-
Notifications
You must be signed in to change notification settings - Fork 4
/
Copy patheval.py
209 lines (189 loc) · 9.87 KB
/
eval.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
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
import argparse
import os
import numpy as np
from tqdm import tqdm
import time
import torch
from torchvision.transforms import ToPILImage
from PIL import Image
import logging
import datetime
from dataloaders import make_data_loader
from dataloaders.utils import Colorize
from utils.metrics import Evaluator
from models.edcnet import EDCNet
import torch.backends.cudnn as cudnn
def write_config(args, log):
log.info('\n----------------- Options ---------------\n')
for k, v in sorted(vars(args).items()):
log.info('{:>25}: {:<30}\n'.format(str(k), str(v)))
log.info('----------------- End -------------------\n')
def create_logger(dir):
logger = logging.getLogger("Logger")
log_time = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
file_path = os.path.join(dir, "eval_{}.log".format(log_time))
hdlr = logging.FileHandler(file_path)
formatter = logging.Formatter("%(asctime)s %(levelname)s %(message)s")
hdlr.setFormatter(formatter)
hdlr.setLevel(logging.INFO)
logger.addHandler(hdlr)
logger.setLevel(logging.INFO)
return logger
class Validator(object):
def __init__(self, args, logger):
self.args = args
self.logger = logger
self.time_train = []
self.args.evaluate= True
self.args.merge=True
kwargs = {'num_workers':args.workers, 'pin_memory': False}
_, self.val_loader, _, self.num_class = make_data_loader(args, **kwargs)
print('un_classes:'+str(self.num_class))
self.resize = args.crop_size if args.crop_size else [512, 1024]
self.evaluator = Evaluator(self.num_class, self.logger)
self.model = EDCNet(self.args.rgb_dim, args.event_dim, num_classes=self.num_class, use_bn=True)
if args.cuda:
self.model = torch.nn.DataParallel(self.model, device_ids=self.args.gpu_ids)
self.model = self.model.to(self.args.device)
cudnn.benchmark = True
print('Model loaded successfully!')
assert os.path.exists(args.weight_path), 'weight-path:{} doesn\'t exit!'.format(args.weight_path)
self.new_state_dict = torch.load(os.path.join(args.weight_path), map_location='cuda:0')
self.model = load_my_state_dict(self.model.module, self.new_state_dict['state_dict'])
def validate(self):
self.model.eval()
self.evaluator.reset()
tbar = tqdm(self.val_loader, desc='\r')
for i, (sample, gt_path) in enumerate(tbar):
target = sample['label']
image = sample['image']
event = sample['event']
if self.args.cuda:
target = target.to(self.args.device)
image = image.to(self.args.device)
event = event.to(self.args.device)
start_time = time.time()
with torch.no_grad():
output, output_event = self.model(image)
pred = output.data.cpu().numpy()
target = target.cpu().numpy()
pred = np.argmax(pred, axis=1)
self.evaluator.add_batch(target, pred)
if self.args.cuda:
torch.cuda.synchronize()
if i!=0:
fwt = time.time() - start_time
self.time_train.append(fwt)
print("Forward time per img (bath size=%d): %.3f (Mean: %.3f)" % (
self.args.val_batch_size, fwt / self.args.val_batch_size,
sum(self.time_train) / len(self.time_train) / self.args.val_batch_size))
time.sleep(0.1)
pre_colors = Colorize()(torch.max(output, 1)[1].detach().cpu().byte())
pre_colors_gt = Colorize()(torch.ByteTensor(target))
checkname = self.args.weight_path.split('/')[-2]
prediction_save_dir = os.path.join(self.args.label_save_path, checkname)
if self.args.label_save:
for j in range(pre_colors.shape[0]):
label_name = os.path.join(
*[prediction_save_dir, gt_path[j].split('gtFine/val/')[1].replace('/','_')])
if 'dada' in self.args.dataset:
label_name = label_name.replace('.jpg', '.png')
os.makedirs(os.path.dirname(label_name), exist_ok=True)
if 'dada' in self.args.dataset:
leftImg8bit_path = gt_path[j].replace('_labelTrainIds.png', '.jpg')
elif 'cityscape' in self.args.dataset:
leftImg8bit_path = gt_path[j].replace('_gtFine_labelTrainIds.png', '_leftImg8bit.png')
leftImg8bit_path = leftImg8bit_path.replace('/gtFine/', '/leftImg8bit/')
pre_color_image = ToPILImage()(pre_colors[j])
pre_colors_gt = ToPILImage()(pre_colors_gt[j])
img_ = Image.open(leftImg8bit_path)
img_ = img_.crop((280, 32, 1304, 544)) # [162, 0, 1422, 600]
img_ = img_.resize((self.resize[1], self.resize[0]), Image.BILINEAR)
event_ = ToPILImage()(event[j].cpu())
pre_event_ = ToPILImage()(torch.sigmoid(output_event[j]).cpu()) # blur
if self.args.event_dim:
event_path = leftImg8bit_path.replace('/leftImg8bit/', '/event_image/')
event_path = event_path.replace('.jpg', '_event_image.png')
image_stack(img_, pre_color_image, pre_colors_gt, label_name, event_, pre_event_)
else:
image_stack(Image.open(leftImg8bit_path), pre_color_image, pre_colors_gt, label_name)
Acc = self.evaluator.Pixel_Accuracy()
Acc_class = self.evaluator.Pixel_Accuracy_Class()
mIoU = self.evaluator.Mean_Intersection_over_Union()
FWIoU = self.evaluator.Frequency_Weighted_Intersection_over_Union()
self.logger.info('Validation:')
self.logger.info('[Epoch: %d, numImages: %5d]' % (0, i * self.args.batch_size + target.data.shape[0]))
self.logger.info("Acc:{}, Acc_class:{}, mIoU:{}, fwIoU: {}".format(Acc, Acc_class, mIoU, FWIoU))
def image_stack(org, pred, gt, savepath, event=None, pre_event_=None):
imgs = [org, pred, gt]
if event is not None:
event = event.convert('RGB')
imgs.insert(1, event)
if pre_event_ is not None:
pre_event_ = pre_event_.convert('RGB')
imgs.insert(2, pre_event_)
min_shape = sorted([(np.sum(i.size), i.size) for i in imgs])[0][1]
store_img = np.vstack([np.asarray(i.resize(min_shape)) for i in imgs])
store_img = Image.fromarray(store_img)
store_img.save(savepath)
def load_my_state_dict(model, state_dict):
own_state = model.state_dict()
for name, param in state_dict.items():
if name not in own_state:
print('{} not in model_state'.format(name))
continue
else:
own_state[name].copy_(param)
return model
def main():
parser = argparse.ArgumentParser(description="PyTorch Event validation")
parser.add_argument('--model', type=str, default='EDCNet',
choices=['EDCNet'], help='model name (default: EDCNet)')
parser.add_argument('--rgb-dim', type=int, default=3,
choices=[0, 3], help='whether use rgb as input (default: 3)')
parser.add_argument('--event-dim', type=int, default=1,
choices=[1, 2, 18], help='event volume dimension (default: 2)')
parser.add_argument('--dataset', type=str, default='dadaevent',
choices=['cityscapesevent', 'dadaevent', 'apolloscapeevent', 'bdd', 'kittievent', 'merge3'],
help='dataset name (default: dadaevent)')
parser.add_argument('--workers', type=int, default=2,
metavar='N', help='dataloader threads')
parser.add_argument('--base-size', type=int, default=1024,
help='base image size')
parser.add_argument('--crop-size', type=int, default=(512, 1024),
help='crop image size')
parser.add_argument('--batch-size', type=int, default=1,
help='batch size for training')
parser.add_argument('--val-batch-size', type=int, default=1,
metavar='N', help='input batch size for validating (default: auto)')
parser.add_argument('--test-batch-size', type=int, default=1,
metavar='N', help='input batch size for testing (default: auto)')
parser.add_argument('--gpu-ids', type=str, default='0',
help='use which gpu to train, must be a \
comma-separated list of integers only (default=0)')
parser.add_argument('--device', type=torch.device, default='cpu',
help='torch device')
parser.add_argument('--checkname', type=str, default=None,
help='set the checkpoint name')
parser.add_argument('--weight-path', type=str, default="./run/cityscapesevent/test_EDCNet_r18/model_best.pth",
help='enter your path of the weight')
parser.add_argument('--label-save', action='store_true', default=False, help='save label')
parser.add_argument('--label-save-path', type=str, default='results/',
help='path to save label')
parser.add_argument('--evaluate', action='store_true', default=False, help='evaluate')
args = parser.parse_args()
args.cuda = torch.cuda.is_available()
if args.cuda:
try:
args.gpu_ids = [int(s) for s in args.gpu_ids.split(',')]
args.device = torch.device('cuda', args.gpu_ids[0])
except ValueError:
raise ValueError('Argument --gpu_ids must be a comma-separated list of integers only')
log_dir = os.path.dirname(args.weight_path) + '/eval_DADA_log'
os.makedirs(log_dir, exist_ok=True)
logger = create_logger(log_dir)
write_config(args, logger)
validator = Validator(args, logger)
validator.validate()
if __name__ == "__main__":
main()