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yi he
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add reqiurements
1 parent ff02485 commit 6bd88f4

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5 files changed

+148
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‎deeplesion/dataset/DeepLesionDataset_25d.py‎

Lines changed: 11 additions & 12 deletions
Original file line numberDiff line numberDiff line change
@@ -65,15 +65,12 @@ def __getitem__(self, index):
6565
# masks = masks.sum(0)>0
6666
slice_intv = ann['ann']['slice_intv']
6767
spacing = ann['ann']['spacing']
68-
# if slice_intv < self.cfg.SLICE_INTV:
69-
# slice_intv = self.cfg.SLICE_INTV
7068
label = ann['ann']['labels']
71-
# recists = ann['ann']['recists']
72-
# diameters = ann['ann']['diameters']
73-
# window = ann['ann']['window']
74-
# gender = ann['ann']['gender']
75-
# age = ann['ann']['age']
76-
# z_coord = ann['ann']['z_coord']
69+
recists = ann['ann']['recists']
70+
diameters = ann['ann']['diameters']
71+
gender = float(ann['ann']['gender'])
72+
age = float(ann['ann']['age'])
73+
z_coord = float(ann['ann']['z_coord'])
7774

7875
im, im_scale = load_prep_img(self.img_path, image_fn, spacing, slice_intv,
7976
self.cfg, num_slice=self.slice_num, is_train=self.is_train)
@@ -83,13 +80,15 @@ def __getitem__(self, index):
8380

8481
masks = masks.transpose((2, 0, 1))
8582
boxes = boxes.astype(np.float32)
86-
infos = {'masks': masks,
87-
'bboxes': boxes,
88-
'labels': label,
89-
}
9083
results = dict()#img_info=ann, ann_info=infos
9184
results['filename'] = image_fn
9285
# results['flage'] = flage
86+
infos = {'recists': recists,
87+
'diameters': diameters,
88+
'spacing': spacing,
89+
'thickness':slice_intv
90+
}
91+
# results['flage'] = flage
9392
results['img'] = im
9493
results['img_shape'] = im.shape
9594
results['ori_shape'] = im.shape#[ann['height'], ann['width']]

‎deeplesion/dataset/DeepLesionDataset_align.py‎

Lines changed: 11 additions & 7 deletions
Original file line numberDiff line numberDiff line change
@@ -66,9 +66,8 @@ def __getitem__(self, index):
6666
slice_intv = ann['ann']['slice_intv']
6767
spacing = ann['ann']['spacing']
6868
label = ann['ann']['labels']
69-
# recists = ann['ann']['recists']
70-
# diameters = ann['ann']['diameters']
71-
# window = ann['ann']['window']
69+
recists = ann['ann']['recists']
70+
diameters = ann['ann']['diameters']
7271
gender = float(ann['ann']['gender'])
7372
age = float(ann['ann']['age'])
7473
z_coord = float(ann['ann']['z_coord'])
@@ -81,10 +80,15 @@ def __getitem__(self, index):
8180

8281
masks = masks.transpose((2, 0, 1))
8382
boxes = boxes.astype(np.float32)
84-
infos = dict(gender=gender,
85-
age=age,
86-
z_coord=z_coord)
87-
results = dict(additional_fts=infos)#img_info=ann, ann_info=infos
83+
results = dict()#img_info=ann, ann_info=infos
84+
results['filename'] = image_fn
85+
# results['flage'] = flage
86+
infos = {'recists': recists,
87+
'diameters': diameters,
88+
'spacing': spacing,
89+
'thickness':slice_intv
90+
}
91+
results['infos'] = infos
8892

8993
results['filename'] = image_fn
9094
# results['flage'] = flage

‎deeplesion/dataset/DeepLesionDataset_tsm.py‎

Lines changed: 11 additions & 12 deletions
Original file line numberDiff line numberDiff line change
@@ -72,15 +72,12 @@ def __getitem__(self, index):
7272
# masks = masks.sum(0)>0
7373
slice_intv = ann['ann']['slice_intv']
7474
spacing = ann['ann']['spacing']
75-
# if slice_intv < self.cfg.SLICE_INTV:
76-
# slice_intv = self.cfg.SLICE_INTV
7775
label = ann['ann']['labels']
78-
# recists = ann['ann']['recists']
79-
# diameters = ann['ann']['diameters']
80-
# window = ann['ann']['window']
81-
# gender = ann['ann']['gender']
82-
# age = ann['ann']['age']
83-
# z_coord = ann['ann']['z_coord']
76+
recists = ann['ann']['recists']
77+
diameters = ann['ann']['diameters']
78+
gender = float(ann['ann']['gender'])
79+
age = float(ann['ann']['age'])
80+
z_coord = float(ann['ann']['z_coord'])
8481

8582
im, im_scale = load_prep_img(self.img_path, image_fn, spacing, slice_intv,
8683
self.cfg, num_slice=self.slice_num, is_train=self.is_train)
@@ -90,13 +87,15 @@ def __getitem__(self, index):
9087

9188
masks = masks.transpose((2, 0, 1))
9289
boxes = boxes.astype(np.float32)
93-
infos = {'masks': masks,
94-
'bboxes': boxes,
95-
'labels': label,
96-
}
9790
results = dict()#img_info=ann, ann_info=infos
9891
results['filename'] = image_fn
9992
# results['flage'] = flage
93+
infos = {'recists': recists,
94+
'diameters': diameters,
95+
'spacing': spacing,
96+
'thickness':slice_intv
97+
}
98+
# results['flage'] = flage
10099
results['img'] = im
101100
results['img_shape'] = im.shape
102101
results['ori_shape'] = im.shape#[ann['height'], ann['width']]

‎deeplesion/eval.py‎

Lines changed: 108 additions & 27 deletions
Original file line numberDiff line numberDiff line change
@@ -3,7 +3,7 @@
33
import numpy as np
44
from pycocotools import mask as mutils
55
import sys
6-
# sys.path.append('add your workspace here')
6+
# sys.path.append('/your alignshit path')
77
import os
88
os.environ['CUDA_VISIBLE_DEVICES']='0'
99
# from dataset import DeepLesionDataset
@@ -28,10 +28,14 @@
2828
# from deeplesion.evaluation.visualize import draw_bounding_boxes_on_image_array
2929
from deeplesion.evaluation.evaluation_metrics import sens_at_FP
3030

31+
from deeplesion.evaluation.evaluation_metrics import sens_at_FP, IOU
32+
from skimage.measure import regionprops
33+
from pycocotools.mask import decode
34+
3135
def parse_args():
3236
parser = argparse.ArgumentParser(description='eval deeplesion')
3337
# parser.add_argument('config', help='train config file path')
34-
parser.add_argument('--config', help='config path')
38+
parser.add_argument('--config', default=None, help='config path')
3539
parser.add_argument('--checkpoint', help='checkpoint path')
3640
args = parser.parse_args()
3741

@@ -67,23 +71,52 @@ def get_model(cfg_path):
6771
model.CLASSES = dataset.CLASSES
6872
return model, data_loadertest
6973

74+
# def single_gpu_test(model, data_loader):
75+
# model.eval()
76+
# results = []
77+
# # dataset = data_loader.dataset
78+
# prog_bar = mmcv.ProgressBar(len(data_loader))
79+
# with torch.no_grad():
80+
# for i, data in enumerate(data_loader):
81+
82+
# gt_boxes = data.pop('gt_bboxes')
83+
# r = model(return_loss=False, rescale=False, **data)
84+
# # inference_time.append(time.time() - start_time)
85+
# data['gt_boxes'] = gt_boxes
86+
# data['bboxes'] = r[0]
87+
# data['segs'] = r[1]
88+
# # data['img'] = data['img'].data[0][data['img'].data[0].shape[0]//2]
89+
# data.pop('img')
90+
# results.append(data)
91+
# prog_bar.update()
92+
# return results
93+
7094
def single_gpu_test(model, data_loader):
7195
model.eval()
7296
results = []
73-
# dataset = data_loader.dataset
7497
prog_bar = mmcv.ProgressBar(len(data_loader))
7598
with torch.no_grad():
99+
ann = data_loader.dataset.ann
76100
for i, data in enumerate(data_loader):
77-
78101
gt_boxes = data.pop('gt_bboxes')
79102
r = model(return_loss=False, rescale=False, **data)
80-
# inference_time.append(time.time() - start_time)
81-
data['gt_boxes'] = gt_boxes
82-
data['bboxes'] = r[0]
83-
data['segs'] = r[1]
84-
# data['img'] = data['img'].data[0][data['img'].data[0].shape[0]//2]
85-
data.pop('img')
86-
results.append(data)
103+
j=0
104+
res_dict={}
105+
res_dict['gt_boxes'] = gt_boxes.data[0][j].numpy()
106+
res_dict['bboxes'] = r[0][j]
107+
res_dict['segs'] = r[1][j]
108+
# res_dict['img'] = data['img'].data[j][0][0]
109+
dd = ann[i+j]['ann']['diameters']
110+
res_dict['diameters'] = dd
111+
res_dict['img_meta'] = data['img_meta'].data[0][j]
112+
# data.pop('img')
113+
res_dict['gt_masks'] = data['gt_masks'].data[0][j]
114+
res_dict['spacing'] = ann[i]['ann']['spacing']
115+
res_dict['recists'] = ann[i]['ann']['recists']
116+
res_dict['thickness'] = ann[i]['ann']['slice_intv']
117+
# res_dict['diameter_erro'], res_dict['pred_mask'], res_dict['gt_mask_my'], res_dict['pred_mask_index'] = mask_matrics(res_dict)
118+
results.append(res_dict)
119+
87120
prog_bar.update()
88121
return results
89122

@@ -96,35 +129,83 @@ def write_metrics(outputs, log_path, epoch):
96129
s5_gt=[]
97130
so_box=[]
98131
so_gt=[]
132+
so_seg_erro = []
99133
for d in outputs:
100-
if d['thickness'].data[0]<=2.:
134+
if d['thickness']<=2.:
101135
s1_box.append(np.vstack(d['bboxes']))
102-
s1_gt.append(d['gt_boxes'].data[0][0].numpy())
103-
elif d['thickness'].data[0]==5.:
136+
s1_gt.append(d['gt_boxes'])
137+
elif d['thickness']==5.:
104138
s5_box.append(np.vstack(d['bboxes']))
105-
s5_gt.append(d['gt_boxes'].data[0][0].numpy())
139+
s5_gt.append(d['gt_boxes'])
106140
else:
107141
so_box.append(np.vstack(d['bboxes']))
108-
so_gt.append(d['gt_boxes'].data[0][0].numpy())
109-
142+
so_gt.append(d['gt_boxes'])
143+
# so_seg_erro.extend(d['diameter_erro'])
110144
sens1 = sens_at_FP(s1_box, s1_gt, avgFP, iou_th)
111145
sens2 = sens_at_FP(s5_box, s5_gt, avgFP, iou_th)
112146
sens = sens_at_FP(s1_box+s5_box+so_box, s1_gt+s5_gt+so_gt, avgFP, iou_th)
113-
s = str(epoch)+':\t'+str(sens)+'\t'+str(sens1)+'\t'+str(sens2)+'\t align srl\n'
147+
148+
# so_seg_erro = np.array(so_seg_erro)
149+
# diameter_erro = so_seg_erro[so_seg_erro>-1].mean()
150+
s = str(epoch)+':\t'+str(sens)+'\t'+str(sens1)+'\t'+str(sens2)+f'\t \n'#diameter_erro:{diameter_erro}\n'
114151
print(s)
115-
with open(log_path,'a+') as f:
116-
f.write(s)
152+
# with open(log_path,'a+') as f:
153+
# f.write(s)
117154
return s
118155

156+
def mask_matrics(output, iou_thresh=0.5):
157+
erro = [-1] * len(output['gt_boxes'])
158+
pred_mask = []
159+
gt_mask = []
160+
pred_mask_index = []
161+
pred_mask_contours = []
162+
for i, box in enumerate(output['gt_boxes']):
163+
iou1 = IOU(box, output['bboxes'])
164+
if len(iou1)==0:
165+
continue
166+
indx = iou1.argmax()
167+
try:
168+
if iou1[indx] > iou_thresh:
169+
d_seg = decode(output['segs'][indx])
170+
pred_mask.append(output['segs'][indx])
171+
l_seg = output['gt_masks'][i].astype(np.uint8)
172+
gt_mask.append(l_seg)
173+
# _, cnts = cv2.findContours(l_seg, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
174+
pred_mask_index.append(indx)
175+
prop = regionprops(d_seg)[0]
176+
diameter = np.sqrt(hprop.major_axis_lengt**2 + prop.minor_axis_length**2)/2
177+
recists = output['recists'][i].reshape((4, 2))
178+
l = np.linalg.norm(recists[0] - recists[1])
179+
s = np.linalg.norm(recists[2] - recists[3])
180+
diameter1 = np.sqrt(l**2 + s**2)/2
181+
182+
erro[i] = (abs(prop.major_axis_lengt-max(l,s)) + abs(prop.minor_axis_length-min(l,s))) * output['spacing']
183+
# erro[i] = np.abs(diameter1 - diameter) * output['spacing']
184+
except Exception as e:
185+
print(e)
186+
print(len(output['segs']), indx, i, output['bboxes'].shape, decode(output['segs'][indx]).sum())
187+
188+
return erro, pred_mask, gt_mask, pred_mask_index
189+
190+
def main(checkpoint, cfg_path=None):
191+
if cfg_path is None:
192+
cfg_path = generate_cfg(checkpoint)
193+
print(cfg_path)
194+
model, dl = get_model(cfg_path)
195+
log_path = './log/metrix_log.txt'
196+
load_checkpoint(model, checkpoint, map_location='cpu', strict=True)
197+
outputs = single_gpu_test(model, dl)
198+
r = write_metrics(outputs, log_path, 'N/A')
199+
# save_output(outputs, os.path.basename(os.path.dirname(checkpoint))+'.pkl')
200+
with open(log_path,'a+') as f:
201+
f.write(checkpoint+':\n'+r)
202+
with open(os.path.dirname(checkpoint)+'metrics_log.txt','a+') as f:
203+
f.write(r)
204+
print(r)
205+
119206
if __name__ =='__main__':
120207
# checkpoint_path = f'/mnt/data3/deeplesion/dl/work_dirs/densenet_3d_acs_r2/latest.pth'
121208
args = parse_args()
122209
checkpoint = args.checkpoint
123210
cfg_path = args.config#generate_cfg(checkpoint)
124-
model, dl = get_model(cfg_path)
125-
# log_path = checkpoint.replace('latest.pth', 'metrix_log.txt')
126-
log_path = '/mnt/data3/alignconv/logs/metrix_log.txt'
127-
load_checkpoint(model, checkpoint, map_location='cpu', strict=True)
128-
outputs = single_gpu_test(model, dl)
129-
r = write_metrics(outputs, log_path, 'N/A')
130-
print(r)
211+
main(checkpoint, cfg_path)

‎requirements.txt‎

Lines changed: 7 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,7 @@
1+
albumentations>=0.3.2
2+
python>=3.7
3+
mmcv
4+
torch=1.3.0
5+
torchvision
6+
matplotlib
7+
numpy

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