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updated inference for DFPE npy input #42

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updated inference for DFPE npy input #42

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30 changes: 24 additions & 6 deletions inference.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,7 +6,7 @@
import numpy as np
from PIL import Image
from tqdm import tqdm
from scipy.ndimage.filters import maximum_filter
from scipy.ndimage import maximum_filter
from shapely.geometry import Polygon

import torch
Expand Down Expand Up @@ -76,9 +76,12 @@ def inference(net, x, device, flip=False, rotate=[], visualize=False,
# Network feedforward (with testing augmentation)
x, aug_type = augment(x, flip, rotate)
y_bon_, y_cor_ = net(x.to(device))
output_npy = np.vstack((y_bon_[0], y_cor_[0, : :]))

y_bon_ = augment_undo(y_bon_.cpu(), aug_type).mean(0)
y_cor_ = augment_undo(torch.sigmoid(y_cor_).cpu(), aug_type).mean(0)

# output_npy = np.vstack((y_bon_[0], y_cor_[0, : :]))
# Visualize raw model output
if visualize:
vis_out = visualize_a_data(x[0],
Expand Down Expand Up @@ -138,18 +141,20 @@ def inference(net, x, device, flip=False, rotate=[], visualize=False,
cor_id[:, 0] /= W
cor_id[:, 1] /= H

return cor_id, z0, z1, vis_out
return cor_id, z0, z1, vis_out, output_npy


if __name__ == '__main__':

parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--pth', required=True,
help='path to load saved checkpoint.')
parser.add_argument('--img_glob', required=True,
parser.add_argument('--img_glob', required=False,
help='NOTE: Remeber to quote your glob path. '
'All the given images are assumed to be aligned'
'or you should use preporcess.py to do so.')
parser.add_argument('--dataset_dir', required=False,
help='NOTE: Remeber to quote your glob path.')
parser.add_argument('--output_dir', required=True)
parser.add_argument('--visualize', action='store_true')
# Augmentation related
Expand All @@ -171,7 +176,12 @@ def inference(net, x, device, flip=False, rotate=[], visualize=False,
args = parser.parse_args()

# Prepare image to processed
paths = sorted(glob.glob(args.img_glob))
# paths = sorted(glob.glob(args.img_glob))

with open(os.path.join(args.dataset_dir, 'vo_final/keyframe_list.txt'), 'r') as f:
kf_list = f.readlines()
paths = [os.path.join(args.dataset_dir, "rgb/", str(int(kf[:-1])) +".png") for kf in kf_list]

if len(paths) == 0:
print('no images found')
for path in paths:
Expand All @@ -182,11 +192,15 @@ def inference(net, x, device, flip=False, rotate=[], visualize=False,
print('Output directory %s not existed. Create one.' % args.output_dir)
os.makedirs(args.output_dir)
device = torch.device('cpu' if args.no_cuda else 'cuda')
device = torch.device('cpu')

# Loaded trained model
net = utils.load_trained_model(HorizonNet, args.pth).to(device)
net.eval()

npy_dir = os.path.join(os.path.dirname(args.output_dir), "vo_final/npy/")
if not os.path.isdir(npy_dir):
print('Output directory %s not existed. Create one.' % npy_dir)
os.makedirs(npy_dir)
# Inferencing
with torch.no_grad():
for i_path in tqdm(paths, desc='Inferencing'):
Expand All @@ -200,7 +214,7 @@ def inference(net, x, device, flip=False, rotate=[], visualize=False,
x = torch.FloatTensor([img_ori / 255])

# Inferenceing corners
cor_id, z0, z1, vis_out = inference(net=net, x=x, device=device,
cor_id, z0, z1, vis_out, output_npy = inference(net=net, x=x, device=device,
flip=args.flip, rotate=args.rotate,
visualize=args.visualize,
force_cuboid=args.force_cuboid,
Expand All @@ -215,6 +229,10 @@ def inference(net, x, device, flip=False, rotate=[], visualize=False,
'uv': [[float(u), float(v)] for u, v in cor_id],
}, f)

npy_filepath = os.path.join(npy_dir, k + '.npy')
np.save(npy_filepath, output_npy)
print (f'Saving npy file at {npy_filepath}')

if vis_out is not None:
vis_path = os.path.join(args.output_dir, k + '.raw.png')
vh, vw = vis_out.shape[:2]
Expand Down