File size: 4,390 Bytes
3894c45 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 |
#!/usr/bin/env python
# coding: utf-8
import pathlib
import torch
import yaml
import sys
import os
from math import pi
from PIL import Image
from munch import Munch
from argparse import ArgumentParser as AP
from torchvision.transforms import ToPILImage, ToTensor
p_mod = str(pathlib.Path('.').absolute())
sys.path.append(p_mod.replace("/scripts", ""))
from data.base_dataset import get_transform
from networks import create_model
device='cuda' if torch.cuda.is_available() else 'cpu'
def printProgressBar(i, max, postText):
n_bar = 20 # size of progress bar
j = i / max
sys.stdout.write('\r')
sys.stdout.write(f"[{'=' * int(n_bar * j):{n_bar}s}] {int(100 * j)}% {postText}")
sys.stdout.flush()
def inference(model, opt, A_path, phi):
t_phi = torch.tensor(phi)
A_img = Image.open(A_path).convert('RGB')
A = get_transform(opt, convert=False)(A_img)
img_real = (((ToTensor()(A)) * 2) - 1).unsqueeze(0)
img_fake = model.forward(img_real.to(device), t_phi.to(device))
return ToPILImage()((img_fake[0].cpu() + 1) / 2)
def main(cmdline):
if cmdline.checkpoint is None:
# Load names of directories inside /logs
p = pathlib.Path('./logs')
list_run_id = [x.name for x in p.iterdir() if x.is_dir()]
RUN_ID = list_run_id[0]
root_dir = os.path.join('logs', RUN_ID, 'tensorboard', 'default', 'version_0')
p = pathlib.Path(root_dir + '/checkpoints')
# Load a list of checkpoints, use the last one by default
list_checkpoint = [x.name for x in p.iterdir() if 'iter' in x.name]
list_checkpoint.sort(reverse=True, key=lambda x: int(x.split('_')[1].split('.pth')[0]))
CHECKPOINT = list_checkpoint[0]
else:
RUN_ID = os.path.basename(cmdline.checkpoint.split("/tensorboard")[0])
root_dir = os.path.dirname(cmdline.checkpoint.split("/checkpoints")[0])
CHECKPOINT = os.path.basename(cmdline.checkpoint.split('checkpoints/')[1])
print(f"Load checkpoint {CHECKPOINT} from {RUN_ID}")
# Load parameters
with open(os.path.join(root_dir, 'hparams.yaml')) as cfg_file:
opt = Munch(yaml.safe_load(cfg_file))
opt.no_flip = True
# Load parameters to the model, load the checkpoint
model = create_model(opt)
model = model.load_from_checkpoint(os.path.join(root_dir, 'checkpoints', CHECKPOINT))
# Transfer the model to the GPU
model.to(device)
# Load paths of all files contained in /Day
p = pathlib.Path(cmdline.load_path)
dataset_paths = [str(x.relative_to(cmdline.load_path)) for x in p.iterdir()]
dataset_paths.sort()
# Load only files that contained the given string
sequence_name = []
if cmdline.sequence is not None:
for file in dataset_paths:
if cmdline.sequence in file:
sequence_name.append(file)
else:
sequence_name = dataset_paths
# Create directory if it doesn't exist
os.makedirs(cmdline.save_path, exist_ok=True)
i = 0
for path_img in sequence_name:
printProgressBar(i, len(sequence_name), path_img)
# Loop over phi values from 0 to 2pi with increments of 0.2
for phi in torch.arange(0, 2 * pi, 0.2):
# Forward our image into the model with the specified ɸ
out_img = inference(model, opt, os.path.join(cmdline.load_path, path_img), phi)
# Saving the generated image with phi in the filename
save_path = os.path.join(cmdline.save_path, f"{os.path.splitext(os.path.basename(path_img))[0]}_phi_{phi:.1f}.png")
out_img.save(save_path)
i += 1
if __name__ == '__main__':
ap = AP()
ap.add_argument('--load_path', default='/datasets/waymo_comogan/val/sunny/Day/', type=str, help='Set a path to load the dataset to translate')
ap.add_argument('--save_path', default='/CoMoGan/images/', type=str, help='Set a path to save the dataset')
ap.add_argument('--sequence', default=None, type=str, help='Set a sequence, will only use the image that contained the string specified')
ap.add_argument('--checkpoint', default=None, type=str, help='Set a path to the checkpoint that you want to use')
ap.add_argument('--phi', default=0.0, type=float, help='Choose the angle of the sun 𝜙 between [0,2𝜋], which maps to a sun elevation ∈ [+30◦,−40◦]')
main(ap.parse_args())
print("\n")
|