/
demo_with_ade20k_ssn.py
134 lines (124 loc) · 6.68 KB
/
demo_with_ade20k_ssn.py
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
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
"""
Copyright (C) 2018 NVIDIA Corporation. All rights reserved.
Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
"""
from __future__ import print_function
import argparse
import os
import torch
import process_stylization_ade20k_ssn
from torch import nn
from photo_wct import PhotoWCT
from segmentation.dataset import round2nearest_multiple
from segmentation.models import ModelBuilder, SegmentationModule
from lib.nn import user_scattered_collate, async_copy_to
from lib.utils import as_numpy, mark_volatile
from scipy.misc import imread, imresize
import cv2
from torchvision import transforms
import numpy as np
parser = argparse.ArgumentParser(description='Photorealistic Image Stylization')
parser.add_argument('--model_path', help='folder to model path', default='baseline-resnet50_dilated8-ppm_bilinear_deepsup')
parser.add_argument('--suffix', default='_epoch_20.pth', help="which snapshot to load")
parser.add_argument('--arch_encoder', default='resnet50_dilated8', help="architecture of net_encoder")
parser.add_argument('--arch_decoder', default='ppm_bilinear_deepsup', help="architecture of net_decoder")
parser.add_argument('--fc_dim', default=2048, type=int, help='number of features between encoder and decoder')
parser.add_argument('--num_val', default=-1, type=int, help='number of images to evalutate')
parser.add_argument('--num_class', default=150, type=int, help='number of classes')
parser.add_argument('--batch_size', default=1, type=int, help='batchsize. current only supports 1')
parser.add_argument('--imgSize', default=[300, 400, 500, 600], nargs='+', type=int, help='list of input image sizes.' 'for multiscale testing, e.g. 300 400 500')
parser.add_argument('--imgMaxSize', default=1000, type=int, help='maximum input image size of long edge')
parser.add_argument('--padding_constant', default=8, type=int, help='maxmimum downsampling rate of the network')
parser.add_argument('--segm_downsampling_rate', default=8, type=int, help='downsampling rate of the segmentation label')
parser.add_argument('--gpu_id', default=0, type=int, help='gpu_id for evaluation')
parser.add_argument('--model', default='./PhotoWCTModels/photo_wct.pth', help='Path to the PhotoWCT model. These are provided by the PhotoWCT submodule, please use `git submodule update --init --recursive` to pull.')
parser.add_argument('--content_image_path', default="./images/content3.png")
parser.add_argument('--content_seg_path', default='./results/content3_seg.pgm')
parser.add_argument('--style_image_path', default='./images/style3.png')
parser.add_argument('--style_seg_path', default='./results/style3_seg.pgm')
parser.add_argument('--output_image_path', default='./results/example3.png')
parser.add_argument('--save_intermediate', action='store_true', default=False)
parser.add_argument('--fast', action='store_true', default=False)
parser.add_argument('--no_post', action='store_true', default=False)
parser.add_argument('--output_visualization', action='store_true', default=False)
parser.add_argument('--cuda', type=int, default=1, help='Enable CUDA.')
parser.add_argument('--label_mapping', type=str, default='ade20k_semantic_rel.npy')
args = parser.parse_args()
segReMapping = process_stylization_ade20k_ssn.SegReMapping(args.label_mapping)
# Absolute paths of segmentation model weights
SEG_NET_PATH = 'segmentation'
args.weights_encoder = os.path.join(SEG_NET_PATH,args.model_path, 'encoder' + args.suffix)
args.weights_decoder = os.path.join(SEG_NET_PATH,args.model_path, 'decoder' + args.suffix)
args.arch_encoder = 'resnet50_dilated8'
args.arch_decoder = 'ppm_bilinear_deepsup'
args.fc_dim = 2048
# Load semantic segmentation network module
builder = ModelBuilder()
net_encoder = builder.build_encoder(arch=args.arch_encoder, fc_dim=args.fc_dim, weights=args.weights_encoder)
net_decoder = builder.build_decoder(arch=args.arch_decoder, fc_dim=args.fc_dim, num_class=args.num_class, weights=args.weights_decoder, use_softmax=True)
crit = nn.NLLLoss(ignore_index=-1)
segmentation_module = SegmentationModule(net_encoder, net_decoder, crit)
segmentation_module.cuda()
segmentation_module.eval()
transform = transforms.Compose([transforms.Normalize(mean=[102.9801, 115.9465, 122.7717], std=[1., 1., 1.])])
# Load FastPhotoStyle model
p_wct = PhotoWCT()
p_wct.load_state_dict(torch.load(args.model))
if args.fast:
from photo_gif import GIFSmoothing
p_pro = GIFSmoothing(r=35, eps=0.001)
else:
from photo_smooth import Propagator
p_pro = Propagator()
if args.cuda:
p_wct.cuda(0)
def segment_this_img(f):
img = imread(f, mode='RGB')
img = img[:, :, ::-1] # BGR to RGB!!!
ori_height, ori_width, _ = img.shape
img_resized_list = []
for this_short_size in args.imgSize:
scale = this_short_size / float(min(ori_height, ori_width))
target_height, target_width = int(ori_height * scale), int(ori_width * scale)
target_height = round2nearest_multiple(target_height, args.padding_constant)
target_width = round2nearest_multiple(target_width, args.padding_constant)
img_resized = cv2.resize(img.copy(), (target_width, target_height))
img_resized = img_resized.astype(np.float32)
img_resized = img_resized.transpose((2, 0, 1))
img_resized = transform(torch.from_numpy(img_resized))
img_resized = torch.unsqueeze(img_resized, 0)
img_resized_list.append(img_resized)
input = dict()
input['img_ori'] = img.copy()
input['img_data'] = [x.contiguous() for x in img_resized_list]
segSize = (img.shape[0],img.shape[1])
with torch.no_grad():
pred = torch.zeros(1, args.num_class, segSize[0], segSize[1])
for timg in img_resized_list:
feed_dict = dict()
feed_dict['img_data'] = timg.cuda()
feed_dict = async_copy_to(feed_dict, args.gpu_id)
# forward pass
pred_tmp = segmentation_module(feed_dict, segSize=segSize)
pred = pred + pred_tmp.cpu() / len(args.imgSize)
_, preds = torch.max(pred, dim=1)
preds = as_numpy(preds.squeeze(0))
return preds
cont_seg = segment_this_img(args.content_image_path)
cv2.imwrite(args.content_seg_path, cont_seg)
style_seg = segment_this_img(args.style_image_path)
cv2.imwrite(args.style_seg_path, style_seg)
process_stylization_ade20k_ssn.stylization(
stylization_module=p_wct,
smoothing_module=p_pro,
content_image_path=args.content_image_path,
style_image_path=args.style_image_path,
content_seg_path=args.content_seg_path,
style_seg_path=args.style_seg_path,
output_image_path=args.output_image_path,
cuda=True,
save_intermediate=args.save_intermediate,
no_post=args.no_post,
label_remapping=segReMapping,
output_visualization=args.output_visualization
)