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452723c | 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 | # Copyright (c) 2020, Roy Or-El. All rights reserved.
#
# This work is licensed under the Creative Commons
# Attribution-NonCommercial-ShareAlike 4.0 International License.
# To view a copy of this license, visit
# http://creativecommons.org/licenses/by-nc-sa/4.0/ or send a letter to
# Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
# This code is a modification of the main.py file
# from the https://github.com/chenxi116/DeepLabv3.pytorch repository
import os
import argparse
import requests
import numpy as np
from tqdm import tqdm
from pathlib import Path
import torch
from PIL import Image
from src.models import deeplab
from src.data.data_loader import CelebASegmentation
from src.utils.deeplab_util import download_file
# python -m src.run_deeplab --resolution 512 --dataset_root ./images/images512x512 --output_dir ./segmaps/segmaps512x512
parser = argparse.ArgumentParser()
parser.add_argument('--resolution', type=int, default=256,
help='segmentation output size')
parser.add_argument('--dataset_root', type=str, default="./images/images512x512",
help='Path to images dir')
parser.add_argument('--output_dir', type=str, default="./segmaps/segmaps512x512",
help='Path to output segmap dir')
# parser.add_argument('--workers', type=int, default=4,
# help='number of data loading workers')
args = parser.parse_args()
resnet_file_spec = dict(file_url='https://drive.google.com/uc?id=1oRGgrI4KNdefbWVpw0rRkEP1gbJIRokM', file_path='deeplab_model/R-101-GN-WS.pth.tar', file_size=178260167, file_md5='aa48cc3d3ba3b7ac357c1489b169eb32')
deeplab_file_spec = dict(file_url='https://drive.google.com/uc?id=1w2XjDywFr2NjuUWaLQDRktH7VwIfuNlY', file_path='deeplab_model/deeplab_model.pth', file_size=464446305, file_md5='8e8345b1b9d95e02780f9bed76cc0293')
def main():
resolution = args.resolution
assert torch.cuda.is_available()
torch.backends.cudnn.benchmark = True
model_fname = 'deeplab_model/deeplab_model.pth'
# dataset_root = 'ffhq_aging{}x{}'.format(resolution,resolution)
assert os.path.isdir(args.dataset_root)
# dataset = CelebASegmentation(args.dataset_root)
dataset = CelebASegmentation(args.dataset_root, crop_size=513) # ~!
if not os.path.isfile(resnet_file_spec['file_path']):
print('Downloading backbone Resnet Model parameters')
with requests.Session() as session:
download_file(session, resnet_file_spec)
print('Done!')
model = getattr(deeplab, 'resnet101')(
pretrained=True,
num_classes=len(dataset.CLASSES),
num_groups=32,
weight_std=True,
beta=False)
model = model.cuda()
model.eval()
if not os.path.isfile(deeplab_file_spec['file_path']):
print('Downloading DeeplabV3 Model parameters')
with requests.Session() as session:
download_file(session, deeplab_file_spec)
print('Done!')
checkpoint = torch.load(model_fname)
state_dict = {k[7:]: v for k, v in checkpoint['state_dict'].items() if 'tracked' not in k}
model.load_state_dict(state_dict)
for i in tqdm(range(len(dataset))):
inputs=dataset[i]
inputs = inputs.cuda()
outputs = model(inputs.unsqueeze(0))
_, pred = torch.max(outputs, 1)
pred = pred.data.cpu().numpy().squeeze().astype(np.uint8)
mask_pred = Image.fromarray(pred)
mask_pred=mask_pred.resize((resolution,resolution), Image.NEAREST)
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
savename = os.path.join(args.output_dir, f"{Path(dataset.images[i]).stem}.png")
mask_pred.save(savename)
# imname = os.path.basename(dataset.images[i])
# try:
# mask_pred.save(dataset.images[i].replace(imname,'parsings/'+imname[:-4]+'.png'))
# except FileNotFoundError:
# os.makedirs(os.path.join(os.path.dirname(dataset.images[i]),'parsings'))
# mask_pred.save(dataset.images[i].replace(imname,'parsings/'+imname[:-4]+'.png'))
print('processed {0}/{1} images'.format(i + 1, len(dataset)))
if __name__ == "__main__":
main()
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