MI-SegNet/inference.py at main · FraukeWilm/MI-SegNet · 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
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
import torch
from tqdm import tqdm
import cv2
import os
import json
import numpy as np
import yaml
import albumentations.augmentations.functional as F
from albumentations.pytorch.transforms import ToTensorV2
from PIL import Image
from torchvision import transforms
from torchmetrics.classification import ConfusionMatrix
from torchmetrics.functional.classification.jaccard import _jaccard_index_reduce
from MI_SegNet import Seg_encoder_LM, Seg_decoder_LM
import argparse
from omegaconf import DictConfig
from segmentation_models_pytorch import Unet, DeepLabV3Plus
from torch import nn

class Inference:
    def __init__(self, dir_path, image_path, annotation_path, config, num_classes):
        self.config = config
        self.model = config.model.name
        self.dir_path = dir_path
        self.image_path = image_path
        self.annotation_path = annotation_path
        self.num_classes = num_classes
        self.mode_keys = {'scanner': ["cs2", "nz20", "nz210", "gt450", "p1000"]}
        self.n_domains = len(self.mode_keys['scanner'])
        if torch.backends.mps.is_available():
            self.device = torch.device("mps")
        elif torch.cuda.is_available():
            self.device = torch.device("cuda")
        else:
            self.device = torch.device("cpu")
        self.patch_size = self.config.data.patch_size
        self.test_images = os.listdir(self.image_path)
        self.label_dict = {'background': 0, 'non-tumor': 1, 'tumor': 2}
        with open("data/whites.yaml", 'r') as stream:
            self.whites = yaml.safe_load(stream)
        with open(self.annotation_path) as f:
            self.annotation_dict = json.load(f)
        self.cm = [ConfusionMatrix(task='multiclass', num_classes=self.num_classes, ignore_index=-1).to(self.device) for _ in range(self.n_domains)]
        self.transform_image = nn.Identity() #transforms.Normalize(0.5, 0.5)


    def configure_model(self):
        if self.model.__contains__ ('segnet'):
            self.encoder = Seg_encoder_LM(self.config.model.input_channel, init_features=64, num_blocks=2).to(self.device)
            self.decoder = Seg_decoder_LM(self.config.model.output_channel, init_features=64, num_blocks=2).to(self.device)
        elif self.model.__contains__ ('unet'):
            unet = Unet(encoder_name='resnet34', classes=3)
            self.encoder = unet.encoder.to(self.device)
            self.decoder = unet.decoder.to(self.device)
            self.segmentation_head = unet.segmentation_head.to(self.device)
        elif self.model == 'densenet':
            densenet = DeepLabV3Plus(encoder_name='resnet34', classes=3)
            self.encoder = densenet.encoder.to(self.device)
            self.decoder = densenet.decoder.to(self.device)
            self.segmentation_head = densenet.segmentation_head.to(self.device)

    def load_model_checkpoint(self):
        encoder_ckpts = {}
        decoder_ckpts = {}
        head_ckpts = {}
        ckpt_name = list(filter(lambda file: file.endswith('.ckpt'), os.listdir(os.path.join(self.dir_path, 'files'))))[0]
        ckpt_temp = torch.load(os.path.join(self.dir_path,'files', ckpt_name), map_location=self.device)['state_dict']
        for (key, value) in ckpt_temp.items():
            if key.startswith('seg_encoder'):
                encoder_ckpts[key.split("seg_encoder.")[-1]] = value
            elif key.startswith('seg_decoder'):
                decoder_ckpts[key.split("seg_decoder.")[-1]] = value
            elif key.startswith('segmentation_head'):
                head_ckpts[key.split("segmentation_head.")[-1]] = value
        self.encoder.load_state_dict(encoder_ckpts)
        self.encoder.eval()
        self.decoder.load_state_dict(decoder_ckpts)
        self.decoder.eval()
        if not self.model.__contains__ ('segnet'):
            self.segmentation_head.load_state_dict(head_ckpts)
            self.segmentation_head.eval()

    def process(self):
        # start extracting patches
        with torch.inference_mode():
            for sample in tqdm(self.test_images):
                tensors, gt = self.get_batch(sample)
                tensors = [self.transform_image(t) for t in tensors]
                tensors = self.chunk(tensors, n_chunks=gt.shape[1]//self.patch_size)
                gt = self.chunk(gt.unsqueeze(1), n_chunks=gt.shape[1]//self.patch_size)
                input_batch = torch.stack(tensors).to(self.device)
                features = self.encoder(input_batch)
                if not self.model.__contains__ ('segnet'):
                    outputs = self.decoder(*features)
                    outputs = self.segmentation_head(outputs)
                else:
                    outputs = self.decoder(features)
                outputs = torch.max(outputs, dim=1)[1]
                self.update_cm(outputs, gt)

    def get_batch(self, sample):
        patches = cv2.imread(os.path.join(self.image_path, sample))
        patches = cv2.cvtColor(patches, cv2.COLOR_BGR2RGB)
        patches[(patches == [0, 0, 0]).all(axis=-1)] = [255, 255, 255]
        tensors = [ToTensorV2().apply(patches[:, i * patches.shape[0]:(i + 1) * patches.shape[0], :]) / 255. for i in range(self.n_domains)]
        gt = torch.stack([ToTensorV2().apply(patch) for patch in
                          self.get_y_patch(sample, patches, self.annotation_dict,
                                           whites=self.whites[sample[:6]])]).squeeze(1)
        return tensors, gt

    def get_y_patch(self, sample_name, patches, annotation_dict, down_factor=4, whites={}):
        whites = [whites[s] for s in self.mode_keys['scanner']]
        x, y = sample_name[:-4].split("_")[2:]
        image_id = [i["id"] for i in annotation_dict["images"] if i["file_name"] == sample_name[:6] + "_cs2.svs"][0]
        polygons = [anno for anno in annotation_dict['annotations'] if anno["image_id"] == image_id]
        y_patch = -1 * np.ones(shape=(self.n_domains, patches.shape[0], patches.shape[0]), dtype=np.int8)

        for poly in polygons:
            coordinates = np.array(poly['segmentation']).reshape((-1, 2))
            coordinates = (coordinates - (int(x) * 100, int(y) * 100)) / down_factor
            label = 1 if poly["category_id"] < 7 else 2
            for i in range(self.n_domains):
                cv2.drawContours(y_patch[i], [coordinates.reshape((-1, 1, 2)).astype(int)], -1, label, -1)
        white_mask = np.array(
            [cv2.cvtColor(patches[:, i * patches.shape[0]:(i + 1) * patches.shape[0], :], cv2.COLOR_RGB2GRAY) > whites[i] for i in
             range(self.n_domains)])
        excluded = (y_patch == -1)
        y_patch[np.logical_and(white_mask, excluded)] = 0
        return y_patch

    def chunk(self, tensors, n_chunks=2):
        x_chunks = [torch.chunk(t, n_chunks, dim=1) for t in tensors]
        x_chunks = [ch for chunk in x_chunks for ch in chunk]
        y_chunks = [torch.chunk(t, n_chunks, dim=2) for t in x_chunks]
        y_chunks = [ch for chunk in y_chunks for ch in chunk]
        return y_chunks


    def update_cm(self, outputs, gt):
        for o in range(outputs.shape[0]):
            idx = o//(outputs.shape[0]//self.n_domains)
            self.cm[idx].update(outputs[o], gt[o].squeeze().to(self.device))

    def get_ious(self):
        ious = [_jaccard_index_reduce(mat.compute(), average='none').cpu().numpy() for mat in self.cm]
        ious = {str(self.mode_keys['scanner'][idx]): {**{key: str(iou[value]) for key, value in self.label_dict.items()}, **{'mIoU': str(iou.mean())}}
                  for idx, iou in enumerate(ious)}

        with open(os.path.join(self.dir_path, 'files',"iou.json"),'w') as f:
            json.dump(ious, f)

    def run(self):
        self.configure_model()
        self.load_model_checkpoint()
        self.process()
        self.get_ious()

if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument("--experiment_dir", help="Define experiment directory.")
    parser.add_argument("--datadir", help="Set data dir.")
    parser.add_argument("--annotation_path", help="Set annotation path.")
    args = parser.parse_args()
    runs = filter(lambda file: file.__contains__('fold'), os.listdir(args.experiment_dir))
    for run in list(runs):
        with open(os.path.join(args.experiment_dir, run, 'files', "config.yaml"), 'r') as stream:
            config = DictConfig(eval(yaml.safe_load(stream)['cfg']['value']))
        with open(os.path.join(args.experiment_dir, run, 'files', "wandb-metadata.json"), 'r') as stream:
            wandb_config = json.load(stream)
        print("Evaluating", run)
        config['model']['name'] = wandb_config['args'][5]
        inference_module = Inference(dir_path = os.path.join(args.experiment_dir, run), image_path=args.datadir, annotation_path=args.annotation_path, config=config, num_classes=3)
        inference_module.run()