-
-
Notifications
You must be signed in to change notification settings - Fork 51
/
evaluate_gta5.py
executable file
·167 lines (138 loc) · 6.43 KB
/
evaluate_gta5.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
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
import argparse
import scipy
from scipy import ndimage
import numpy as np
import sys
from packaging import version
import torch
from torch.autograd import Variable
import torchvision.models as models
import torch.nn.functional as F
from torch.utils import data, model_zoo
from model.deeplab import Res_Deeplab
from model.deeplab_multi import DeeplabMulti
from model.deeplab_vgg import DeeplabVGG
from dataset.gta5_dataset import GTA5DataSet
from collections import OrderedDict
import os
from PIL import Image
from utils.tool import fliplr
import matplotlib.pyplot as plt
import torch.nn as nn
IMG_MEAN = np.array((104.00698793,116.66876762,122.67891434), dtype=np.float32)
# We just use this file to evaluate the perfromance on the training set
DATA_DIRECTORY = './data/GTA5'
DATA_LIST_PATH = './dataset/gta5_list/train.txt'
SAVE_PATH = './result/GTA5'
IGNORE_LABEL = 255
NUM_CLASSES = 19
NUM_STEPS = 500 # Number of images in the validation set.
RESTORE_FROM = 'http://vllab.ucmerced.edu/ytsai/CVPR18/GTA2Cityscapes_multi-ed35151c.pth'
RESTORE_FROM_VGG = 'http://vllab.ucmerced.edu/ytsai/CVPR18/GTA2Cityscapes_vgg-ac4ac9f6.pth'
RESTORE_FROM_ORC = 'http://vllab1.ucmerced.edu/~whung/adaptSeg/cityscapes_oracle-b7b9934.pth'
SET = 'val'
MODEL = 'DeeplabMulti'
palette = [128, 64, 128, 244, 35, 232, 70, 70, 70, 102, 102, 156, 190, 153, 153, 153, 153, 153, 250, 170, 30,
220, 220, 0, 107, 142, 35, 152, 251, 152, 70, 130, 180, 220, 20, 60, 255, 0, 0, 0, 0, 142, 0, 0, 70,
0, 60, 100, 0, 80, 100, 0, 0, 230, 119, 11, 32]
zero_pad = 256 * 3 - len(palette)
for i in range(zero_pad):
palette.append(0)
def colorize_mask(mask):
# mask: numpy array of the mask
new_mask = Image.fromarray(mask.astype(np.uint8)).convert('P')
new_mask.putpalette(palette)
return new_mask
def get_arguments():
"""Parse all the arguments provided from the CLI.
Returns:
A list of parsed arguments.
"""
parser = argparse.ArgumentParser(description="DeepLab-ResNet Network")
parser.add_argument("--model", type=str, default=MODEL,
help="Model Choice (DeeplabMulti/DeeplabVGG/Oracle).")
parser.add_argument("--data-dir", type=str, default=DATA_DIRECTORY,
help="Path to the directory containing the Cityscapes dataset.")
parser.add_argument("--data-list", type=str, default=DATA_LIST_PATH,
help="Path to the file listing the images in the dataset.")
parser.add_argument("--ignore-label", type=int, default=IGNORE_LABEL,
help="The index of the label to ignore during the training.")
parser.add_argument("--num-classes", type=int, default=NUM_CLASSES,
help="Number of classes to predict (including background).")
parser.add_argument("--restore-from", type=str, default=RESTORE_FROM,
help="Where restore model parameters from.")
parser.add_argument("--gpu", type=int, default=0,
help="choose gpu device.")
parser.add_argument("--batchsize", type=int, default=10,
help="choose gpu device.")
parser.add_argument("--set", type=str, default=SET,
help="choose evaluation set.")
parser.add_argument("--save", type=str, default=SAVE_PATH,
help="Path to save result.")
return parser.parse_args()
def main():
"""Create the model and start the evaluation process."""
args = get_arguments()
gpu0 = args.gpu
batchsize = args.batchsize
model_name = os.path.basename( os.path.dirname(args.restore_from) )
args.save += model_name
if not os.path.exists(args.save):
os.makedirs(args.save)
if args.model == 'DeeplabMulti':
model = DeeplabMulti(num_classes=args.num_classes, train_bn = False, norm_style = 'in')
elif args.model == 'Oracle':
model = Res_Deeplab(num_classes=args.num_classes)
if args.restore_from == RESTORE_FROM:
args.restore_from = RESTORE_FROM_ORC
elif args.model == 'DeeplabVGG':
model = DeeplabVGG(num_classes=args.num_classes)
if args.restore_from == RESTORE_FROM:
args.restore_from = RESTORE_FROM_VGG
if args.restore_from[:4] == 'http' :
saved_state_dict = model_zoo.load_url(args.restore_from)
else:
saved_state_dict = torch.load(args.restore_from)
try:
model.load_state_dict(saved_state_dict)
except:
model = torch.nn.DataParallel(model)
model.load_state_dict(saved_state_dict)
model.eval()
model.cuda()
testloader = data.DataLoader(GTA5DataSet(args.data_dir, args.data_list, crop_size=(640, 1280), resize_size=(1280, 640), mean=IMG_MEAN, scale=False, mirror=False),
batch_size=batchsize, shuffle=False, pin_memory=True)
if version.parse(torch.__version__) >= version.parse('0.4.0'):
interp = nn.Upsample(size=(640, 1280 ), mode='bilinear', align_corners=True)
else:
interp = nn.Upsample(size=(640, 1280 ), mode='bilinear')
sm = torch.nn.Softmax(dim = 1)
for index, batch in enumerate(testloader):
if (index*batchsize) % 100 == 0:
print('%d processd' % (index*batchsize))
image, _, _, name = batch
print(image.shape)
inputs = Variable(image).cuda()
if args.model == 'DeeplabMulti':
output1, output2 = model(inputs)
output_batch = interp(sm(0.5* output1 + output2)).cpu().data.numpy()
#output1, output2 = model(fliplr(inputs))
#output2 = fliplr(output2)
#output_batch += interp(output2).cpu().data.numpy()
elif args.model == 'DeeplabVGG' or args.model == 'Oracle':
output_batch = model(Variable(image).cuda())
output_batch = interp(output_batch).cpu().data.numpy()
output_batch = output_batch.transpose(0,2,3,1)
output_batch = np.asarray(np.argmax(output_batch, axis=3), dtype=np.uint8)
for i in range(output_batch.shape[0]):
output = output_batch[i,:,:]
output_col = colorize_mask(output)
output = Image.fromarray(output)
name_tmp = name[i].split('/')[-1]
output.save('%s/%s' % (args.save, name_tmp))
output_col.save('%s/%s_color.png' % (args.save, name_tmp.split('.')[0]))
return args.save
if __name__ == '__main__':
with torch.no_grad():
save_path = main()
os.system('python compute_iou.py ./data/GTA5/data/gtFine/val %s'%save_path)