-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathevaluate_frrn.py
149 lines (137 loc) · 6.97 KB
/
evaluate_frrn.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
import os
import torch
import torch.nn.functional as F
import numpy as np
from tqdm import tqdm
from PIL import Image
import sys
import yaml
from dataloader.a2d2_loader import get_dataloader
from models.frrn import FRRNet
from models.frrn_ensemble import Ensemble
from models.frrn_moe import MoE
from utils.metrics import Evaluator
from utils.saver import Saver
from utils.train_utils import ensure_dir, colorize, save_stack_img, load_my_state_dict
class Validator():
"""Define model validator"""
# pylint: disable=too-many-instance-attributes
# pylint: disable=too-few-public-methods
def __init__(self, _config):
self.params = params = _config
params["gpu_ids"] = [0]
params["output"] = params["DIR"]
params["dataset"] = ""
params["checkname"] = params['MODEL']['arch']+'_'+params['MODEL']['expert']+ params['MODEL']['gate']
self.is_cuda = len(params["gpu_ids"]) > 0
self.device = torch.device('cuda', params["gpu_ids"][0]) \
if self.is_cuda else torch.device('cpu')
saver = Saver(params)
save_path = ensure_dir(os.path.join(saver.directory, 'eval_logs'))
self.test_loader, self.label_names, self.label_colors = \
get_dataloader(params, params['test_sets'], 'test')
if 'moe' in params['MODEL']['expert'].lower():
expert_names = list(s for s in params['DATASET']['dataset'].split(','))
checkpoints = [os.path.join(*[params['DIR'],
params['MODEL']['arch'] + '_' + expert_name,
'model_best.pth'])
for expert_name in expert_names]
# fully connect layer feature depend on scale factor
l_feat = int((params['DATASET']['img_height']/16) * (params['DATASET']['img_width']/16))
self.model = MoE(3, params['DATASET']['num_class'],
l_feat,
checkpoints,
params['MODEL']['gate'],
params['MODEL']['with_conv'],
len(expert_names))
elif 'ensemble' in params['MODEL']['expert'].lower():
expert_names = list(s for s in params['DATASET']['dataset'].split(','))
checkpoints = [os.path.join(*[params['DIR'],
params['MODEL']['arch'] + '_' + expert_name,
'model_best.pth'])
for expert_name in expert_names]
# fully connect layer feature depend on scale factor
self.model = Ensemble(3,
params['DATASET']['num_class'],
checkpoints,
params['MODEL']['ensemble_type'])
else:
self.model = FRRNet(out_channels=params['DATASET']['num_class'])
self.evaluator = Evaluator(params['DATASET']['num_class'])
if self.is_cuda:
self.model = torch.nn.DataParallel(self.model, device_ids=params["gpu_ids"])
self.model = self.model.to(self.device)
if params['MODEL']['expert'] == "moe":
saved_ckpt_path = os.path.join(*[params['DIR'], params['MODEL']['arch']+'_'+
params['MODEL']['expert'] +'_'+
#params['MODEL']['layer']+'_'+
params['MODEL']['gate']
,params['VAL']['checkpoint']])
else:
saved_ckpt_path = os.path.join(*[params['DIR'], params['MODEL']['arch']+'_'+
params['MODEL']['expert'], params['VAL']['checkpoint']])
if not params['MODEL']['expert'] == "ensemble":
assert os.path.exists(saved_ckpt_path), '{} not exit!'.format(saved_ckpt_path)
print(saved_ckpt_path)
new_state_dict = torch.load(saved_ckpt_path)
self.model = load_my_state_dict(self.model.module, new_state_dict['state_dict'])
def validate(self):
"""validate model"""
# pylint: disable-msg=too-many-locals
self.model.eval()
self.evaluator.reset()
tbar = tqdm(self.test_loader, desc='\r')
if 'delta_path' in params:
delta = torch.load(params['delta_path'])
print("Loading noise from", params['delta_path'])
delta = delta.to(self.device)
for _, sample in enumerate(tbar):
image, label, label_path = sample
image = image.to(self.device)
label = label.to(self.device)
if 'delta_path' in params:
#add delta to image
image = image + delta.repeat(params["TEST"]["batch_size"], 1, 1, 1)
image.data = torch.clamp(image.data, min=0, max=1)
with torch.no_grad():
output = self.model(image)
label = label.cpu().numpy()
pred = output.data.cpu().numpy()
pred = np.argmax(pred, axis=1)
# Add batch sample into evaluator
self.evaluator.add_batch(label, pred)
if self.params['VAL']['visualize']:
for i in range(output.shape[0]):
# Val batch size is 1.
pred = output[i].data.max(0)[1].cpu().numpy()
ground_true = label[i]
pred_colors = colorize(self.label_colors, pred)
gt_colors = colorize(self.label_colors, ground_true)
# save wrt. label path and weight path
_, city, label_name = label_path[i].split('/')[-3:]
pred_save_path = os.path.join(
os.path.dirname(self.params['VAL']['checkpoint']), 'pred_results',
city, label_name)
if not os.path.exists(os.path.dirname(pred_save_path)):
os.makedirs(os.path.dirname(pred_save_path))
save_stack_img([gt_colors, pred_colors], pred_save_path)
# Fast test during the training
acc = self.evaluator.pixel_accuracy()
acc_class = self.evaluator.pixel_accuracy_class()
miou, log_miou_cls = self.evaluator.mean_intersection_over_union(self.label_names)
fwiou = self.evaluator.frequency_weighted_intersection_over_union()
print("Validation:")
print("pAcc:{}, mAcc:{}, m_iou:{}, fwIoU: {}".format(acc, acc_class, miou, fwiou))
if __name__ == '__main__':
if len(sys.argv) != 2:
print('\nPlease pass the desired param file for training as an argument.\n'
'e.g: params/params_moe.py')
else:
print('STARTING EVALUATION WITH PARAM FILE: ', str(sys.argv[1]))
with open(str(sys.argv[1]), 'r') as stream:
try:
params = yaml.safe_load(stream)
validator = Validator(params)
validator.validate()
except yaml.YAMLError as exc:
print(exc)