp.cpu()[:, 0].numpy()
if opt.post_process:
N = pred_disp.shape[0] // 2
pred_disp = batch_post_process_disparity(pred_disp[:N], pred_disp[N:, :, ::-1])
pred_disps.append(pred_disp)
if opt.uncertainty:
std = output[("uncert", 0)]
std = std[:, 0].cpu().numpy()
if opt.uncert_as_a_fraction_of_depth:
std /= pred_disp
pred_std.append(std)
pred_disps = np.concatenate(pred_disps)
if opt.uncertainty:
pred_stds = np.concatenate(pred_std)
else:
# Load predictions from file
print("-> Loading predictions from {}".format(opt.ext_disp_to_eval))
pred_disps = np.load(opt.ext_disp_to_eval)
if opt.eval_eigen_to_benchmark:
eigen_to_benchmark_ids = np.load(
os.path.join(splits_dir, "benchmark", "eigen_to_benchmark_ids.npy"))
pred_disps = pred_disps[eigen_to_benchmark_ids]
if opt.save_pred_disps:
output_path = os.path.join(
opt.load_weights_folder, "disps_{}_split.npy".format(opt.eval_split))
print("-> Saving predicted disparities to ", output_path)
np.save(output_path, pred_disps)
if opt.no_eval:
print("-> Evaluation disabled. Done.")
quit()
elif opt.eval_split == 'benchmark':
save_dir = os.path.join(opt.load_weights_folder, "benchmark_predictions")
print("-> Saving out benchmark predictions to {}".format(save_dir))
if not os.path.exists(save_dir):
os.makedirs(save_dir)
for idx in range(len(pred_disps)):
disp_resized = cv2.resize(pred_disps[idx], (1216, 352))
depth = STEREO_SCALE_FACTOR / disp_resized
depth = np.clip(depth, 0, 80)
depth = np.uint16(depth * 256)
save_path = os.path.join(save_dir, "{:010d}.png".format(idx))
cv2.imwrite(save_path, depth)
print("-> No ground truth is available for the KITTI benchmark, so not evaluating. Done.")
quit()
gt_path = os.path.join(splits_dir, opt.eval_split, "gt_depths.npz")
gt_depths = np.load(gt_path, fix_imports=True, encoding='latin1', allow_pickle=True)["data"]
print("-> Evaluating")
if opt.eval_stereo:
print(" Stereo evaluation - "
"disabling median scaling, scaling by {}".format(STEREO_SCALE_FACTOR))
opt.disable_median_scaling = True
opt.pred_depth_scale_factor = STEREO_SCALE_FACTOR
else:
print(" Mono evaluation - using median scaling")
errors = {}
ratios = []
for i in range(pred_disps.shape[0]):
gt_depth = gt_depths[i]
gt_height, gt_width = gt_depth.shape[:2]
pred_disp = pred_disps[i]
pred_disp = cv2.resize(pred_disp, (gt_width, gt_height))
pred_depth = 1 / pred_disp
if opt.uncertainty:
pred_std = cv2.resize(pred_stds[i], (gt_width, gt_height))
if opt.eval_split == "eigen":
mask = np.logical_and(gt_depth > MIN_DEPTH, gt_depth < MAX_DEPTH)
crop = np.array([0.40810811 * gt_height, 0.99189189 * gt_height,
0.03594771 * gt_width, 0.96405229 * gt_width]).astype(np.int32)
crop_mask = np.zeros(mask.shape)
crop_mask[crop[0]:crop[1], crop[2]:crop[3]] = 1
mask = np.logical_and(mask, crop_mask)
else:
mask = gt_depth > 0
if opt.save_visu:
if opt.uncertainty:
save_uncertainty_visualization(gt_depth, pred_depth, pred_std, mask, i, opt.visu_dir)
else:
save_visualization(gt_depth, 1/pred_disp, mask, i, opt.visu_dir)
pred_depth = pred_depth[mask]
gt_depth = gt_depth[mask]
if opt.uncertainty:
pred_std = pred_std[mask]
pred_depth *= opt.pred_depth_scale_factor
if not opt.disable_median_scaling:
ratio = np.median(gt_depth) / np.median(pred_depth)
ratios.append(ratio)
pred_depth *= ratio
if opt.uncertainty:
pred_std *= ratio
pred_depth[pred_depth < MIN_DEPTH] = MIN_DEPTH
pred_depth[pred_depth > MAX_DEPTH] = MAX_DEPTH
if opt.uncertainty:
update_dict(errors, compute_errors_uncertainty(gt_depth, pred_depth, pred_std))
# metrics_list = ("abs_rel", "sq_rel", "rmse", "rmse_log", "a1", "a2", "a3", 'mean_std', 'abs_uncert', 'precision', 'recall', 'f1')
else:
update_dict(errors, compute_errors(gt_depth, pred_depth))
# errors.append(compute_errors(gt_depth, pred_depth))
# metrics_list = ("abs_rel", "sq_rel", "rmse", "rmse_log", "a1", "a2", "a3")
if not opt.disable_median_scaling:
ratios = np.array(ratios)
med = np.median(ratios)
print(" Scaling ratios | med: {:0.3f} | std: {:0.3f}".format(med, np.std(ratios / med)))
mean_errors = {key: np.array(value).mean(0) for key, value in errors.items()}
print("\n " + ("{:>8} | " * len(mean_errors)).format(*mean_errors.keys()))
print(("&{: 8.3f} " * len(mean_errors)).format(*mean_errors.values()) + "\\\\")
print("\n-> Done!")
if __name__ == "__main__":
options = MonodepthOptions()
evaluate(options.parse())