-
Notifications
You must be signed in to change notification settings - Fork 3
/
inference.py
317 lines (244 loc) · 10.3 KB
/
inference.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
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
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
import argparse
import scipy, math
from scipy import ndimage
import cv2
import numpy as np
import sys
import json
import models
import dataloaders
from utils.helpers import colorize_mask
from utils.pallete import get_voc_pallete
from utils import metrics
import torch
import torch.nn as nn
from torchvision import transforms
import torch.nn.functional as F
from torch.utils.data import DataLoader, Dataset
import os
from tqdm import tqdm
from math import ceil
from PIL import Image
from pathlib import Path
import SimpleITK as sitk
from LungSeg import lung_segmentation
from medpy import metric
import pandas as pd
from scipy.ndimage import zoom
from skimage import measure
os.environ['CUDA_VISIBLE_DEVICES'] = '1'
def resample_ct_image(image, new_size, new_spacing, interpolator=sitk.sitkLinear, min_value=None):
resample = sitk.ResampleImageFilter()
resample.SetSize(new_size)
resample.SetInterpolator(interpolator)
resample.SetOutputSpacing(new_spacing)
if min_value:
resample.SetDefaultPixelValue(min_value)
new_image = resample.Execute(image)
return new_image
def resample_ct_image_with_shape(image, spacing, new_shape, interpolator=sitk.sitkLinear, min_value=None):
size = [image.shape[2], image.shape[1], image.shape[0]]
new_spacing = [size[0] * spacing[0] / new_shape[0],
size[1] * spacing[1] / new_shape[1],
size[2] * spacing[2] / new_shape[2]]
print (new_spacing)
image = sitk.GetImageFromArray(image)
image.SetSpacing(spacing)
return resample_ct_image(image, new_shape, new_spacing, interpolator=interpolator, min_value=min_value)
def resample_ct_image_with_spacing(image, spacing, new_spacing, interpolator=sitk.sitkLinear, min_value=None):
size = [image.shape[2], image.shape[1], image.shape[0]]
new_size = [int(size[0] * spacing[0] / new_spacing[0]),
int(size[1] * spacing[1] / new_spacing[1]),
int(size[2] * spacing[2] / new_spacing[2])]
image = sitk.GetImageFromArray(image)
image.SetSpacing(spacing)
return resample_ct_image(image, new_size, new_spacing, interpolator=interpolator, min_value=min_value)
def read_dcm_series(path):
reader = sitk.ImageSeriesReader()
reader.MetaDataDictionaryArrayUpdateOn()
dicom_names = reader.GetGDCMSeriesFileNames(path)
reader.SetFileNames(dicom_names)
image = reader.Execute()
return image
def calculate_metric_percase(pred, gt):
dice = metric.binary.dc(pred, gt)
jc = metric.binary.jc(pred, gt)
hd = metric.binary.hd95(pred, gt)
asd = metric.binary.asd(pred, gt)
# asd = 0
return dice, jc, hd, asd
def get_patch_position(shape, window, stride):
patch_position = []
def get_anchors(axis_idx):
anchors = [anchor for anchor in range(0, shape[axis_idx] - window[axis_idx], stride[axis_idx])]
cur = anchors[-1] + stride[axis_idx]
if cur + window[axis_idx] <= shape[axis_idx]:
anchors.append(cur)
else:
anchors.append(shape[axis_idx] - window[axis_idx])
return anchors
x_anchors = get_anchors(2)
y_anchors = get_anchors(1)
z_anchors = get_anchors(0)
for z in z_anchors:
for y in y_anchors:
for x in x_anchors:
patch_position.append((z, y, x))
return patch_position
def normalize(img, max, min):
img [img>max] = max
img[img<min] = min
image = (img-min)/(max-min)
return image
class testDataset(Dataset):
def __init__(self, images):
mean = [0.485, 0.456, 0.406]
std = [0.229, 0.224, 0.225]
images_path = Path(images)
self.filelist = list(images_path.glob("*.jpg"))
self.to_tensor = transforms.ToTensor()
self.normalize = transforms.Normalize(mean, std)
def __len__(self):
return len(self.filelist)
def __getitem__(self, index):
image_path = self.filelist[index]
image_id = str(image_path).split("/")[-1].split(".")[0]
image = Image.open(image_path)
image = self.normalize(self.to_tensor(image))
return image, image_id
def multi_scale_predict(model, image, scales, num_classes, flip=True):
H, W = (image.size(2), image.size(3))
upsize = (ceil(H / 8) * 8, ceil(W / 8) * 8)
upsample = nn.Upsample(size=upsize, mode='bilinear', align_corners=True)
pad_h, pad_w = upsize[0] - H, upsize[1] - W
image = F.pad(image, pad=(0, pad_w, 0, pad_h), mode='reflect')
total_predictions = np.zeros((num_classes, image.shape[2], image.shape[3]))
for scale in scales:
scaled_img = F.interpolate(image, scale_factor=scale, mode='bilinear', align_corners=False)
scaled_prediction = upsample(model(scaled_img))
if flip:
fliped_img = scaled_img.flip(-1)
fliped_predictions = upsample(model(fliped_img))
scaled_prediction = 0.5 * (fliped_predictions.flip(-1) + scaled_prediction)
total_predictions += scaled_prediction.data.cpu().numpy().squeeze(0)
total_predictions /= len(scales)
return total_predictions[:, :H, :W]
def predict(model, image, num_classes):
prediction = model(image)
pred_mask = F.softmax(prediction, dim=1)
pred_mask = pred_mask.cpu().numpy()
return pred_mask
def cal_dice(prediction, label, num=2):
total_dice = np.zeros(num-1)
for i in range(1, num):
prediction_tmp = (prediction==i)
label_tmp = (label==i)
prediction_tmp = prediction_tmp.astype(np.float)
label_tmp = label_tmp.astype(np.float)
dice = 2 * np.sum(prediction_tmp * label_tmp) / (np.sum(prediction_tmp) + np.sum(label_tmp))
total_dice[i - 1] += dice
return dice
def dice_loss(input, target):
# N = target.size(0)
smooth = 1
input_flat = input
target_flat = target
intersection = input_flat * target_flat
loss = 2 * (np.sum(intersection + smooth) / (np.sum(input_flat) + np.sum(target_flat) + smooth))
loss = 1 - loss
return loss
def main():
args = parse_arguments()
# CONFIG
assert args.config
config = json.load(open(args.config))
scales = [0.5, 0.75, 1.0, 1.25, 1.5]
num_classes = 2
# MODEL
config['model']['supervised'] = True; config['model']['semi'] = False
model = models.CCT(num_classes=num_classes,
conf=config['model'], testing=True)
checkpoint = torch.load(args.model)
model = torch.nn.DataParallel(model)
pretrained_dict_2 = {}
model_dict = model.state_dict()
for k, v in checkpoint['state_dict'].items():
if k in model_dict:
pretrained_dict_2[k] = v
else:
print (k)
model_dict.update(pretrained_dict_2)
model.load_state_dict(model_dict)
model.eval()
model.cuda()
test_path = args.data_path
test_mask_path = args.mask_path
window = [80, 112, 112]
stride = [80, 112, 112]
dice_test_all_avg = 0
pat_num = 0
pat_predict_result = []
count = 0
for data_path in os.listdir(test_path):
dice_test_avg = 0
if data_path == 'test_val':
continue
for pat in os.listdir(test_path + data_path):
print(pat)
pat_img = np.load(test_path + data_path + '/'+pat)
pat_mask = np.load(test_mask_path + data_path + '_mask'+'/' + pat[:-4]+'_mask.npy')
pat_pred = np.zeros_like(pat_img)
try:
patch_positions = get_patch_position(pat_img.shape, window, stride)
except:
continue
for position in patch_positions:
patch_img_arr = np.zeros([window[0], window[1], window[2]], dtype='float32')
patch_z_min = position[0]
patch_z_max = patch_z_min + window[0]
patch_y_min = position[1]
patch_y_max = patch_y_min + window[1]
patch_x_min = position[2]
patch_x_max = patch_x_min + window[2]
patch_img_arr = pat_img[patch_z_min:patch_z_max,
patch_y_min:patch_y_max,
patch_x_min:patch_x_max]
patch_img_arr = np.transpose(patch_img_arr, (1, 2, 0))
patch_img_arr = patch_img_arr.reshape(1, 1, patch_img_arr.shape[0], patch_img_arr.shape[1], patch_img_arr.shape[2]).astype(np.float32)
patch_img_arr = torch.from_numpy(patch_img_arr.astype(np.float32)).cuda()
# PREDICT
with torch.no_grad():
output_patch = predict(model, patch_img_arr, num_classes)
output_patch = output_patch[0]
pred_patch = np.asarray(np.argmax(output_patch, axis=0), dtype=np.uint8)
pred_patch = np.transpose(pred_patch, (2, 0, 1))
pat_pred[patch_z_min:patch_z_max, patch_y_min:patch_y_max, patch_x_min:patch_x_max] = pred_patch
dice_coef = cal_dice(pat_pred, pat_mask)
dice_test_avg += dice_coef
print ('dice_coef', dice_coef)
metrix = calculate_metric_percase(pat_pred, pat_mask[:])
print(metrix[0])
pat_predict_result.append([pat, metrix[0], metrix[1], metrix[2], metrix[3]])
dice_test_all_avg += dice_test_avg
pat_num += len(os.listdir(test_path + data_path))
dice_test_avg = dice_test_avg / len(os.listdir(test_path + data_path))
print('test dataset'+ data_path + 'avg dice', dice_test_avg)
print(count)
dice_test_all_avg = dice_test_all_avg/pat_num
print(args.model)
print ('test_all_dice', dice_test_all_avg)
#
def parse_arguments():
parser = argparse.ArgumentParser(description='PyTorch Training')
parser.add_argument('--config', default='./saved_semi_confi_1125_flip/CCT/config.json',type=str,
help='Path to the config file')
parser.add_argument( '--model', default='./saved_semi_confi_1125_flip/CCT/checkpoint_best.pth', type=str,
help='Path to the trained .pth model')
parser.add_argument('--data_path', default="/home2/pneumonia_ct/test/image/", type=str,
help='Test images for Pascal VOC')
parser.add_argument('--mask_path', default="/home2/pneumonia_ct/test/label", type=str,
help='Test images for Pascal VOC')
args = parser.parse_args()
return args
if __name__ == '__main__':
main()