-
Notifications
You must be signed in to change notification settings - Fork 423
/
visualise_att_maps_epoch.py
92 lines (73 loc) · 3.4 KB
/
visualise_att_maps_epoch.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
from torch.utils.data import DataLoader
from dataio.loader import get_dataset, get_dataset_path
from dataio.transformation import get_dataset_transformation
from utils.util import json_file_to_pyobj
from models import get_model
import matplotlib.cm as cm
import matplotlib.pyplot as plt
import math, numpy, os
from dataio.loader.utils import write_nifti_img
from torch.nn import functional as F
def mkdirfun(directory):
if not os.path.exists(directory):
os.makedirs(directory)
def plotNNFilter(units, figure_id, interp='bilinear', colormap=cm.jet, colormap_lim=None):
plt.ion()
filters = units.shape[2]
n_columns = round(math.sqrt(filters))
n_rows = math.ceil(filters / n_columns) + 1
fig = plt.figure(figure_id, figsize=(n_rows*3,n_columns*3))
fig.clf()
for i in range(filters):
ax1 = plt.subplot(n_rows, n_columns, i+1)
plt.imshow(units[:,:,i].T, interpolation=interp, cmap=colormap)
plt.axis('on')
ax1.set_xticklabels([])
ax1.set_yticklabels([])
plt.colorbar()
if colormap_lim:
plt.clim(colormap_lim[0],colormap_lim[1])
plt.subplots_adjust(wspace=0, hspace=0)
plt.tight_layout()
# Epochs
layer_name = 'attentionblock2'
layer_save_directory = os.path.join('/vol/bitbucket/oo2113/tmp/attention_maps', layer_name); mkdirfun(layer_save_directory)
epochs = range(225, 230, 3)
att_maps = list()
int_imgs = list()
subject_id = int(2)
for epoch in epochs:
# Load options and replace the epoch attribute
json_opts = json_file_to_pyobj('/vol/biomedic2/oo2113/projects/syntAI/ukbb_pytorch/configs_final/debug_ct.json')
json_opts = json_opts._replace(model=json_opts.model._replace(which_epoch=epoch))
# Setup the NN Model
model = get_model(json_opts.model)
# Setup Dataset and Augmentation
dataset_class = get_dataset('test_sax')
dataset_path = get_dataset_path('test_sax', json_opts.data_path)
dataset_transform = get_dataset_transformation('test_sax', json_opts.augmentation)
# Setup Data Loader
dataset = dataset_class(dataset_path, transform=dataset_transform['test'])
data_loader = DataLoader(dataset=dataset, num_workers=1, batch_size=1, shuffle=False)
# test
for iteration, (input_arr, input_meta, _) in enumerate(data_loader, 1):
# look for the subject_id
if iteration == subject_id:
# load the input image into the model
model.set_input(input_arr)
inp_fmap, out_fmap = model.get_feature_maps(layer_name=layer_name, upscale=False)
# Display the input image and Down_sample the input image
orig_input_img = model.input.permute(2, 3, 4, 1, 0).cpu().numpy()
upsampled_attention = F.upsample(out_fmap[1], size=input_arr.size()[2:], mode='trilinear').data.squeeze().permute(1,2,3,0).cpu().numpy()
# Append it to the list
int_imgs.append(orig_input_img[:,:,:,0,0])
att_maps.append(upsampled_attention[:,:,:,1])
# return the model
model.destructor()
# Write the attentions to a nifti image
input_meta['name'][0] = str(subject_id) + '_img_2.nii.gz'
int_imgs = numpy.array(int_imgs).transpose([1,2,3,0])
write_nifti_img(int_imgs, input_meta, savedir=layer_save_directory)
input_meta['name'][0] = str(subject_id) + '_att_2.nii.gz'
att_maps = numpy.array(att_maps).transpose([1,2,3,0])
write_nifti_img(att_maps, input_meta, savedir=layer_save_directory)