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attention_map.py
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attention_map.py
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import dsmil as mil
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
import torchvision.models as models
import torchvision.transforms.functional as VF
from torchvision import transforms
import sys, argparse, os, glob
import pandas as pd
import numpy as np
from PIL import Image
from collections import OrderedDict
from skimage import exposure, io, img_as_ubyte, transform
import warnings
class BagDataset():
def __init__(self, csv_file, transform=None):
self.files_list = csv_file
self.transform = transform
def __len__(self):
return len(self.files_list)
def __getitem__(self, idx):
path = self.files_list[idx]
img = Image.open(path)
img_name = path.split(os.sep)[-1]
img_pos = np.asarray([int(img_name.split('.')[0].split('_')[0]), int(img_name.split('.')[0].split('_')[1])]) # row, col
sample = {'input': img, 'position': img_pos}
if self.transform:
sample = self.transform(sample)
return sample
class ToTensor(object):
def __call__(self, sample):
img = sample['input']
img = VF.to_tensor(img)
sample['input'] = img
return sample
class Compose(object):
def __init__(self, transforms):
self.transforms = transforms
def __call__(self, img):
for t in self.transforms:
img = t(img)
return img
def bag_dataset(args, csv_file_path):
transformed_dataset = BagDataset(csv_file=csv_file_path,
transform=Compose([
ToTensor()
]))
dataloader = DataLoader(transformed_dataset, batch_size=args.batch_size, shuffle=False, num_workers=args.num_workers, drop_last=False)
return dataloader, len(transformed_dataset)
def test(args, bags_list, milnet):
milnet.eval()
num_bags = len(bags_list)
Tensor = torch.FloatTensor
colors = [np.random.choice(range(256), size=3) for i in range(args.num_classes)]
for i in range(0, num_bags):
feats_list = []
pos_list = []
classes_list = []
csv_file_path = glob.glob(os.path.join(bags_list[i], '*.'+args.patch_ext))
dataloader, bag_size = bag_dataset(args, csv_file_path)
with torch.no_grad():
for iteration, batch in enumerate(dataloader):
patches = batch['input'].float().cuda()
patch_pos = batch['position']
feats, classes = milnet.i_classifier(patches)
feats = feats.cpu().numpy()
classes = classes.cpu().numpy()
feats_list.extend(feats)
pos_list.extend(patch_pos)
classes_list.extend(classes)
pos_arr = np.vstack(pos_list)
feats_arr = np.vstack(feats_list)
classes_arr = np.vstack(classes_list)
bag_feats = torch.from_numpy(feats_arr).cuda()
ins_classes = torch.from_numpy(classes_arr).cuda()
bag_prediction, A, _ = milnet.b_classifier(bag_feats, ins_classes)
bag_prediction = torch.sigmoid(bag_prediction).squeeze().cpu().numpy()
if len(bag_prediction.shape)==0 or len(bag_prediction.shape)==1:
bag_prediction = np.atleast_1d(bag_prediction)
benign = True
num_pos_classes = 0
for c in range(args.num_classes):
if bag_prediction[c] >= args.thres[c]:
attentions = A[:, c].cpu().numpy()
num_pos_classes += 1
if benign: # first class detected
print(bags_list[i] + ' is detected as: ' + args.class_name[c])
colored_tiles = np.matmul(attentions[:, None], colors[c][None, :])
else:
print('and ' + args.class_name[c])
colored_tiles = colored_tiles + np.matmul(attentions[:, None], colors[c][None, :])
benign = False # set flag
if benign:
print(bags_list[i] + ' is detected as: benign')
attentions = torch.sum(A, 1).cpu().numpy()
colored_tiles = np.matmul(attentions[:, None], colors[0][None, :]) * 0
colored_tiles = (colored_tiles / num_pos_classes)
colored_tiles = exposure.rescale_intensity(colored_tiles, out_range=(0, 1))
color_map = np.zeros((np.amax(pos_arr, 0)[0]+1, np.amax(pos_arr, 0)[1]+1, 3))
for k, pos in enumerate(pos_arr):
color_map[pos[0], pos[1]] = colored_tiles[k]
slide_name = bags_list[i].split(os.sep)[-1]
color_map = transform.resize(color_map, (color_map.shape[0]*32, color_map.shape[1]*32), order=0)
io.imsave(os.path.join(args.map_path, slide_name+'.png'), img_as_ubyte(color_map))
if args.export_scores:
df_scores = pd.DataFrame(A.cpu().numpy())
pos_arr_str = [str(s) for s in pos_arr]
df_scores['pos'] = pos_arr_str
df_scores.to_csv(os.path.join(args.score_path, slide_name+'.csv'), index=False)
if __name__ == '__main__':
warnings.filterwarnings('ignore')
parser = argparse.ArgumentParser(description='Testing workflow includes attention computing and color map production')
parser.add_argument('--num_classes', type=int, default=2, help='Number of output classes')
parser.add_argument('--batch_size', type=int, default=64, help='Batch size of feeding patches')
parser.add_argument('--num_workers', type=int, default=0)
parser.add_argument('--feats_size', type=int, default=512)
parser.add_argument('--thres', nargs='+', type=float, default=[0.7371, 0.2752])
parser.add_argument('--class_name', nargs='+', type=str, default=None)
parser.add_argument('--embedder_weights', type=str, default='test/weights/embedder.pth')
parser.add_argument('--aggregator_weights', type=str, default='test/weights/aggregator.pth')
parser.add_argument('--bag_path', type=str, default='test/patches')
parser.add_argument('--patch_ext', type=str, default='jpg')
parser.add_argument('--map_path', type=str, default='test/output')
parser.add_argument('--export_scores', type=int, default=0)
parser.add_argument('--score_path', type=str, default='test/score')
args = parser.parse_args()
if args.embedder_weights == 'ImageNet':
print('Use ImageNet features')
resnet = models.resnet18(pretrained=True, norm_layer=nn.BatchNorm2d)
else:
resnet = models.resnet18(pretrained=False, norm_layer=nn.InstanceNorm2d)
for param in resnet.parameters():
param.requires_grad = False
resnet.fc = nn.Identity()
i_classifier = mil.IClassifier(resnet, args.feats_size, output_class=args.num_classes).cuda()
b_classifier = mil.BClassifier(input_size=args.feats_size, output_class=args.num_classes).cuda()
milnet = mil.MILNet(i_classifier, b_classifier).cuda()
if args.embedder_weights != 'ImageNet':
state_dict_weights = torch.load(args.embedder_weights)
new_state_dict = OrderedDict()
for i in range(4):
state_dict_weights.popitem()
state_dict_init = i_classifier.state_dict()
for (k, v), (k_0, v_0) in zip(state_dict_weights.items(), state_dict_init.items()):
name = k_0
new_state_dict[name] = v
i_classifier.load_state_dict(new_state_dict, strict=False)
state_dict_weights = torch.load(args.aggregator_weights)
state_dict_weights["i_classifier.fc.weight"] = state_dict_weights["i_classifier.fc.0.weight"]
state_dict_weights["i_classifier.fc.bias"] = state_dict_weights["i_classifier.fc.0.bias"]
milnet.load_state_dict(state_dict_weights, strict=False)
bags_list = glob.glob(os.path.join(args.bag_path, '*'))
os.makedirs(args.map_path, exist_ok=True)
if args.export_scores:
os.makedirs(args.score_path, exist_ok=True)
if args.class_name == None:
args.class_name = ['class {}'.format(c) for c in range(args.num_classes)]
if len(args.thres) != args.num_classes:
raise ValueError('Number of thresholds does not match classes.')
test(args, bags_list, milnet)