Hugging Face's logo

monai-test
/
brats_mri_segmentation

monai
medical
Model card Files Community
brats_mri_segmentation
File size: 3,866 Bytes 
139850b
 
 
 
 
9daa8eb
139850b
 
 
41526c1
 
139850b
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0238d7d
 
139850b
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
dab3954
 
 
 
 
139850b
 
 
 
dab3954
 
 
 
 
139850b
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0238d7d
 
 
 
139850b
 
 
 
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
 
{
    "imports": [
        "$import glob",
        "$import os"
    ],
    "bundle_root": ".",
    "ckpt_dir": "$@bundle_root + '/models'",
    "output_dir": "$@bundle_root + '/eval'",
    "data_list_file_path": "$@bundle_root + '/configs/datalist.json'",
    "dataset_dir": "/workspace/data/medical/brats2018challenge",
    "test_datalist": "$monai.data.load_decathlon_datalist(@data_list_file_path, data_list_key='testing', base_dir=@dataset_dir)",
    "device": "$torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')",
    "amp": true,
    "network_def": {
        "_target_": "SegResNet",
        "blocks_down": [
            1,
            2,
            2,
            4
        ],
        "blocks_up": [
            1,
            1,
            1
        ],
        "init_filters": 16,
        "in_channels": 4,
        "out_channels": 3,
        "dropout_prob": 0.2
    },
    "network": "$@network_def.to(@device)",
    "preprocessing": {
        "_target_": "Compose",
        "transforms": [
            {
                "_target_": "LoadImaged",
                "keys": "image",
                "image_only": false
            },
            {
                "_target_": "NormalizeIntensityd",
                "keys": "image",
                "nonzero": true,
                "channel_wise": true
            }
        ]
    },
    "dataset": {
        "_target_": "Dataset",
        "data": "@test_datalist",
        "transform": "@preprocessing"
    },
    "dataloader": {
        "_target_": "DataLoader",
        "dataset": "@dataset",
        "batch_size": 1,
        "shuffle": true,
        "num_workers": 4
    },
    "inferer": {
        "_target_": "SlidingWindowInferer",
        "roi_size": [
            240,
            240,
            160
        ],
        "sw_batch_size": 1,
        "overlap": 0.5
    },
    "postprocessing": {
        "_target_": "Compose",
        "transforms": [
            {
                "_target_": "Activationsd",
                "keys": "pred",
                "sigmoid": true
            },
            {
                "_target_": "Invertd",
                "keys": "pred",
                "transform": "@preprocessing",
                "orig_keys": "image",
                "meta_keys": "pred_meta_dict",
                "nearest_interp": false,
                "to_tensor": true
            },
            {
                "_target_": "AsDiscreted",
                "keys": "pred",
                "threshold": 0.5
            },
            {
                "_target_": "Lambdad",
                "keys": "pred",
                "func": "$lambda x: torch.where(x[[2]] > 0, 4, torch.where(x[[0]] > 0, 1, torch.where(x[[1]] > 0, 2, 0)))"
            },
            {
                "_target_": "SaveImaged",
                "keys": "pred",
                "meta_keys": "pred_meta_dict",
                "output_dir": "@output_dir",
                "output_postfix": "seg",
                "output_dtype": "uint8",
                "resample": false,
                "squeeze_end_dims": true
            }
        ]
    },
    "handlers": [
        {
            "_target_": "CheckpointLoader",
            "load_path": "$@bundle_root + '/models/model.pt'",
            "load_dict": {
                "model": "@network"
            }
        },
        {
            "_target_": "StatsHandler",
            "iteration_log": false
        }
    ],
    "evaluator": {
        "_target_": "SupervisedEvaluator",
        "device": "@device",
        "val_data_loader": "@dataloader",
        "network": "@network",
        "inferer": "@inferer",
        "postprocessing": "@postprocessing",
        "val_handlers": "@handlers",
        "amp": true
    },
    "initialize": [
        "$setattr(torch.backends.cudnn, 'benchmark', True)"
    ],
    "run": [
        "[email protected]()"
    ]
}