BenchmarkTransferLearning/main_classification.py at main · pkharge/BenchmarkTransferLearning · GitHub

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
import os
import sys
import shutil
import time
import numpy as np
from optparse import OptionParser
from shutil import copyfile
from tqdm import tqdm

from utils import vararg_callback_bool, vararg_callback_int
from dataloader import  *

import torch
from engine import classification_engine

sys.setrecursionlimit(40000)


def get_args_parser():
    parser = OptionParser()

    parser.add_option("--GPU", dest="GPU", help="the index of gpu is used", default=None, action="callback",
                      callback=vararg_callback_int)
    parser.add_option("--model", dest="model_name", help="DenseNet121", default="Resnet50", type="string")
    parser.add_option("--init", dest="init",
                      help="Random | ImageNet| or any other pre-training method",
                      default="Random", type="string")
    parser.add_option("--num_class", dest="num_class", help="number of the classes in the downstream task",
                      default=14, type="int")
    parser.add_option("--data_set", dest="data_set", help="ChestXray14|CheXpert", default="ChestXray14", type="string")
    parser.add_option("--normalization", dest="normalization", help="how to normalize data (imagenet|chestx-ray)", default="imagenet",
                      type="string")
    parser.add_option("--img_size", dest="img_size", help="input image resolution", default=224, type="int")
    parser.add_option("--img_depth", dest="img_depth", help="num of image depth", default=3, type="int")
    parser.add_option("--data_dir", dest="data_dir", help="dataset dir",default=None, type="string")
    parser.add_option("--train_list", dest="train_list", help="file for training list",
                      default=None, type="string")
    parser.add_option("--val_list", dest="val_list", help="file for validating list",
                      default=None, type="string")
    parser.add_option("--test_list", dest="test_list", help="file for test list",
                      default=None, type="string")
    parser.add_option("--mode", dest="mode", help="train | test", default="train", type="string")
    parser.add_option("--batch_size", dest="batch_size", help="batch size", default=32, type="int")
    parser.add_option("--num_epoch", dest="num_epoch", help="num of epoches", default=1000, type="int")
    parser.add_option("--optimizer", dest="optimizer", help="Adam | SGD", default="Adam", type="string")
    parser.add_option("--lr", dest="lr", help="learning rate", default=2e-4, type="float")
    parser.add_option("--lr_Scheduler", dest="lr_Scheduler", help="learning schedule", default="ReduceLROnPlateau",
                      type="string")
    parser.add_option("--patience", dest="patience", help="num of patient epoches", default=10, type="int")
    parser.add_option("--early_stop", dest="early_stop", help="whether use early_stop", default=True, action="callback",
                      callback=vararg_callback_bool)
    parser.add_option("--trial", dest="num_trial", help="number of trials", default=1, type="int")
    parser.add_option("--start_index", dest="start_index", help="the start model index", default=0, type="int")
    parser.add_option("--clean", dest="clean", help="clean the existing data", default=False, action="callback",
                      callback=vararg_callback_bool)
    parser.add_option("--resume", dest="resume", help="whether latest checkpoint", default=False, action="callback",
                      callback=vararg_callback_bool)
    parser.add_option("--workers", dest="workers", help="number of CPU workers", default=8, type="int")
    parser.add_option("--print_freq", dest="print_freq", help="print frequency", default=50, type="int")
    parser.add_option("--test_augment", dest="test_augment", help="whether use test time augmentation",
                      default=True, action="callback", callback=vararg_callback_bool)
    parser.add_option("--proxy_dir", dest="proxy_dir", help="Path to the Pretrained model", default=None, type="string")
    parser.add_option("--anno_percent", dest="anno_percent", help="data percent", default=100, type="int")
    parser.add_option("--device", dest="device", help="cpu|cuda", default="cuda", type="string")
    parser.add_option("--activate", dest="activate", help="Sigmoid", default="Sigmoid", type="string")
    parser.add_option("--uncertain_label", dest="uncertain_label",
                      help="the label assigned to uncertain data (Ones | Zeros | LSR-Ones | LSR-Zeros)",
                      default="LSR-Ones", type="string")
    parser.add_option("--unknown_label", dest="unknown_label", help="the label assigned to unknown data",
                      default=0, type="int")


    (options, args) = parser.parse_args()

    return options


def main(args):
    print(args)
    assert args.data_dir is not None
    assert args.train_list is not None
    assert args.val_list is not None
    assert args.test_list is not None
    if args.init.lower() != 'imagenet' and args.init.lower() != 'random':
        assert args.proxy_dir is not None

    args.exp_name = args.model_name + "_" + args.init
    model_path = os.path.join("./Models/Classification",args.data_set)
    output_path = os.path.join("./Outputs/Classification",args.data_set)

    if args.data_set == "ChestXray14":
        diseases = ['Atelectasis', 'Cardiomegaly', 'Effusion', 'Infiltration', 'Mass', 'Nodule',
                    'Pneumonia', 'Pneumothorax', 'Consolidation', 'Edema',
                    'Emphysema', 'Fibrosis', 'Pleural_Thickening', 'Hernia']
        dataset_train = ChestXray14Dataset(images_path=args.data_dir, file_path=args.train_list,
                                           augment=build_transform_classification(normalize=args.normalization, mode="train"))

        dataset_val = ChestXray14Dataset(images_path=args.data_dir, file_path=args.val_list,
                                         augment=build_transform_classification(normalize=args.normalization, mode="valid"))
        dataset_test = ChestXray14Dataset(images_path=args.data_dir, file_path=args.test_list,
                                          augment=build_transform_classification(normalize=args.normalization, mode="test"))

        classification_engine(args, model_path, output_path, diseases, dataset_train, dataset_val, dataset_test)


    elif args.data_set == "CheXpert":
        diseases = ['No Finding', 'Enlarged Cardiomediastinum', 'Cardiomegaly', 'Lung Opacity',
                           'Lung Lesion', 'Edema', 'Consolidation', 'Pneumonia', 'Atelectasis', 'Pneumothorax',
                           'Pleural Effusion', 'Pleural Other', 'Fracture', 'Support Devices']
        test_diseases_name = ['Atelectasis', 'Cardiomegaly', 'Consolidation', 'Edema', 'Pleural Effusion']
        test_diseases = [diseases.index(c) for c in test_diseases_name]
        dataset_train = CheXpertDataset(images_path=args.data_dir, file_path=args.train_list,
                                        augment=build_transform_classification(normalize=args.normalization, mode="train"), uncertain_label=args.uncertain_label, unknown_label=args.unknown_label, annotation_percent=args.anno_percent)

        dataset_val = CheXpertDataset(images_path=args.data_dir, file_path=args.val_list,
                                      augment=build_transform_classification(normalize=args.normalization, mode="valid"), uncertain_label=args.uncertain_label, unknown_label=args.unknown_label, annotation_percent=args.anno_percent)

        dataset_test = CheXpertDataset(images_path=args.data_dir, file_path=args.test_list,
                                       augment=build_transform_classification(normalize=args.normalization, mode="test"), uncertain_label=args.uncertain_label, unknown_label=args.unknown_label, annotation_percent=args.anno_percent)

        classification_engine(args, model_path, output_path, diseases, dataset_train, dataset_val, dataset_test, test_diseases)

    elif args.data_set == "Shenzhen":
        diseases = ['TB']
        dataset_train = ShenzhenCXR(images_path=args.data_dir, file_path=args.train_list,
                                    augment=build_transform_classification(normalize=args.normalization, mode="train"), annotation_percent=args.anno_percent)

        dataset_val = ShenzhenCXR(images_path=args.data_dir, file_path=args.val_list,
                                  augment=build_transform_classification(normalize=args.normalization, mode="valid"), annotation_percent=args.anno_percent)

        dataset_test = ShenzhenCXR(images_path=args.data_dir, file_path=args.test_list,
                                   augment=build_transform_classification(normalize=args.normalization, mode="test"), annotation_percent=args.anno_percent)


        classification_engine(args, model_path, output_path, diseases, dataset_train, dataset_val, dataset_test)


if __name__ == '__main__':
    args = get_args_parser()
    main(args)