-
Notifications
You must be signed in to change notification settings - Fork 0
/
evaluate.py
116 lines (84 loc) · 4.08 KB
/
evaluate.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
# from Model_0 import resnet101
from Model_7 import resnet101
# from hierarchy_cls_train import model_save_path,train_loader,valid_loader,DEVICE,NUM_CLASSES, GRAYSCALE
import torch
import torch.nn as nn
import os
from util import compute_accuracy_model0,calculate_num_class,hierarchy_dict, \
calculate_num_class_model0, compute_accuracy_model12, \
compute_accuracy_model7_track_based, track_based_accuracy,\
track_based_accuracy_majority_vote,Otsu_Threshold, compute_accuracy_model7_track_based_level_2_only, track_based_accuracy_level2_only
from IPython import embed
from torchvision import transforms
from fish_rail_dataloader_track_based import Fish_Rail_Dataset
from torch.utils.data import DataLoader
import timm
from prefetch_generator import BackgroundGenerator
class DataLoaderX(DataLoader):
def __iter__(self):
return BackgroundGenerator(super().__iter__())
GRAYSCALE = False
# NUM_CLASSES = calculate_num_class(hierarchy_dict) #37
# NUM_CLASSES = calculate_num_class_model0(hierarchy_dict) # model0 31
NUM_level_1_CLASSES, NUM_level_2_CLASSES= calculate_num_class(hierarchy_dict)
model_name = 'resnext50_32x4d'
model_save_path = './checkpoints/' +model_name+'_aug' +'_cutmix_autoaug'
DEVICE = 'cuda:0'
BATCH_SIZE=256 *3
img_size=224
# model-7
save_path_val = './per img predictions val/'+model_name+'_aug'+'_cutmix_autoaug'
save_path_tr = './per img predictions train/'+model_name+'_aug'+'_cutmix_autoaug'
custom_transform = transforms.Compose([transforms.Resize((img_size, img_size)),
transforms.ToTensor()])
valid_gt_path = './rail_cropped_data/labels_track_based/fish-rail-valid-plus_sleeper_shark_nonfish-level2_only.csv'
train_gt_path = 'rail_cropped_data/labels_track_based/fish-rail-train-plus_sleeper_shark_nonfish-level2_only.csv'
img_dir = './rail_cropped_data/cropped_box_with_sleeper_shark_non_fish'
train_dataset = Fish_Rail_Dataset(csv_path=train_gt_path,
img_dir=img_dir,
transform=custom_transform,
hierarchy = hierarchy_dict)
valid_dataset = Fish_Rail_Dataset(csv_path=valid_gt_path,
img_dir=img_dir,
transform=custom_transform,
hierarchy = hierarchy_dict)
train_loader = DataLoaderX(dataset=train_dataset,
batch_size=BATCH_SIZE,
shuffle=False,
num_workers=0)
valid_loader = DataLoaderX(dataset=valid_dataset,
batch_size=BATCH_SIZE,
shuffle=False,
num_workers=0)
### load model
# best_epoch=23 # no aug
# best_epoch=11 # aug
best_epoch=93 # aug + cutmix + autoaug
stop_at_level_1_threshold=0.85
model = timm.create_model(model_name, pretrained=True, num_classes=NUM_level_2_CLASSES)
PATH = os.path.join(model_save_path,'parameters_epoch_'+str(best_epoch)+'.pkl')
model.load_state_dict(torch.load(PATH))
model.to(DEVICE)
### 最后测试一下 for model7
model.eval()
# for model7
with torch.set_grad_enabled(False): # save memory during inference
avg_level_2_acc_p1p2_31_val, acc_2_p1p2_31_val = compute_accuracy_model7_track_based_level_2_only(
model, valid_loader, best_epoch, DEVICE, save_path_val)
##根据记录下来的confidence，计算tarck-based的accuracy
avg_level_2_acc_p1p2_31_val_track, acc_2_p1p2_31_val_track = \
track_based_accuracy_level2_only(save_path_val, best_epoch)
print(
'Track-based Epoch: %03d | Valid: Level-2 Avg p1p2 max out of 31: %.3f%%' % (
best_epoch,
avg_level_2_acc_p1p2_31_val_track * 100,
))
print('Track-based Individual accuracy: Valid: '
'Level-2 p1p2 max out of 31:', acc_2_p1p2_31_val_track)
print('Image-based Epoch: %03d | Valid: Level-2 Avg p1p2 max out of 31: %.3f%%' % (
best_epoch,
avg_level_2_acc_p1p2_31_val * 100
))
print('Image-based Individual accuracy: Valid: '
'Level-2 p1p2 max out of 31:', acc_2_p1p2_31_val)
embed()